MicroStrategy Project Design User Guide
Below you will find brief information for Project Design. This guide will provide you with a comprehensive overview of the MicroStrategy platform and how to design, create, and modify a MicroStrategy project.
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Project Design Guide
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Document Number: 09331000
Version 10
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Document number: 09331000
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6,154,766, 6,173,310, 6,260,050, 6,263,051, 6,269,393, 6,279,033, 6,501,832, 6,567,796, 6,587,547, 6,606,596, 6,658,093, 6,658,432, 6,662,195,
6,671,715, 6,691,100, 6,694,316, 6,697,808, 6,704,723, 6,707,889, 6,741,980, 6,765,997, 6,768,788, 6,772,137, 6,788,768, 6,792,086, 6,798,867,
6,801,910, 6,820,073, 6,829,334, 6,836,537, 6,850,603, 6,859,798, 6,873,693, 6,885,734, 6,888,929, 6,895,084, 6,940,953, 6,964,012, 6,977,992,
6,996,568, 6,996,569, 7,003,512, 7,010,518, 7,016,480, 7,020,251, 7,039,165, 7,082,422, 7,113,474, 7,113,993, 7,127,403, 7,174,349, 7,181,417,
7,194,457, 7,197,461, 7,228,303, 7,260,577, 7,266,181, 7,272,212, 7,302,639, 7,324,942, 7,330,847, 7,340,040, 7,356,758, 7,356,840, 7,415,438,
7,428,302, 7,430,562, 7,440,898, 7,457,397, 7,486,780, 7,509,671, 7,516,181, 7,559,048, 7,574,376, 7,617,201, 7,725,811, 7,801,967, 7,836,178,
7,861,161, 7,861,253, 7,881,443, 7,925,616, 7,945,584, 7,970,782, 8,005,870, 8,035,382, 8,051,168, 8,051,369, 8,094,788, 8,130,918, 8,296,287,
8,321,411, 8,452,755, 8,521,733, 8,522,192, 8,577,902, 8,606,813, 8,607,138, 8,645,313, 8,761,659, 8,775,807, 8,782,083, 8,812,490, 8,832,588,
8,943,044, and 8,943,187. Other patent applications are pending.
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C
ONTENTS
Description of this guide.......................................................... xiii
About this book .............................................................................xv
How to find business scenarios and examples .......................xv
What’s new in this guide .........................................................xv
Prerequisites ..........................................................................xvi
Who should use this guide.....................................................xvi
Resources....................................................................................xvi
Documentation...................................................................... xvii
Education....................................................................................xxv
Consulting.............................................................................xxv
Technical Support................................................................ xxvi
Feedback ................................................................................... xxvi
1. BI Architecture and the MicroStrategy
Introduction.................................................................................. 1
Business intelligence architecture ................................................. 2
Source systems for data collection .......................................... 2
Extraction, transformation, and loading process...................... 4
Data warehouse for data storage and relational design .......... 4
The MicroStrategy platform ........................................................... 7
MicroStrategy metadata........................................................... 8
MicroStrategy Intelligence Server.......................................... 10
MicroStrategy Developer ....................................................... 11
MicroStrategy Web and Web Universal ................................. 12
MicroStrategy project............................................................. 13
MicroStrategy Architect.......................................................... 14
MicroStrategy Integrity Manager............................................ 14
© 2015 MicroStrategy, Inc.
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Contents Project Design Guide
MicroStrategy Object Manager .............................................. 15
The project design process.......................................................... 15
Conceptualizing your business model and the data on which to report
Introduction................................................................................ 17
Overview of a logical data model................................................. 17
Facts: Business data and measurements.................................... 20
Attributes: Context for your levels of data.................................... 21
Attribute elements: Data level values..................................... 22
Attribute relationships ............................................................ 23
Hierarchies: Data relationship organization ................................. 23
Sample data model...................................................................... 24
Building a logical data model ....................................................... 25
User requirements ................................................................. 25
Existing source systems ........................................................ 26
Converting source data to analytical data.............................. 26
Logical data modeling conventions.............................................. 31
Unique identifiers ................................................................... 31
Cardinalities and ratios .......................................................... 32
Attribute forms ....................................................................... 33
3. Warehouse Structure for Your Logical Data
Introduction................................................................................ 35
Columns: Data identifiers and values .......................................... 37
Tables: Physical groupings of related data.................................. 37
Uniquely identifying data in tables with key structures........... 37
Lookup tables: Attribute storage ............................................ 39
Relate tables: A unique case for relating attributes ............... 40
Fact tables: Fact data and levels of aggregation ................... 41
Homogeneous versus heterogeneous column naming.......... 45
Highly normalized schema: Minimal storage space............... 48
Moderately normalized schema: Balanced storage space
and query performance.......................................................... 49
Highly denormalized schema: Enhanced query
performance........................................................................... 51
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Project Design Guide Contents
Design trade-offs ......................................................................... 53
Schema type comparisons .......................................................... 55
Supporting data internationalization ............................................ 56
Internationalization through tables and columns or
databases .............................................................................. 56
Supporting various character sets within databases.............. 62
Supporting map data and Geo Location ...................................... 62
Introduction................................................................................ 67
Overview of project creation ........................................................ 68
Strategies to include supplemental data in a project ............. 70
Project connectivity components ................................................. 80
MicroStrategy metadata......................................................... 80
Metadata shell ....................................................................... 80
Project source........................................................................ 81
Database instance ................................................................. 82
Project.................................................................................... 83
Summary of project connectivity ............................................ 83
Creating the metadata repository ................................................ 84
Connecting to the metadata repository and data source ............. 84
Connecting to the metadata repository .................................. 84
Connecting to a data source.................................................. 85
Creating a production project....................................................... 85
Planning your project ............................................................. 86
Architect versus Project Creation Assistant ........................... 87
Creating a new project using the Project Creation
Assistant ................................................................................ 88
Creating a new project using Architect .................................. 94
Enabling data internationalization for a project ...................... 94
Enabling data internationalization through SQL queries........ 96
Using read only or edit mode for schema editors .................. 99
Creating facts and attributes...................................................... 101
Configuring additional schema-level settings ............................ 101
Deploying your project and creating reports .............................. 102
Introduction.............................................................................. 105
Creating and modifying projects ................................................ 106
Creating projects using Architect ......................................... 106
Modifying projects using Architect ....................................... 109
© 2015 MicroStrategy, Inc.
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Contents Project Design Guide
Defining project creation and display options ...................... 111
Using the Architect toolbar................................................... 125
Adding, removing, and administering tables.............................. 126
Displaying data sources in Architect .................................... 127
Adding tables ....................................................................... 128
Removing tables .................................................................. 130
Updating, modifying, and administering tables .................... 131
Organizing project tables: Layers ........................................ 137
Creating and modifying facts ..................................................... 138
Creating facts....................................................................... 139
Creating and modifying multiple facts .................................. 142
Creating and modifying attributes .............................................. 151
Creating attributes ............................................................... 151
Creating and modifying multiple attributes........................... 155
Defining attribute relationships .................................................. 174
Automatically defining attribute relationships....................... 178
Creating and modifying user hierarchies ................................... 181
Creating user hierarchies..................................................... 181
Introduction.............................................................................. 185
Creating facts............................................................................. 187
Simultaneously creating multiple, simple facts .................... 188
Creating and modifying simple and advanced facts ............ 190
The structure of facts ................................................................. 195
How facts are defined ............................................................... 196
Mapping physical columns to facts: Fact expressions ......... 197
Fact column names and data types: Column aliases ................ 203
Modifying the levels at which facts are reported: Level
extensions.................................................................................. 206
Defining a join on fact tables using table relations............... 208
Defining a join on fact tables using fact relations................. 212
Forcing facts to relate to attributes: Using cross product
joins ..................................................................................... 214
Lowering the level of fact data: Fact degradations .............. 215
Disallowing the reporting of a fact at a certain level............. 220
Introduction.............................................................................. 223
Overview of attributes ................................................................ 224
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Project Design Guide
© 2015 MicroStrategy, Inc.
Contents
Creating attributes ..................................................................... 226
Simultaneously creating multiple attributes.......................... 227
Adding and modifying attributes .......................................... 232
Unique sets of attribute information: Attribute elements ............ 238
Supporting data internationalization for attribute elements.. 241
Column data descriptions and identifiers: Attribute forms ......... 244
Displaying forms: Attribute form properties.......................... 246
Attribute form expressions ................................................... 248
Modifying attribute data types: Column aliases ................... 258
Attribute forms versus separate attributes ........................... 261
Attribute relationships ................................................................ 262
Viewing and editing the parents and children of attributes .. 264
Supporting many-to-many and joint child relationships ....... 266
Attributes that use the same lookup table: Attribute roles ......... 276
Specifying attribute roles ..................................................... 279
Attributes with multiple ID columns: Compound attributes ........ 285
Example: Creating compound attributes.............................. 286
Using attributes to browse and report on data........................... 288
Defining how attribute forms are displayed by default ......... 289
Introduction.............................................................................. 293
Updating your MicroStrategy project schema............................ 294
Data warehouse and project interaction: Warehouse Catalog .. 296
Before you begin using the Warehouse Catalog ................. 297
Accessing the Warehouse Catalog...................................... 297
Adding and removing tables for a project ............................ 297
Managing warehouse and project tables ............................. 299
Modifying data warehouse connection and operation
defaults ................................................................................ 304
Customizing catalog SQL statements.................................. 311
Troubleshooting table and column messages ..................... 317
Accessing multiple data sources in a project............................. 318
Connecting data sources to a project .................................. 319
Adding data into a project .................................................... 322
Improving database insert performance: parameterized
Using summary tables to store data: Aggregate tables ............. 333
When to use aggregate tables ............................................. 334
Determining the frequency of queries at a specific level...... 337
Considering any related parent-child relationships .............. 338
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Contents Project Design Guide
Compression ratio................................................................ 339
Creating aggregate tables ................................................... 340
The size of tables in a project: Logical table size................. 340
Dividing tables to increase performance: Partition mapping...... 341
Server versus application partitioning .................................. 341
Metadata partition mapping ................................................. 342
Warehouse partition mapping.............................................. 344
Metadata versus warehouse partition mapping ................... 346
9. Creating Hierarchies to Organize and
Introduction.............................................................................. 347
Creating user hierarchies........................................................... 348
Creating user hierarchies using Architect ............................ 350
Types of hierarchies .................................................................. 351
System hierarchy: Project schema definition ....................... 351
User hierarchies: Logical business relationships ................. 352
Hierarchy organization............................................................... 352
Hierarchy structure............................................................... 353
Viewing hierarchies: Hierarchy Viewer ................................ 354
Configuring hierarchy display options........................................ 354
Controlling the display of attribute elements ........................ 355
Filtering attributes in a hierarchy.......................................... 359
Entry point............................................................................ 361
Hierarchy browsing .............................................................. 362
Using the Hierarchy Viewer and Table Viewer .......................... 367
Using the Hierarchy Viewer ................................................. 368
Using the Table Viewer........................................................ 369
Introduction.............................................................................. 371
Creating transformations ........................................................... 372
Expression-based versus table-based transformations ....... 373
Building a table-based transformation ................................. 374
Building an expression-based transformation...................... 375
Transformation components ...................................................... 376
Transformation metrics and joint child attributes ....................... 378
11. Designing a Project for Financial
Introduction.............................................................................. 381
Prerequisites.............................................................................. 382
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Project Design Guide Contents
Physical warehouse requirements............................................. 383
Creating tables to provide hierarchical organization ............ 383
Storing financial data ........................................................... 390
Designing the financial reporting project.................................... 392
Creating attributes for financial line items ............................ 392
Creating schema objects for financial data .......................... 395
Creating reporting objects for financial data .............................. 396
Determining financial line items using filters ........................ 396
Returning financial data with metrics ................................... 397
Creating reports for your financial data................................ 403
What is the MicroStrategy Tutorial?........................................... 409
MicroStrategy Tutorial data model............................................. 413
Data modeling notations ...................................................... 414
Geography hierarchy ........................................................... 414
Products hierarchy ............................................................... 416
Customers hierarchy............................................................ 417
Time hierarchy ..................................................................... 420
Viewing the MicroStrategy Tutorial data model ................... 421
MicroStrategy Tutorial schema .................................................. 422
Exploring the MicroStrategy Tutorial schema ...................... 423
Table column information..................................................... 426
Logical tables............................................................................. 449
Creating logical tables ............................................................... 452
Defining logical table sizes................................................... 452
Defining the primary key for a table ..................................... 455
Creating logical views ................................................................ 456
Logical view examples............................................................... 460
Business case 1: Distinct attribute lookup table................... 460
Business case 2: Attribute form expression across
multiple tables...................................................................... 461
Business case 3: Slowly changing dimensions.................... 462
Business case 4: One-to-many transformation tables ......... 472
Business case 5: Outer joins between attribute lookup
tables ................................................................................... 473
© 2015 MicroStrategy, Inc.
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Contents
Project Design Guide
Introduction.............................................................................. 477
Mapping of external data types to MicroStrategy data types..... 477
MicroStrategy data types ........................................................... 495
Format types.............................................................................. 496
Data type and format type compatibility..................................... 497
Supporting the BLOB format type........................................ 498
Big Decimal................................................................................ 499
Using the Big Decimal data type.......................................... 499
MicroStrategy support for binary data types .............................. 502
Supporting barcode data with VarChar...................................... 503
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© 2015 MicroStrategy, Inc.
B
OOK
O
VERVIEW AND
A
DDITIONAL
R
ESOURCES
Description of this guide
The MicroStrategy Project Design Guide provides comprehensive information on planning, creating, and modifying a project in MicroStrategy and covers a wide range of project-related topics, including the following:
•
Chapter 1, BI Architecture and the MicroStrategy Platform
brief introduction to business intelligence architecture and some of the main components within the MicroStrategy platform.
•
Chapter 2, The Logical Data Model
, explores logical data modeling and
how it can help you identify the different elements within your business data and plan your project.
•
Chapter 3, Warehouse Structure for Your Logical Data Model
components of the physical warehouse schema such as columns and tables and explores how you can map components from the logical data model to components in the database to form the physical warehouse schema.
•
Chapter 4, Creating and Configuring a Project
, describes the major components involved in project creation and guides you through the process of creating a project in MicroStrategy.
© 2015 MicroStrategy, Inc.
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Book Overview and Additional Resources Project Design Guide
•
Chapter 5, Creating a Project Using Architect
process of creating a project in MicroStrategy using Architect.
•
Chapter 6, The Building Blocks of Business Data: Facts
, describes the structure of facts and explores different types of facts and how they relate to your business data. This chapter also covers all the steps necessary to create facts for your project.
•
Chapter 7, The Context of Your Business Data: Attributes
conceptual look at the structure of attributes and explores different types of attributes and how they relate to your business data. This chapter also covers all the steps necessary to create attributes for your project.
•
Chapter 9, Creating Hierarchies to Organize and Browse Attributes
, discusses the different types of hierarchies in MicroStrategy, and explains how you can create user hierarchies to help organize and enhance your project.
•
Chapter 8, Optimizing and Maintaining Your Project
you can implement to better optimize and maintain your project for both the short and long term.
•
Chapter 10, Creating Transformations to Define Time-Based and Other
, discusses the different types of transformations in
MicroStrategy and describes how you can create transformations in your project.
•
Chapter 11, Designing a Project for Financial Reporting
step-by-step example of using your own data to design a project for financial reporting and analysis, including profit and loss reporting.
The appendixes contain the following additional reference information, which you may or may not require depending on your specific needs:
•
Appendix A, MicroStrategy Tutorial
MicroStrategy Tutorial project, which includes a metadata and warehouse, and a set of demonstration applications designed to illustrate the features of the MicroStrategy platform.
•
, discusses logical tables, the different types of logical tables, and how to create logical tables and views in MicroStrategy.
•
, provides information about the different data
types in MicroStrategy.
Information on integrating MicroStrategy with your MDX Cube sources such as SAP BW, Microsoft Analysis Services, and Hyperion Essbase is provided in the MicroStrategy MDX Cube Reporting Guide.
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Project Design Guide Book Overview and Additional Resources
About this book
This book is divided into chapters that begin with a brief overview of the chapter’s content.
The following sections provide the location of examples, list prerequisites for using this book, and describe the user roles the information in this book was designed for.
The sample documents and images in this guide, as well as some example steps, were created with dates that may no longer be available in the MicroStrategy Tutorial project. If you are re-creating an example, replace the year(s) shown in this guide with the most recent year(s) available in the software.
How to find business scenarios and examples
Within this guide, many of the concepts discussed are accompanied by business scenarios or other descriptive examples. Many of the examples use the MicroStrategy Tutorial, which is MicroStrategy’s sample warehouse and project. Information about the MicroStrategy Tutorial can be found in the
MicroStrategy Basic Reporting Guide.
Detailed examples of advanced reporting functionality can be found in the
MicroStrategy Advanced Reporting Guide.
What’s new in this guide
MicroStrategy 10
MicroStrategy can automatically recognize area codes as geographical data, as described in
Integrating geographical information with your imported data, page 74
MicroStrategy Analytics Enterprise
The name of MicroStrategy Desktop has been changed to MicroStrategy
Developer.
© 2015 MicroStrategy, Inc.
About this book
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Book Overview and Additional Resources Project Design Guide
MicroStrategy 9.4
Any updates to this guide were minor and not directly related to
MicroStrategy 9.4. For a list of new features in MicroStrategy 9.4, see the
MicroStrategy Readme for that release.
Prerequisites
Before working with this document, you should be familiar with:
•
The information provided in the MicroStrategy Installation and
Configuration Guide.
•
The nature and structure of the data you want to use for your business intelligence application.
Who should use this guide
This document is designed for all users who require an understanding of how to design, create, and modify a MicroStrategy project using the
MicroStrategy platform.
In short, the following business intelligence application users should read this guide:
•
Project Designers
•
Database Administrators
Resources
This section provides details on how to access books, online help,
MicroStrategy Education and Consulting resources, and how to contact
MicroStrategy Technical Support.
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Resources © 2015 MicroStrategy, Inc.
Project Design Guide Book Overview and Additional Resources
Documentation
MicroStrategy provides both manuals and online help; these two information sources provide different types of information, as described below:
• Manuals
: MicroStrategy manuals provide:
Introductory information and concepts
Examples and images
Checklists and high-level procedures to get started
The steps to access the manuals are described in
Accessing manuals and other documentation sources, page xxiii
.
Most of these manuals are also available printed in a bound, soft cover format. To purchase printed manuals, contact your MicroStrategy
Account Executive with a purchase order number.
• Help
: MicroStrategy online help provides:
Detailed steps to perform procedures
Descriptions of each option on every software screen
Additional formats
MicroStrategy manuals are available as electronic publications, downloadable on the Apple iBooks Store or Google Play, and can be read on your iOS or Android device respectively. To download a book, search for the book’s title in the iBookstore or Google Play. To view a list of manuals that are currently available, scan the following QR codes using your device’s camera:
•
For iOS devices, scan the following QR code:
© 2015 MicroStrategy, Inc.
Resources
xvii
Book Overview and Additional Resources
•
For Android devices, scan the following QR code:
Project Design Guide
xviii
Resources
For new MicroStrategy releases, it may take several days for the latest manuals to be available on the iBookstore or Google Play.
Translations
For the most up-to-date translations of MicroStrategy documentation, refer to the MicroStrategy Knowledge Base. Due to translation time, manuals in languages other than English may contain information that is one or more releases behind. You can see the version number on the title page of each manual.
Finding information
You can search all MicroStrategy books and Help for a word or phrase, with a simple Google™ search at
http://www.google.com
. For example, type
“MicroStrategy derived metric” or “MicroStrategy logical table” into a Google search. As described above, books typically describe general concepts and examples; Help typically provides detailed steps and screen options. To limit your search to MicroStrategy books, on Google’s main page you can click
More
, then select Books.
Manuals for MicroStrategy overview and evaluation
•
Introduction to MicroStrategy: Evaluation Guide
Instructions for installing, configuring, and using the MicroStrategy
Evaluation Edition of the software. This guide includes a walkthrough of
MicroStrategy features so you can perform reporting with the
MicroStrategy Tutorial project and its sample business data.
© 2015 MicroStrategy, Inc.
Project Design Guide Book Overview and Additional Resources
•
MicroStrategy Evaluation Edition Quick Start Guide
Overview of the installation and evaluation process, and additional resources.
Resources for security
•
Usher Help
Steps to perform mobile identity validation using the Usher mobile security network to issue electronic badges for identifying users.
Manuals for query, reporting, and analysis
•
MicroStrategy Installation and Configuration Guide
Information to install and configure MicroStrategy products on
Windows, UNIX, Linux, and HP platforms, and basic maintenance guidelines.
•
MicroStrategy Upgrade Guide
Steps to upgrade existing MicroStrategy products.
•
MicroStrategy Project Design Guide
Information to create and modify MicroStrategy projects, and create the objects that present your organization’s data, such as facts, attributes, hierarchies, transformations, advanced schemas, and project optimization.
•
MicroStrategy Basic Reporting Guide
Steps to get started with MicroStrategy Web, and how to analyze and format data in a report. Includes the basics for creating reports, metrics, filters, and prompts.
•
MicroStrategy Advanced Reporting Guide: Enhancing Your Business
Intelligence Application
Steps to create Freeform SQL reports, Query Builder reports, complex filters and metrics, use Data Mining Services, and create custom groups, consolidations, and complex prompts.
•
Document and Dashboard Analysis Guide
Steps to execute, analyze, and format a dashboard in MicroStrategy Web.
© 2015 MicroStrategy, Inc.
Resources
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Book Overview and Additional Resources Project Design Guide
•
MicroStrategy Report Services Document Creation Guide: Creating
Boardroom Quality Documents
Steps to create Report Services documents, add objects, and format the document and its objects.
•
MicroStrategy Dashboards and Widgets Creation Guide: Creating
Interactive Dashboards for Your Data
Steps to create MicroStrategy Report Services dashboards and add interactive visualizations.
•
MicroStrategy In-memory Analytics Guide
Information to use MicroStrategy OLAP Services features, including
Intelligent Cubes, derived metrics, derived elements, dynamic aggregation, view filters, and dynamic sourcing.
•
MicroStrategy Office User Guide
Instructions to use MicroStrategy Office to work with MicroStrategy reports and documents in Microsoft® Excel, PowerPoint, and Word, to analyze, format, and distribute business data.
•
MicroStrategy Mobile Analysis Guide: Analyzing Data with
MicroStrategy Mobile
Steps to use MicroStrategy Mobile to view and analyze data, and perform other business tasks with MicroStrategy reports and documents on a mobile device.
•
MicroStrategy Mobile Design and Administration Guide: A Platform for
Mobile Intelligence
Information and instructions to install and configure MicroStrategy
Mobile, as well as steps for a designer working in MicroStrategy
Developer or MicroStrategy Web to create effective reports and documents for use with MicroStrategy Mobile.
•
MicroStrategy System Administration Guide: Tuning, Monitoring, and
Troubleshooting Your MicroStrategy Business Intelligence System
Steps to implement, deploy, maintain, tune, and troubleshoot a
MicroStrategy business intelligence system.
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•
MicroStrategy Supplemental Reference for System Administration:
VLDB Properties, Internationalization, User Privileges, and other
Supplemental Information for Administrators
Steps for administrative tasks such as configuring VLDB properties and defining data and metadata internationalization, and reference material for other administrative tasks.
•
MicroStrategy Functions Reference
Function syntax and formula components; instructions to use functions in metrics, filters, attribute forms; examples of functions in business scenarios.
•
MicroStrategy MDX Cube Reporting Guide
Information to integrate MicroStrategy with MDX cube sources. You can integrate data from MDX cube sources into your MicroStrategy projects and applications.
•
MicroStrategy Operations Manager Guide
Instructions for managing, monitoring, and setting alerts for all of your
MicroStrategy systems from one console. This guide also includes instructions for setting up and using Enterprise Manager to analyze your
MicroStrategy system usage.
Manual for the Human Resources Analytics Module
•
Human Resources Analytics Module Reference
Software Development Kits
•
MicroStrategy Developer Library (MSDL)
Information to understand the MicroStrategy SDK, including details about architecture, object models, customization scenarios, code samples, and so on.
•
MicroStrategy Web SDK
The Web SDK is available in the MicroStrategy Developer Library, which is part of the MicroStrategy SDK.
Resources
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Documentation for MicroStrategy Portlets
•
Enterprise Portal Integration Help
Information to help you implement and deploy MicroStrategy BI within your enterprise portal, including instructions for installing and configuring out-of-the-box MicroStrategy Portlets for several major enterprise portal servers.
This resource is available from
http://www.microstrategy.com/ producthelp.
Documentation for MicroStrategy GIS Connectors
•
GIS Integration Help
Information to help you integrate MicroStrategy with Geospatial
Information Systems (GIS), including specific examples for integrating with various third-party mapping services.
This resource is available from
http://www.microstrategy.com/ producthelp.
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Resources
Help
Each MicroStrategy product includes an integrated help system to complement the various interfaces of the product as well as the tasks that can be accomplished using the product.
Some of the MicroStrategy help systems require a web browser to be viewed.
For supported web browsers, see the MicroStrategy Readme.
MicroStrategy provides several ways to access help:
•
Help button: Use the Help button or ? (question mark) icon on most software windows to see help for that window.
•
Help menu: From the Help menu or link at the top of any screen, select
MicroStrategy Help to see the table of contents, the Search field, and the index for the help system.
•
F1 key: Press F1 to see context-sensitive help that describes each option in the software window you are currently viewing.
For MicroStrategy Web, MicroStrategy Web Administrator, and
MicroStrategy Mobile Server, pressing the F1 key opens the
© 2015 MicroStrategy, Inc.
Project Design Guide Book Overview and Additional Resources context-sensitive help for the web browser you are using to access these MicroStrategy interfaces. Use the Help menu or ? (question mark) icon to access help for these MicroStrategy interfaces.
Accessing manuals and other documentation sources
The manuals are available from
http://www.microstrategy.com/ producthelp
, as well as from your MicroStrategy disk or the machine where
MicroStrategy was installed.
Adobe Reader is required to view these manuals. If you do not have
Adobe Reader installed on your computer, you can download it from
http://get.adobe.com/reader/
.
The best place for all users to begin is with the MicroStrategy Basic
Reporting Guide.
To access the installed manuals and other documentation sources, see the following procedures:
•
To access documentation resources from any location, page xxiii
•
To access documentation resources on Windows, page xxiii
•
To access documentation resources on UNIX and Linux, page xxiv
To access documentation resources from any location
1
Visit
http://www.microstrategy.com/producthelp
.
To access documentation resources on Windows
1
From the Windows Start menu, choose Programs (or All Programs),
MicroStrategy Documentation
, then Product Manuals. A page opens in your browser showing a list of available manuals in PDF format and other documentation sources.
2
Click the link for the desired manual or other documentation source.
If bookmarks are not visible on the left side of a product manual, from the View menu click Bookmarks and Page. This step varies slightly depending on your version of Adobe Reader.
Resources
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Type bold
italic
To access documentation resources on UNIX and Linux
1
Within your UNIX or Linux machine, navigate to the directory where you installed MicroStrategy. The default location is /opt/MicroStrategy, or $HOME/MicroStrategy/install if you do not have write access to
/opt/MicroStrategy.
2
From the MicroStrategy installation directory, open the Help folder.
3
Open the Product_Manuals.htm file in a web browser. A page opens in your browser showing a list of available manuals in PDF format and other documentation sources.
4
Click the link for the desired manual or other documentation source.
If bookmarks are not visible on the left side of a product manual, from the View menu click Bookmarks and Page. This step varies slightly depending on your version of Adobe Reader.
Documentation standards
MicroStrategy online help and PDF manuals (available both online and in printed format) use standards to help you identify certain types of content.
The following table lists these standards.
These standards may differ depending on the language of this manual; some languages have rules that supersede the table below.
Indicates
• Button names, check boxes, options, lists, and menus that are the focus of actions or part of a list of such GUI elements and their definitions
Example: Click Select Warehouse.
• Names of other product manuals and documentation resources
• When part of a command syntax, indicates variable information to be replaced by the user
Example: Type copy c:\filename d:\foldername\filename
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Type Indicates
Courier font
• Calculations
• Code samples
• Registry keys
• Path and file names
• URLs
• Messages displayed in the screen
• Text to be entered by the user
Example: Sum(revenue)/number of months.
Example: Type cmdmgr -f scriptfile.scp and press Enter.
+ A keyboard command that calls for the use of more than one key (for example,
SHIFT+F1).
A note icon indicates helpful information for specific situations.
A warning icon alerts you to important information such as potential security risks; these should be read before continuing.
Education
MicroStrategy Education Services provides a comprehensive curriculum and highly skilled education consultants. Many customers and partners from over 800 different organizations have benefited from MicroStrategy instruction.
Courses that can help you prepare for using this manual or that address some of the information in this manual include:
•
MicroStrategy Architect: Project Design Essentials
For the most up-to-date and detailed description of education offerings and course curricula, visit
http://www.microstrategy.com/Education
.
Consulting
MicroStrategy Consulting Services provides proven methods for delivering leading-edge technology solutions. Offerings include complex security architecture designs, performance and tuning, project and testing strategies and recommendations, strategic planning, and more. For a detailed description of consulting offerings, visit
http://www.microstrategy.com/ services-support/consulting
.
Education
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Technical Support
If you have questions about a specific MicroStrategy product, you should:
1
Consult the product guides, Help, and readme files. Locations to access each are described above.
2
Consult the MicroStrategy Knowledge Base online at
https:// resource.microstrategy.com/support
.
A technical administrator in your organization may be able to help you resolve your issues immediately.
3
MicroStrategy Technical Support can be contacted by your company's
Support Liaison. Contact information and the Technical Support policy information is available at
http://www.microstrategy.com/ services-support/support/contact
.
Feedback
Please send any comments or suggestions about user documentation for
MicroStrategy products to: [email protected]
Send suggestions for product enhancements to: [email protected]
When you provide feedback to us, please include the name and version of the products you are currently using. Your feedback is important to us as we prepare for future releases.
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1
1.
BI A
RCHITECTURE AND THE
M
ICRO
S
TRATEGY
P
LATFORM
Introduction
Before planning and creating a project in MicroStrategy, it is important to understand how business intelligence systems work and, specifically, how the MicroStrategy platform interacts with your business data to provide a wide range of functionality.
Business intelligence (BI) systems
facilitate the analysis of volumes of complex data by providing the ability to view data from multiple perspectives. An optimum business intelligence application:
•
Gives users access to data at various levels of detail
•
Allows users to request information and have it delivered to them accurately and quickly
•
Provides a foundation for the proactive delivery of information to system subscribers
This chapter introduces you to the basic architecture of BI systems, as well as some of the components within the MicroStrategy platform that allow you to create and analyze your business intelligence.
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Business intelligence architecture
A BI architecture has the following components:
•
A source system—typically an online transaction processing (OLTP) system, but other systems or files that capture or hold data of interest are also possible
•
An extraction, transformation, and loading (ETL) process
•
A data warehouse—typically an online analytical processing (OLAP) system
•
A business intelligence platform such as MicroStrategy
The diagram above illustrates the common setup for standardizing data from source systems and transferring that data into
MicroStrategy. MicroStrategy can also access data from text files,
Excel files, SAP BI, Hyperion Essbase, Microsoft Analysis Services, and other data sources. For more information on how MicroStrategy can access your data sources, see
Data warehouse for data storage and relational design, page 4
.
Source systems for data collection
Source systems
refer to any system or file that captures or holds data of interest. A bank is an example of a business with many source systems. An average bank offers several services such as account activity updates and loan disbursement, and therefore has many source systems to support these services. For example, suppose one source system—a database file on the bank’s server—keeps track of deposits and withdrawals as they occur.
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Meanwhile, a different source system—another file on the server—keeps track of each customer’s contact information.
A source system is usually the most significant site of
online transaction processing (OLTP)
. Transactional processing involves the simple recording of transactions and other business data such as sales, inventory, e-commerce, deposits, web site usage, and order processing. This processing is relied upon daily by nearly every industry, including health care, telecommunications, manufacturing, and many others.
OLTP systems are databases or mainframes that store real-time processing data and have the following characteristics:
•
Data access is optimized for frequent reading and writing, as the system records huge volumes of data every day. An example of data that benefits from this type of optimization is the number of credit card transactions that an OLTP system might record in a single day. This is in contrast to data warehouses which are often designed for reading data for analysis with a minimum number of updates, insertions, or deletions. For more
information on data warehouse design, see
Data warehouse for data storage and relational design, page 4
.
•
Data is aligned by application, that is, by business activities and workflow.
•
Data formats are not necessarily uniform across systems.
•
Data history is limited to recent or current data.
Recall the example of a bank that relies on several source systems to store data related to the many services the bank offers. Each of these business services has a different and specific workflow.
At an automated teller machine (ATM), you can withdraw or deposit money as well as check on balances. However, to get a money order, you must enter the bank and perform the transaction with a bank teller. This is because the operational systems supporting these two services are designed to perform specific tasks, and these two services require different operational systems.
If a bank wants to see a unified view of a particular customer, such as a customer’s ATM activity, loan status, account balances, and money market account information, the customer information stored in each of these
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BI Architecture and the MicroStrategy Platform Project Design Guide different systems must be consolidated. This consolidation is achieved using the extraction, transformation, and loading (ETL) process.
The ETL process consolidates data so it can be stored in a data warehouse.
Extraction, transformation, and loading process
The
extraction, transformation, and loading (ETL)
process represents all the steps necessary to move data from different source systems to an integrated data warehouse.
The ETL process involves the following steps:
1
Data is gathered from various source systems.
2
The data is transformed and prepared to be loaded into the data warehouse. Transformation procedures can include converting data types and names, eliminating unwanted data, correcting typographical errors, filling in incomplete data, and similar processes to standardize the format and structure of data.
3
The data is loaded into the data warehouse.
This process can be explained with the example of a bank that wants to consolidate a variety of information about a particular customer, including the customer’s ATM activity, loan status, and account balances. Each of these different sets of data is likely gathered by different source systems. Since each source system can have its own naming conventions, the data that comes from one system may be inconsistent with the data that comes from another system.
In this case, the ETL process extracts the data from the different banking source systems, transforms it until it is standardized and consistent, and then loads the data into the data warehouse.
Data warehouse for data storage and relational design
A well-designed and robust
data warehouse
is the source of data for the decision support system or business intelligence system. It enables its users to leverage the competitive advantage that the business intelligence provides.
Data warehouses are usually based on relational databases or some form of
relational database management system (RDBMS)
platform. These
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Structured Query
Language (SQL)
, a language developed specifically to interact with RDBMS software. However, MicroStrategy does not require that data be stored in a relational database. You can integrate different types of data sources with
MicroStrategy such as text files, Excel files, and MDX Cubes. For more
information on accessing data stored in alternative data sources, see
Storing and analyzing data with alternative data sources, page 6
.
The source systems described above, such as OLTP systems, are generally designed and optimized for transactional processing, whereas data warehouses are usually designed and optimized for analytical processing. In combination with MicroStrategy tools and products, the data warehouse also provides the foundation for a robust
online analytical processing (OLAP)
system. Analytical processing involves activities such as choosing to see sales data by month and selecting the applicable metric to calculate sales trends, growth patterns, percent-to-total contributions, trend reporting, and profit analysis.
Most data warehouses have the following characteristics:
•
Data access is typically read-only. The most common action is the selection of data for analysis. Data is rarely inserted, updated, or deleted.
This is in contrast to most OLTP source systems which must be able to handle frequent updates as data is gathered. For more information on
Source systems for data collection, page 2
•
Data is aligned by business subjects.
•
Data formats are uniformly integrated using an ETL process (see
Extraction, transformation, and loading process, page 4
).
•
Data history extends long-term, usually two to five years.
•
A data warehouse is populated with data from the existing operational
systems using an ETL process, as explained in
Extraction, transformation, and loading process, page 4
.
The structure of data in a data warehouse and how it relates to your
MicroStrategy environment can be defined and understood through a logical data model and physical warehouse schema. Defining a project’s logical data model and physical warehouse schema are important steps in preparing your data for a MicroStrategy project.
For more information on the steps of the project design process, see
Chapter 2, The Logical Data Model
Structure for Your Logical Data Model
.
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Storing and analyzing data with alternative data sources
Along with integrating with relational databases, which are a common type of data warehouse, MicroStrategy can also integrate with a number of alternative data sources. A
data source
is any file, system, or storage location which stores data that is to be used in MicroStrategy for query, reporting, and analysis. A data warehouse can be thought of as one type of data source, and refers specifically to using a database as your data source.
The following are different data source alternatives which MicroStrategy can integrate with:
• MDX Cube sources
: In MicroStrategy you can integrate with sets of data from SAP BW, Microsoft Analysis Services, Hyperion Essbase, and IBM
Cognos TM1, which are referred to as MDX Cube sources. MicroStrategy can integrate with these data sources while simultaneously accessing a relational database effectively. For more information on connecting to and integrating MDX Cube sources in MicroStrategy, see the
MicroStrategy MDX Cube Reporting Guide.
• Text files and Excel files
: With MicroStrategy’s Freeform SQL and
Query Builder features, you can query, analyze, and report on data stored in text files and Excel files. As with MDX Cube sources described above,
MicroStrategy can report against these alternative data sources while concurrently accessing a relational database to integrate all of your data into one cohesive project. For more information on using text files and
Excel files with the Freeform SQL and Query Builder features, see the
MicroStrategy Advanced Reporting Guide.
Additionally, the Data Import feature lets you use MicroStrategy Web to import data from different data sources, such as an Excel file, a table in a database, or the results of a SQL query, with minimum project design requirements. For more information on how Data Import can be used to
integrate data from various data sources into your project, see
Strategies to include supplemental data in a project, page 70
For more information on how to use the Data Import feature, refer to the
MicroStrategy Web Help.
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The MicroStrategy platform
A business intelligence platform offers a complete set of tools for the creation, deployment, support, and maintenance of business intelligence applications. Some of the main components of the MicroStrategy platform include:
•
MicroStrategy metadata, page 8
—a repository that stores MicroStrategy
object definitions and information about the data warehouse
•
MicroStrategy Intelligence Server, page 10
optimized for enterprise querying, reporting, and OLAP analysis
•
MicroStrategy Developer, page 11
environment providing a complete range of analytical functions designed to facilitate the deployment of reports
•
MicroStrategy Web and Web Universal, page 12
—a highly interactive user environment and a low-maintenance interface for reporting and analysis
•
MicroStrategy project, page 13
—where you build and store all schema objects and information you need to create application objects such as reports in the MicroStrategy environment, which together provide a flexible reporting environment
•
MicroStrategy Architect, page 14
—a project design tool, which allows you to define all the required components of your project from a centralized interface
•
MicroStrategy Integrity Manager, page 14
document comparison tool
•
MicroStrategy Object Manager, page 15
management tool for MicroStrategy environments that allows you to copy objects within a project or across related projects
MicroStrategy has a range of products and components that you can install on different operating systems. Depending on the type of setup that you have, you can install various combinations of MicroStrategy components.
See the MicroStrategy Installation and Configuration Guide for a complete list of how these products are packaged.
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The MicroStrategy platform components work together to provide an analysis and reporting environment to your user community, as shown in the following diagram.
The sections that follow provide a brief overview of each of these components. For more detailed information about these and the other components that make up the MicroStrategy platform, refer to the
MicroStrategy Installation and Configuration Guide. To learn how to administer and tune the MicroStrategy platform, see the MicroStrategy
System Administration Guide.
MicroStrategy metadata
MicroStrategy
metadata
is a repository that stores MicroStrategy object definitions and information about your data warehouse. The information is stored in a proprietary format within a relational database. The metadata maps MicroStrategy objects—which are used to build reports and analyze data—to your data warehouse structures and data. The metadata also stores the definitions of all objects created with MicroStrategy Developer and Web such as templates, reports, metrics, facts, and so on.
In general, report creation in MicroStrategy is achieved through using various types of objects which represent your data as report building blocks.
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You can build and manipulate several fundamentally different kinds of objects in MicroStrategy; these objects, which are described below, are all created and stored in the metadata repository.
•
Configuration objects
—Objects that provide important information or governing parameters for connectivity, user privileges, and project administration. Examples include database instances, users, groups, and so on. These objects are not used directly for reporting, but are created by a project architect or administrator to configure and govern the platform.
As a general rule, configuration objects are created and maintained with the managers in MicroStrategy Developer within the Administration icon. For more information about creating and administering configuration objects, see the MicroStrategy System Administration
Guide.
•
Schema objects
—Objects that are created in the application to correspond to database objects, such as tables, views, and columns. Schema objects include facts, attributes, hierarchies, and other objects which are stored in the Schema Objects folder in MicroStrategy Developer’s folder list.
Facts, attributes, and hierarchies are three essential pieces to any business intelligence application. These schema objects are often created and managed by a MicroStrategy architect:
Facts
relate numeric data values from the data warehouse to the
MicroStrategy reporting environment. Facts are used to create metrics, which are analytical calculations that are displayed on a report. The number of units sold is one example of a fact. Facts are discussed in more detail in
Chapter 6, The Building Blocks of
Attributes
represent the business context in which fact data is relevant. In the example of regional sales in the Southeast, Southeast represents the attribute or context of the sales data. Attributes are used to define the level at which you want to view the numeric data on
a report. Attributes are discussed in more detail in
Context of Your Business Data: Attributes
.
Hierarchies
are groupings of attributes so that they can be displayed to reflect their relationships to other attributes. These groupings can help users make logical connections between attributes when reporting and analyzing data. One of the most common examples of a hierarchy is a time hierarchy which includes attributes such as Year,
Month, Quarter, and so on. Hierarchies are discussed in more detail in
Chapter 9, Creating Hierarchies to Organize and Browse
•
Application objects
—Objects used to provide analysis of and insight into relevant data. Application objects include reports, documents, filters,
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BI Architecture and the MicroStrategy Platform Project Design Guide templates, custom groups, metrics, and prompts. Application objects are created using schema objects as building blocks. All application objects can be created and maintained in MicroStrategy Developer. Reports and documents can also be created and managed in MicroStrategy Web.
Information on creating application objects is in the MicroStrategy Basic
Reporting Guide and MicroStrategy Advanced Reporting Guide.
For more information about MicroStrategy Web, see
MicroStrategy Web and Web Universal, page 12
.
The metadata enables the sharing of objects across MicroStrategy applications by providing a central repository for all object definitions.
MicroStrategy Intelligence Server evaluates the most efficient data retrieval scenario to provide excellent query performance.
MicroStrategy metadata also facilitates the retrieval of data from the data warehouse when using MicroStrategy applications. It converts user requests into SQL queries and translates the results of those SQL queries back into
MicroStrategy objects such as reports and documents which can be easily analyzed and understood.
MicroStrategy Intelligence Server
MicroStrategy Intelligence Server is an analytical server optimized for enterprise querying, reporting, and OLAP analysis. The important functions of MicroStrategy Intelligence Server are:
•
Sharing objects
•
Sharing data
•
Managing the sharing of data and objects in a controlled and secure environment
•
Protecting the information in the metadata
MicroStrategy Intelligence Server also provides a library of over 150 different sophisticated mathematical and statistical functions. You can also add and define your own functions. See the MicroStrategy Functions Reference for details about these functions.
For information on how to install and configure MicroStrategy Intelligence
Server, refer to the MicroStrategy Installation and Configuration Guide.
For a detailed description of MicroStrategy Intelligence Server functionality
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Administration Guide.
MicroStrategy Developer
MicroStrategy Developer is an advanced, Windows-based environment providing a complete range of analytical functionality designed to facilitate the deployment of reports. MicroStrategy Developer provides the project designer functionality essential to creating both schema and application objects necessary to serve the user communities of both MicroStrategy
Developer and Web.
Developer enables you to model applications using an intuitive, graphical interface. It provides a unified environment for creating and maintaining business intelligence projects. If you need to change how to view your business information or how the data is modeled, Developer provides the ability to modify one aspect of the application without affecting the others.
Developer is where you can manage application objects such as reports, filters, and metrics. However, before application objects are created, schema objects must first exist. Schema objects allow application objects to interact with the data warehouse to access the data for analysis. Facts, attributes, hierarchies, and other schema objects are the building blocks for application objects such as reports and documents. For example, facts are used to create metrics, which are in turn used to design reports. Application objects such as reports are used to analyze and provide insight into the relevant data.
One of the other functions of MicroStrategy Developer is to create projects.
Chapter 4, Creating and Configuring a Project
The following examples highlight some ways in which Developer allows you to model your business intelligence applications:
•
Every report or query can automatically benefit from the tables you include in an application. Tables in MicroStrategy are references to tables in your data warehouse, thus providing access to your data.
•
You can change the structure of a business hierarchy by re-ordering it.
This modification is necessary if you have new requirements that require you to add or remove new levels of data in a hierarchy. The change automatically takes effect in the application, without making any alterations to the database.
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After reports have been created, report designers and analysts can deploy them through different interfaces, including MicroStrategy Developer,
MicroStrategy Web, and MicroStrategy Office.
For information about the various components that comprise MicroStrategy
Developer, refer to the MicroStrategy Installation and Configuration Guide.
For more information about creating application objects such as reports in
MicroStrategy Developer, refer to the MicroStrategy Basic Reporting Guide.
For information on advanced Developer functionality, see the MicroStrategy
Advanced Reporting Guide.
MicroStrategy Web and Web Universal
MicroStrategy Web provides users with a highly interactive environment and a low-maintenance interface for reporting and analysis. Using the Web interface, users can access, analyze, and share data through any web browser on many operating systems. MicroStrategy Web provides ad-hoc querying, industry-leading analysis, quick deployment, and rapid customization potential, making it easy for users to make informed business decisions.
MicroStrategy Web Universal is a version of MicroStrategy Web that provides the added benefits of also working with:
•
Operating systems such as Oracle Solaris™, IBM AIX®, Red Hat®
Linux®, and HP-UX
•
Application servers such as Oracle WebLogic™, IBM WebSphere®, and
Apache Tomcat
•
All web servers and browsers supported by MicroStrategy Web
MicroStrategy Intelligence Server must be running for users to retrieve information from your data warehouse using
MicroStrategy Web components. For more information about deploying MicroStrategy Web, see the MicroStrategy Installation
and Configuration Guide.
Additional MicroStrategy definitions, including many project-related terms, are discussed in
Project connectivity components, page 80
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MicroStrategy project
A
project
is where you build and store all schema objects and information you need to create application objects such as reports in the MicroStrategy environment, which together provide a flexible reporting environment. A project also represents the intersection of a data source, metadata repository, and user community. In MicroStrategy Developer, projects appear one level below project sources in the Folder List.
A project:
•
Determines the set of data warehouse tables to be used, and therefore the set of data available to be analyzed.
•
Contains all schema objects used to interpret the data in those tables.
Schema objects include facts, attributes, hierarchies, and so on. Schema objects are discussed in later chapters in this guide.
•
Contains all reporting objects used to create reports and analyze the data.
Reporting objects include metrics, filters, reports, and so on. Report objects are covered in the MicroStrategy Basic Reporting Guide and the
MicroStrategy Advanced Reporting Guide.
•
Defines the security scheme for the user community that accesses these objects. Security objects include security filters, security roles, privileges, access control, and so on. Security and other project-level administrative features are discussed in the MicroStrategy System Administration
Guide.
A project can contain any number of reports in addition to a number of other objects that support simple and advanced reporting requirements.
Conceptually, a project is the environment in which all related reporting is done. A project can contain many types of objects, including application objects such as filters, prompts, metrics, and reports that you can create using schema objects such as attributes and facts.
Projects are often used to separate data from a data warehouse into smaller sections of related data that fit user requirements. For example, you may have a project source separated into four different projects with analysis areas such as human resources, sales distribution, inventory, and customer satisfaction. This allows all of your users in the human resources department to use the human resources project and they do not have to look through inventory data that they are not interested in.
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Some key concepts to understand before you begin creating a project are as follows:
•
A project is created within a specified metadata repository, determined by the project source through which you create the project.
•
The project’s warehouse location is specified by associating it with the appropriate database instance.
The procedures associated with these concepts are explained in
Creating a production project, page 85
MicroStrategy Architect
MicroStrategy includes a project design tool known as Architect.
Architect
allows you to define all the required components of your project from a centralized interface. Architect also provides a visual representation of your project as you create it, which helps to provide an intuitive workflow.
Creating and modifying a project using Architect is covered in
Creating and Configuring a Project
Chapter 5, Creating a Project Using
MicroStrategy Integrity Manager
MicroStrategy Integrity Manager is an automated comparison tool designed to streamline the testing of MicroStrategy reports and documents in projects.
This tool can determine how specific changes in a project environment, such as the regular maintenance changes to metadata objects or hardware and software upgrades, affect the reports and documents in that project.
For instance, you may want to ensure that the changes involved in moving your project from a development environment into production do not alter any of your reports. Integrity Manager can compare reports in the development and the production projects, and highlight any differences. This can assist you in tracking down discrepancies between the two projects.
You can use Integrity Manager to execute reports or documents from a single
MicroStrategy project to confirm that they remain operational after changes to the system. Integrity Manager can execute any or all reports from the project, note whether or not those reports execute, and show you the results of each report.
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Integrity Manager can also test the performance of an Intelligence Server by recording how long it takes to execute a given report or document. You can execute the reports or documents multiple times in the same test and record the time for each execution cycle, to get a better idea of the average
Intelligence Server performance time.
For reports you can test and compare the SQL, grid data, graph, Excel, or
PDF output. For documents you can test and compare the Excel or PDF output, or test whether or not the documents execute properly. If you choose not to test and compare the Excel or PDF output, no output is generated for the documents. Integrity Manager still reports whether or not the documents executed successfully and how long it took them to execute.
For steps to configure Integrity Manager and to create and execute an integrity test, see the Verifying Reports and Documents with Integrity
Manager chapter of the MicroStrategy System Administration Guide.
MicroStrategy Object Manager
MicroStrategy Object Manager can help you manage objects as they progress through your project’s life cycle. Using Object Manager, you can copy objects within a project or across projects. Object Manager can copy application, schema, and configuration objects.
In the MicroStrategy system, every object has an ID (or GUID) and a version.
When you copy objects across projects with Object Manager, if an object with the same ID as the source object exists anywhere in the destination project, a conflict occurs. Object Manager helps you to resolve the conflict. For example, you can copy over the existing object, use the newer object, and so on. For steps to copy objects and resolve conflicts, as well as background information to understand how objects are copied, see the Managing Your
Projects chapter of the MicroStrategy System Administration Guide.
The project design process
When you create a project in MicroStrategy Developer, one of the connections you create is between the project and your data warehouse. In the project, you can then create schema objects based on the columns and tables in the warehouse. The diagram below shows this high-level view of
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BI Architecture and the MicroStrategy Platform Project Design Guide data modeling, schema design and implementation, and project creation, which are each covered in the following chapters:
Notice that the project design process includes a feedback loop. Designing a project is very rarely a single, linear process. As projects are deployed and tested, new user requirements and project enhancements require modification to the initial project design. It is important to keep this in mind as you design your project and plan for the next phase of development.
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Conceptualizing your business model and the data on which to report
2
Introduction
Devising a model of your business data can help you analyze the structure of the data, how its various parts interact, and can also help you decide what you intend to learn from the data.
This chapter describes one of the major components of data modeling: the logical data model. A
logical data model
is a logical arrangement of data as experienced by the general user or business analyst. This is different from the physical data model or warehouse schema, which arranges data for efficient database use. The logical data model graphically depicts the flow and structure of data in a business environment, providing a way of organizing data so it can be analyzed from different business perspectives.
Overview of a logical data model
A logical data model is similar in concept to using a map and an itinerary when going on a trip. You need to know where you are going and how to get there. You also need a plan that is visible and laid out correctly. For example, a simple logical data model for a retail company can organize all necessary
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The Logical Data Model Project Design Guide facts by store, product, and time, which are three common business perspectives typically associated with a retail business.
Logical data models are independent of a physical data storage device. This is the key concept of the logical data model. The reason that a logical data model must be independent of technology is because technology is changing so rapidly. What occurs under the logical data model can change with need or with technology, but the blueprint remains the same, and you do not need to start over completely.
If you are familiar with multidimensional data modeling, logical data modeling is similar to multidimensional data modeling. As the
MicroStrategy platform does not require you to define dimensions explicitly, the word logical is a more accurate term than multidimensional. While a multidimensional data model must have at least one dimension, a logical data model may or may not have any explicitly defined dimensions.
The scope and complexity of a logical data model depends on the requirements of the reporting needs of the user community and the availability of source data. The more sophisticated and complex the reporting requirements and source data, the more complex the logical data model becomes.
The logical data modeling process produces a diagram similar to the one shown in the following diagram:
A logical data model represents the definition, characteristics, and relationships of data in a technical, conceptual, or business environment.
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This process can help you think about the various elements that compose your company’s business data and how those elements relate to one another.
Devising a logical data model for your business intelligence environment allows you to then consider various ways to physically store the business data in the data warehouse. This is usually one of the first steps in designing a project, as shown in the following diagram:
This chapter provides conceptual information about logical data models, the elements that exist within them, and also general instructions and guidelines for creating these models.
A logical data model is a graphic representation of the following concepts:
•
Facts: Business data and measurements, page 20
•
Attributes: Context for your levels of data, page 21
•
Hierarchies: Data relationship organization, page 23
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Facts: Business data and measurements
One of the first things you do when you create a logical data model is to determine the facts. Conceptually, you can think of facts as business measurements, data, or variables that are typically numeric and suitable for aggregation. Sales, Inventory, and Account Balance are some examples of facts you can use as business measurements.
Facts allow you to access data stored in a data warehouse and they form the basis for the majority of users’ analysis and report requirements. In
MicroStrategy, facts are schema objects that relate data values (typically numeric data) from the data warehouse to the MicroStrategy reporting environment.
Facts are the building blocks used to create business measurements or metrics from which to derive insight into your data. The rest of data modeling consists mostly of providing context for the data that facts provide access to.
In a data warehouse, facts exist as columns within the fact tables. They can come from different source systems and they can have different levels of detail. For example, you can capture sales data in one system and track it daily, while you capture stock and inventory data in another system and track it weekly.
To those familiar with SQL, facts generally represent the numeric columns in database tables on which you perform SQL aggregations, such as SUM and AVG.
For example, in the following SQL statement, the ORDER_AMT column in the warehouse may correspond to the Order Amount fact in the
MicroStrategy environment:
SELECT sum(a21.ORDER_AMT) EMP_NAME
FROM ORDER_FACT a21
JOIN LU_EMPLOYEE a22
ON (a21.EMP_ID = a22.EMP_ID)
WHERE a22.CALL_CTR_ID in (5, 9, 12)
In addition, while ORDER_AMT is the fact, sum(a21.ORDER_AMT) represents a metric. Metrics, which are business calculations often built using facts, are discussed in detail in the MicroStrategy Basic
Reporting Guide.
For a more complete discussion about facts, refer to
Blocks of Business Data: Facts
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Attributes: Context for your levels of data
After the facts are determined, the attributes must be identified. Attributes allow you to answer questions about a fact and provide a context for reporting and analyzing those facts.
For example, consider the sales figures of your company. If you were informed that your company had sales of $10,000, you can gather little useful information. To make the sales figure meaningful, you would need to know more about the source of that sales figure such as:
•
A time frame for the sales
•
Who and how many people contributed to the sales total
•
What products were sold from which departments
•
The scope of the sale, such as national, regional, local, or a single store
Attributes provide context and levels for convenient summarization and qualification of your data to help answer the type of questions listed above.
They are used to answer business questions about facts at varying levels of detail. For example, if your sales data is stored at the day level, a Month attribute allows you to see the same sales data summarized at the month level.
To those familiar with SQL, attributes generally represent the non-numeric and non-aggregatable columns in database tables. These columns are used to qualify and group fact data.
For example, in the following SQL statement, the MONTH_ID column in the warehouse maps to the Month attribute in the MicroStrategy environment:
SELECT a11.MONTH_ID MONTH_ID, max(a12.MONTH_DESC) MONTH_DESC, sum(a11.TOT_DOLLAR_SALES) DLRSALES
FROM MNTH_CATEGORY_SLS a11 join LU_MONTH a12
on (a11.MONTH_ID = a12.MONTH_ID)
WHERE a11.MONTH_ID in
(200201,200202,200203)
GROUP BY al1.MONTH_ID
Attribute forms contain additional descriptive information about a given attribute and are discussed in terms of the logical data model in
Attributes: Context for your levels of data
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For a complete discussion about attributes, refer to
Your Business Data: Attributes
Attribute elements: Data level values
Attribute elements
are the unique values or contents of an attribute. For example, 2005 and 2006 are elements of the Year attribute while New York and London are elements of the City attribute. On a report, attributes are used to build the report and the attribute elements are displayed in rows or columns on the executed report.
Attribute elements also allow you to qualify on data to retrieve specific results. For example, a Customer attribute allows you to see sales data at the customer level and you can qualify on the elements of the Customer attribute to see sales data for groups such as customers with last names beginning with the letter h.
The following diagram shows some examples of attributes and attribute elements.
By recognizing and understanding the elements of an attribute, you can better design your data model and project. Although attribute elements are not included in the logical data model, they are necessary in understanding attribute relationships.
Attribute elements are discussed in more detail in
Unique sets of attribute information: Attribute elements, page 238
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Attribute relationships
Building an effective project in MicroStrategy requires you, as the project designer, to have a solid understanding of all the attributes in the project, as well as how each of them relates to the other attributes.
Attribute relationships
, which are associations between attributes that specify how attributes are connected, are essential to the logical data model.
Without relationships, there is no interaction between data, and therefore no logical structure. The relationships give meaning to the data by providing logical associations of attributes based on business rules.
Every direct relationship between attributes has two parts—a parent and a child. A child must always have a parent and a parent can have multiple children. The parent attribute is at a higher logical level than the child is. For example, in a relationship between Year and Quarter, Year is the parent attribute and Quarter is the child.
Attributes are either related or unrelated to each other. Examples of related and unrelated attributes, along with more detailed information about attribute relationships, are discussed in
Attribute relationships, page 262
Hierarchies: Data relationship organization
Hierarchies in a logical data model are ordered groupings of attributes arranged to reflect their relationship with other attributes. Usually the best design for a hierarchy is to organize or group attributes into logical business areas. For example, you can group the attributes Year, Month, and Day to form the Time hierarchy.
In a logical data model, hierarchies contain attributes that are directly related to each other. Attributes in one hierarchy are not directly related to attributes in another hierarchy.
For example, Year and Quarter are attributes that are usually directly related to each other. One year has many quarters and both attributes are in the
Time hierarchy.
Year and Customer are attributes that are usually not in the same hierarchy and are not directly related to each other. However, if you want to create a report that shows information about customer purchases in a particular year, there must be some way to determine how these two attributes are related.
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Year and Customer are related through a fact. It is the existence of a fact that ties the Time hierarchy to the Customer hierarchy. In this case, the fact is a customer purchase.
Therefore, facts exist at the intersection of hierarchies. They are identified by multiple attributes, which represent the level at which a fact is stored. A graphical example of how facts, attributes, and hierarchies are related and
form a complete logical data model is shown in the section
below.
For a complete discussion about hierarchies, refer to
Hierarchies to Organize and Browse Attributes
Sample data model
When all of the components are placed in a single diagram—facts, attributes, relationships, and hierarchies—a logical data model begins to take shape.
The following diagram is an example of a logical data model:
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Building a logical data model
The first thing you must do before creating a logical data model is study the factors that influence your design. Some of the things to consider when creating a logical data model are:
•
•
•
Converting source data to analytical data
User requirements
The primary goal of logical data modeling is to meet the needs of your users’ reporting requirements. Developing such a model involves the following:
•
Identification of user requirements
•
Design of solutions
•
Evaluation of those solutions
Logical data modeling is a reiterative process, where additional questions and concerns arise with each draft of the logical data model.
Your user community can consist of people with vastly different requirements. For example, company executives are typically interested in overall trends and may want reports showing data aggregated across the company and over a long period of time. Lower-level managers are typically more interested in data about their particular areas of responsibility. These managers may want reports about their specific region or store over short-and long-terms.
When creating the logical data model, you must consider all the potential users and how to accommodate their varied requirements. In some cases, lack of data in the source systems can limit user requirements. Sometimes, to satisfy user requirements, you can derive additional data not found in the source systems, as explained in
Existing source systems, page 26
User requirements are an important part of the initial project design process.
However, additional user requirements can be encountered after deploying a project as users encounter areas for enhancement. In some cases, new user requirements can require you to modify the logical data model to better support the type of analysis and the retrieval of data that users demand.
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Existing source systems
Understanding what data is available is an important step in creating a logical data model. Existing data is usually abundant, consisting of a large number of facts and attributes. You must determine what facts and attributes in the existing data are necessary for supporting the decision support requirements of your user community.
While a review of your data is initially helpful in identifying components of your logical data model, you may not find all the facts and attributes to meet your needs within the data itself. The existing data should suggest a number of facts, attributes, and relationships, but a substantial portion of the work in creating a suitable logical data model involves determining what additional components are required to satisfy the needs of the user community.
For example, an insurance company’s transactional system records data by customer and city, but the business analysts want to see data for different states or regions. State and region do not appear in the existing source data and so you need to extract them from another source. Additionally, although data is stored at a daily level in the source system, users also want to see data at the monthly or yearly level. In this case, you can plan additional attributes to provide the levels at which you intend to analyze the facts in your data model.
Although some data may not exist in a source system, this does not mean that it should not be included in the logical data model. Conversely, everything you find in the source data does not necessarily need to be included in the logical data model. User requirements should drive the decision on what to include and what to exclude.
Converting source data to analytical data
If there are no existing systems and you are just beginning your data warehousing initiative, you can build the logical data model based heavily on current user requirements. However, most logical models begin with an examination of the source data once existing systems are developed and implemented. The source data usually has some sort of documented physical structure. For example, most OLTP systems have an
entity relationship diagram (ERD)
. An ERD provides a graphical representation of the physical structure of the data in the source system, which lets you easily recognize tables and columns and the data stored in those columns.
A logical data model is similar in concept to an ERD. However, in this guide the logical data model also takes into account how your data can
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Project Design Guide The Logical Data Model 2 be integrated into MicroStrategy to develop a business intelligence solution.
Whether you start from nothing or have an existing source system to use, the steps to create a logical data model are as follows:
•
Step 1: Identify the facts, page 27
•
Step 2: Identify the attributes, page 28
•
Step 3: Determine attribute relationships, page 29
•
Step 4: Define hierarchies, page 30
The details in these steps are related to using an existing source system.
Step 1: Identify the facts
Using your existing data, make a list of all data that can be represented as facts in MicroStrategy. Remember that facts can be calculated and are usually numeric and aggregatable, for example, sales and profit figures. After you have all the facts listed, determine the business level at which each fact is recorded. For example, in retail models, sales facts are often stored at the store, item, or day level, meaning that a sale takes place in a particular store, for a particular item, on a particular day. A product inventory fact, however, can be stored at the region, item, or week level. These business levels become the attributes in your logical data model (see
Step 2: Identify the attributes, page 28
).
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Step 2: Identify the attributes
Uncover attributes by considering the levels at which you would like to view the facts on your reports. Start by looking at the levels at which each fact is recorded and build from there.
For example, in the existing data there may be fact data recorded only at the day level. However, your users are interested in analyzing data at more than just at the day level. They also want to view their data at the year, month, and week levels. This information may only be apparent to you after you deploy your project and you determine that a high percentage of your users are viewing sales data at the yearly level. This analysis requires MicroStrategy to aggregate the sales data from the day level to the year level. To improve performance and meet the requirements of the majority of your users, you can include an aggregate table that stores sales data at the year level (see
Using summary tables to store data: Aggregate tables, page 333
then design a Year attribute for your project. This practice is sometimes a reaction to user requirements established after project deployment, but such considerations should be taken into account during your initial project design initiative.
Be careful not to include more facts and attributes than necessary. It is usually unnecessary to bring all data from the source system into the analytical environment. Only include facts and attributes that can serve your user community. Logical data modeling is an iterative process; if necessary, you can always add more attributes and facts later.
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Step 3: Determine attribute relationships
Once you have identified your data to be defined as attributes in
MicroStrategy, you must then determine which attributes are related to each other. For example, in a project, opportunity information is stored with an
Opportunity attribute which is directly related to the attributes Opportunity
Close Date, Opportunity Open Date, Primary Competitor, and so on. These attributes are all related to the Opportunity attribute because they all answer questions about opportunity information.
Additionally, you should determine the type of relationship. For example, in the diagram below, Year has a one-to-many relationship to Month, and
Month has a one-to-many relationship to Day. This one-to-many relationship specifies that, for every year, several months exist, and for every month, several dates exist. From the reverse perspective the same relationship specifies that, for a number of dates (in a form such as 12/01/
2005), only one month exists (in a form such as Dec 2005), and for a number of months, only one year exists.
This example may not accurately define how you store time information. Consider the Year to Month attribute relationship type of one-to-many. If you define the attribute Month as simply the month name (Dec, Jan, and so on) and not directly connected to a year (Dec
2005, Jan 2006, and so on) then the relationship would become many-to-many.
If you have documentation for the existing data, such as an ERD, it is likely that the documentation provides some additional details about the nature of the data and any inherent relationships.
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Attribute relationships are discussed in detail in
Attribute relationships, page 23
.
Step 4: Define hierarchies
Hierarchies provide a structure for your data and can help your users easily and intuitively browse for related attributes and include them in a report. In the context of a logical data model, think of hierarchies as logical arrangements of attributes into business areas. For example, you can organize all time-related attributes into the Time hierarchy. You can have a
Customer hierarchy containing all attributes related to your customers and a
Supplier hierarchy for all attributes related to supplier data.
Depending on the complexity of your data and the nature of your business, you may have very few hierarchies or you may have many. It is possible that all the data is directly related, in which case you may have one big hierarchy. Again, the requirements of your user community should help you determine what hierarchies are necessary.
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Logical data modeling conventions
There are numerous logical data modeling conventions you can use to enhance your logical data model. These include:
•
•
•
These logical modeling conventions can provide cues for system optimization opportunities, help with system maintenance, and make for a more robust logical data model. Although the user community is the ultimate beneficiary of a well-optimized and maintained system, these conventions are primarily intended for project designers, administrators, and advanced report designers.
Each convention adds more information about the data to the logical data model. This additional information can be particularly useful to a person learning about the system.
Unique identifiers
An additional modeling convention is to add unique identifiers for each attribute and fact. Unique identifiers denote the key that maps an attribute to its source data in the source system, when applicable. This information can
help define primary keys in the physical warehouse schema (see
Uniquely identifying data in tables with key structures, page 37
Remember that facts are usually identified by multiple attributes and therefore will have multiple unique identifiers. The following diagram shows a logical data model with unique identifiers added. Some attributes rely on more than one ID column to identify its elements. For example, note the
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Item attribute, which requires both the Item_ID and Class_ID columns to uniquely identify its elements.
Cardinalities and ratios
Another enhancement to the logical data model is the addition of cardinalities and ratios for each attribute.
Cardinality
is the number of unique elements for an attribute and
ratios
are the ratios between the cardinalities of related attributes.
Cardinalities help the database administrator estimate the size of the data warehouse and help project designers determine the best paths for users to navigate through the data using hierarchies in MicroStrategy, which are
Chapter 9, Creating Hierarchies to Organize and Browse
. Ratios can be particularly helpful when trying to decide where
creating aggregate tables will be most effective. This additional information can be invaluable to database administrators and project designers.
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The following diagram shows a logical data model which includes cardinalities and ratios. Note the cardinalities in the upper right corner of each attribute rectangle and the ratios next to some of the relationships between attributes. Also note that the cardinality of some attributes such as
Date of Birth are unknown; this is because this information varies and is unpredictable. For example, it is impossible to determine how many customers have different dates of birth in the warehouse.
Attribute forms
Including attribute forms in your logical data model can help you get a more complete view of all of the information that is made available in your project.
Attribute forms
contain additional descriptive information about a given attribute. For example, you create an attribute called Customer to represent customers in your system, and it is part of the Customer hierarchy. Each
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The Logical Data Model Project Design Guide element of the Customer attribute represents a different customer, and in the data, you store the following information about your customers:
•
Customer number (some numeric code used to uniquely identify customers)
•
First name
•
Last name
•
Address
•
Email address
In your logical data model, you could have included each of these pieces of information as separate attributes, each with a one-to-one relationship to the
Customer attribute. In reality, though, these attributes simply provide additional information about the Customer attribute; they do not represent different levels within the Customer hierarchy. When a one-to-one relationship exists between an attribute and one of its descriptions, you can model these additional pieces of descriptive information as attribute forms.
The following diagram shows how you add attribute forms to a logical data model:
Attribute forms are discussed in terms of their role in MicroStrategy in
Column data descriptions and identifiers: Attribute forms, page 244
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Physical Warehouse Schema
Introduction
As discussed in the previous chapter, the logical data model can help you think about the logical structure of your business data and the many relationships that exist within that information.
The
physical warehouse schema
is based on the logical data model. It is a detailed graphic representation of your business data as it is stored in the data warehouse. The physical warehouse schema organizes the logical data model in a method that makes sense from a database perspective.
In contrast, the logical data model is a logical arrangement of data from the perspective of the general user or business analyst. For more information on what a logical data model is and how to create one, see
Chapter 2, The Logical Data Model
The logical data model is only concerned with logical objects of the business model, such as Day, Item, Store, or Account. Several physical warehouse schemas can be derived from the same logical data model. The structure of the schema depends on how the data representing those logical objects are to be stored in the warehouse. For example you can store logical objects in the same table, in separate tables, duplicated across several tables, or in some other arrangement.
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Warehouse Structure for Your Logical Data Model Project Design Guide
While the logical data model tells you what facts and attributes to create, the physical warehouse schema tells you where the underlying data for those objects is stored. The physical warehouse schema describes how your data is stored in the data warehouse and how it can be retrieved for analysis.
Creating a physical warehouse schema is the next step in organizing your business data before you create a project, as shown below:
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The key components that make up the physical warehouse schema are columns and tables.
Columns and tables in the physical warehouse schema represent facts and attributes from the logical data model. The rows in a table represent attribute elements and fact data.
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Project Design Guide Warehouse Structure for Your Logical Data Model 3
Columns: Data identifiers and values
Columns
are fields in the warehouse that contain attribute and fact data. The types of columns are:
•
ID columns
contain attribute element identification codes. These codes are typically numeric because computers can process numbers much more rapidly than text. All attributes must have an ID column.
•
Description columns
contain descriptions (text or numeric) of attribute elements. Description columns are optional.
An ID column can serve a dual purpose as both an ID and description.
Date is one example of an attribute that usually does not have a description column.
The majority of attributes typically have an ID column and at least one description column. If an attribute has many attribute forms—additional descriptive information about a given attribute—they are represented by additional description columns.
•
Fact columns
contain fact data.
Tables: Physical groupings of related data
The different types of tables are
•
Lookup tables: Attribute storage, page 39
•
Relate tables: A unique case for relating attributes, page 40
•
Fact tables: Fact data and levels of aggregation, page 41
While each type of table may function differently within the data warehouse, each type of table can be assigned a primary key that uniquely identifies the elements within the given table.
Uniquely identifying data in tables with key structures
In relational databases, each table has a
primary key
that creates a unique value identifying each distinct data record or row. This applies to every type of table within the data warehouse.
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The types of keys that can be assigned to a table include:
•
Simple key
requires only one column to identify a record uniquely within a table.
•
Compound key
requires multiple columns to identify a unique record.
Which key structure you use to identify a unique attribute in a table depends on the nature of your data and business requirements. The following diagram shows how the different key structures can be used to identify a calling center.
The simple key shows how you can identify a calling center with only its
Call_Ctr_id. This means that every calling center has its own unique ID.
In the compound key, calling centers are identified by both Call_Ctr_id and Region_id. This means that two calling centers from different regions can share the same Call_Ctr_id. For example, there can be a calling center with ID 1 in region A, and another calling center with ID 1 in region B. In this case, you cannot identify a unique calling center without knowing both the
Call_Ctr_id and the Region_id.
Simple keys are generally easier to handle in the data warehouse than are compound keys because they require less storage space and they allow for simpler SQL. Compound keys tend to increase SQL query complexity, query time, and required storage space. However, compound keys have a more efficient ETL process.
Which key structure you use for a particular attribute depends entirely on the nature of the data and your system. Consider what key structures work best when creating lookup tables in the physical warehouse schema.
For information on defining the primary key for tables included in a
Defining the primary key for a table, page 455
.
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Lookup tables: Attribute storage
Lookup tables
are the physical representation of attributes. They provide the ability to aggregate data at different levels. Lookup tables store the information for an attribute in ID and description columns (see
Data identifiers and values, page 37
Depending on how you choose to organize the physical schema, a lookup table can store information for one or more related attributes. If a table only stores data about one attribute, it is said to be a
normalized
table. If a table holds data about multiple attributes, it is said to be a
denormalized
table.
The following diagram shows the different ways in which you can organize the same attribute information. Notice that the denormalized table holds the exact same data as the normalized tables. While the denormalized table consolidates information about attributes within one table, the normalized tables each contain only a subset of all of the information about the attributes.
You can use either structure for any table in the physical warehouse schema, though each structure has its advantages and disadvantages, as explained in the following sections and outlined in the table in
Schema type comparisons, page 55
.
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Attribute relationships and lookup table structure
Attribute relationships are a major factor in determining the structure of lookup tables in a physical warehouse schema. In general, the following guidelines apply:
•
One-to-one relationships usually denote the existence of an attribute form. The description column of an attribute form should simply be included as an additional column in the attribute’s lookup table.
•
Many-to-many relationships usually require the use of a relate table distinct from either attribute lookup table. A relate table should include the ID columns of the two attributes in question. For more information
on how to use relate tables, see
Relate tables: A unique case for relating attributes, page 40
Relate tables: A unique case for relating attributes
While lookup tables store information about attributes, relate tables store information about the relationship between two attributes.
Relate tables
contain the ID columns of two or more attributes, thus defining associations between them. Relate tables are often used to create relationships between attributes that have a many-to-many relationship to each other.
With attributes whose direct relationship is
one-to-many
—in which every element of a parent attribute can relate to multiple elements of a child attribute—you define parent-child relationships by placing the ID column of the parent attribute in the lookup table of the child attribute. The parent ID column in the child table is called a foreign key. This technique allows you to define relationships between attributes in the attributes’ lookup tables, creating tables that function as both lookup tables and relate tables as shown in the following diagram:
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In the case of a
many-to-many
relationship—in which multiple elements of a parent attribute can relate to multiple elements of a child attribute—you must create a separate relate table as shown in the following diagram:
Fact tables: Fact data and levels of aggregation
Fact tables
are used to store fact data. Since attributes provide context for fact values, both fact columns and attribute ID columns are included in fact tables. Facts help to link indirectly related attributes. The attribute ID columns included in a fact table represent the level at which the facts in that table are stored.
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For example, fact tables containing sales and inventory data look like the tables shown in the following diagram:
For more details on the level of aggregation of your fact data, see
Fact table levels: The context of your data, page 44
.
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Base fact columns versus derived fact columns
The types of fact columns are base fact columns and derived fact columns:
•
Base fact columns
are represented by a single column in a fact table. The following diagram shows an example of a fact table and base fact columns:
•
Derived fact columns
are created through a mathematical combination of other existing fact columns. The following diagram shows an example of a fact table and how you can create a derived fact column from base fact columns:
In the example, the derived fact Tot_Dollar_Sales is created using the
Qty_Sold, Unit_Price, and Discount fact columns. Also, the derived fact exists in several tables, including Item_Mnth_Sls and
City_Ctr_Sls.
Because facts in different fact tables are typically stored at different levels, derived fact columns can only contain fact columns from the same fact table.
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There are advantages and disadvantages to consider when deciding if you should create a derived fact column. The advantage of storing derived fact columns in the warehouse is that the calculation of data is previously performed and stored separately, which translates into simpler SQL and a speedier query at report run time. The disadvantage is that derived fact columns require more storage space and more time during the ETL process.
You can create the same type of data analysis in MicroStrategy with the use of metrics. Metrics allow you to perform calculations and aggregations on fact data from one or more fact columns. For more information on what metrics are and how to create them, see the MicroStrategy Advanced
Reporting Guide.
For more information on the different types of facts in MicroStrategy and how they are defined, see
How facts are defined, page 196
.
Fact table levels: The context of your data
Facts and fact tables have an associated level based on the attribute ID columns included in the fact table. For example, the following image shows two facts with an Item/Day/Call Center level.
The Item_id, Day_id, and Call_Ctr_id columns in the table above represent practical levels at which sales and inventory data can be analyzed on a report. The Sales and Inventory facts can be analyzed at the item, day, and call center levels because those levels exist as ID columns in the fact table.
You do not need to include more lookup column IDs than are necessary for a given fact table. For example, notice that the table above does not include the
Customer_id column; this is because analyzing inventory data at the customer level does not result in a practical business calculation. Fact tables should only include attribute ID columns that represent levels at which you intend to analyze the specific fact data.
The levels at which facts are stored become especially important when you begin to have complex queries with multiple facts in multiple tables that are
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Homogeneous versus heterogeneous column naming
Suppose the data warehouse has information from two source systems, and in one source system regions are identified by column name Region_id and in the other the column name is Reg_id, as shown in the diagram below.
These naming inconsistencies occur because source systems use different naming conventions to name the data they collect.
Though the Region_id and Reg_id columns have different names, they store the same data: information about regions. This is called
heterogeneous column naming
.
The data for the Lookup_Region table came from a different source system than the data for the Lookup_Call_Ctr and the source systems have different naming conventions. This explains why the same information about regions is represented by two columns with different names.
When you define facts and attributes in MicroStrategy Developer, consider the heterogeneous column names that may exist in your source systems. In order for reports to retrieve accurate and complete results, heterogeneous columns must be mapped to their corresponding facts and attributes.
For example, if you create a Region attribute given the tables in the example above, you must map both the Region_id and Reg_id columns to the attribute so all information about regions is calculated correctly and displayed on reports when the Region attribute is used.
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For consistency, it is a good idea for columns that contain the same data to have the same column name. This is called
homogeneous column naming
. In this case, the Region_ID column has the same name in both tables, as shown in the following diagram:
Just as it is possible for the same attribute data to exist in different lookup tables, it is also possible for the same fact data to exist in different fact tables.
A fact column may or may not have the same name in different tables, as shown below:
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Schema types: Data retrieval performance versus redundant storage
There are many ways to structure your data warehouse and no method is inherently right or wrong. How you choose to structure the warehouse depends on the nature of your data, the available storage space, and the requirements of your user community. Typically, one of the schema types, or a combination of them, is used to organize the physical schema to enhance query performance while maintaining an acceptable amount of data storage space. These schema types are:
•
Highly normalized schema: Minimal storage space
•
Moderately normalized schema: Balanced storage space and query performance
•
Highly denormalized schema: Enhanced query performance
In each of these schemas a base fact table and any number of aggregate fact
tables are used (For more information on aggregate fact tables, see
Using summary tables to store data: Aggregate tables, page 333
consist of attribute keys relevant to the level of data stored in the table. The schema examples that follow show data at the Item/Call Center/Date level.
When comparing the different schema types, you should keep in mind the following concepts:
•
Redundant data can cause a couple of drawbacks. The most obvious drawback is that redundant data requires more storage space to hold the same amount of data as a system with no redundancy.
Data redundancy also makes updating data a more intensive and difficult process because data resides in multiple places. With no data redundancy, data only has to be updated in a single place.
•
Joins are SQL operations that are required to combine two tables together in order to retrieve data. These operations are necessary, but as with any operation performed on your data warehouse, the number of joins required to build your queries affects the performance of those queries.
•
The sections below are not meant to be an exhaustive list of all possible schema types. However, the sections below are meant to give a description of the most common or general schema types that are used to develop a physical warehouse schema.
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Highly normalized schema: Minimal storage space
The following diagram is an example of a
highly normalized schema
. In highly normalized schemas, lookup tables contain unique developer-designed attribute keys, such as Call_Ctr_id, Dist_Ctr_id, and Region_id, as shown in the figure below. They also contain attribute description columns, such as Call_Ctr_desc, Dist_Ctr_desc, and
Region_desc. Also, the lookup table for an attribute contains the ID column of the parent attribute, such as Dist_Ctr_id in the
Lookup_Call_Ctr table.
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The following diagram shows what physical lookup tables look like in the warehouse:
One benefit of using a highly normalized schema is that it requires minimal storage space in the warehouse because of it uses smaller lookup tables than the other schema types.
However, there is a drawback to using only small tables in the data warehouse. When accessing higher-level lookup tables such as
Lookup_Region in the example above, numerous joins are required to retrieve information about the higher-level tables. This is because each table contains only a small amount of information about a given attribute; therefore, multiple tables must be joined until the required column is found.
Moderately normalized schema: Balanced storage space and query performance
The following diagram shows an example of a
moderately normalized schema
. This schema type has the same basic structure as the highly normalized schema. The difference here is the higher-level attribute ID
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Region_id is included in the Lookup_Call_Ctr table.
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The fact table structure within a moderately normalized schema is identical to that of the highly normalized schema. The following diagram shows what the physical lookup tables look like in the warehouse.
Using a moderately normalized schema provides a balance between the pros and cons of normalized and denormalized schema types. Because the ID columns of both the parents and grandparents of an attribute exist in multiple tables, fewer joins are required when retrieving information about an attribute.
However, since some tables contain the same ID columns (as shown above with the Region_ID column), the tables within this type of schema take up some redundant storage space in the warehouse.
Highly denormalized schema: Enhanced query performance
The following diagram is an example of a
highly denormalized schema
. A highly denormalized schema has the same basic structure as the other two schema types. With this type, not only are higher-level attribute ID columns present within all related tables, but the description columns are present as
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Warehouse Structure for Your Logical Data Model Project Design Guide well. For example, Region_desc is included in the Lookup_Call_Ctr table.
Using a highly denormalized schema further reduces the joins necessary to retrieve attribute descriptions.
For example, you can include the descriptions of Call Center, Distribution Center, and Region along with Sales Dollars in the same report while only having to join the Lookup_Call_CTR and
Fact_Sales tables. This is possible because Lookup_Call_CTR contains all information (including description data) for Call Center as well as for
Distribution Center and Region.
However, this schema type requires the largest amount of storage space within the warehouse because of its large lookup tables. High denormalized schemas also cause the highest level of data redundancy.
Star schema: Consolidating lookup tables
When using the highly denormalized schema, it is possible to eliminate most of the lookup tables and leave just a few, as shown below. Arranging the warehouse schema this way produces a
star schema
. In this type of schema,
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Recall that in a highly denormalized schema, each hierarchy (for example, geography) consists of several lookup tables. In a star schema, however, only one lookup table is used to contain all of the attribute IDs and description columns for a given hierarchy, as shown below:
As with any schema type model there are advantages and disadvantages to using a star schema. As with a highly denormalized schema type, the amount of join operations are reduced by using a star schema. A star schema can also reduce the amount of storage space necessary in a highly denormalized schema. However, star schemas can often require large lookup tables that can take a more time to search than the smaller tables that are used in the other schema types.
Design trade-offs
Constructing a logical data model and physical warehouse schema is an iterative process of compromises and trade-offs. The following diagram
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Each of these categories affects the others. If you try to satisfy every single user requirement from the simplest to the most complex, you will have to create an extensive data model and schema to support those requirements.
This results in an increased load on the warehouse, slower query performance, and greater maintenance for the database administrator. You must decide which factors are most important in your particular environment and weigh them against the other factors.
For example, if you have the storage space necessary to accommodate data in a star schema it may seem that you would never want to normalize your schema. However, SQL queries directed at a consolidated table require the use of a DISTINCT operator and these types of queries tend to be very expensive in terms of database resources and processing time. The use of a resource-intensive DISTINCT query could therefore negate any performance gain achieved by reducing the number of joins between higher-level lookup table s.
In addition to the previous points, you may need higher level lookup tables to
take advantage of aggregate tables, which are discussed in
Using summary tables to store data: Aggregate tables, page 333
.
For more comparisons between the different schema types described in this
chapter, see the following section
Schema type comparisons, page 55
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Schema type comparisons
One way to achieve a balance of the various trade-offs in your schema design is to use a variety of schema types in your physical warehouse schema. One hierarchy can be highly normalized while another can be highly denormalized. You can even use different schema types within the same hierarchy. The table below compares the different schema types.
Schema Type
Highly normalized schema
Moderately normalized schema
Highly denormalized schema
Star schema
Lookup Table
Structure
• Attribute ID
• Attribute description column
• ID column of parent
• Attribute ID
• Attribute description column
• ID column of parent
• ID column of grandparents
• Attribute ID
• Attribute description column
• ID column of parent
• description column of parent
• ID column of grandparents
• description column of grandparents
• Consolidates an entire hierarchy into a single lookup table
Advantages
Minimal storage space and minimal data redundancy which makes updating data less intensive than for the other schema types
Disadvantages
Requires numerous joins to retrieve information from higher-level lookup tables
Greatly reduces the number of joins necessary to relate an attribute to its grandparents as compared to a highly normalized schema
Requires some redundant storage
Further reduces joins necessary to retrieve attribute descriptions as compared to a moderately normalized schema
Requires the most storage space and redundant data requires a more intensive process to update
• Further reduces joins necessary to retrieve attribute descriptions as compared to a moderately normalized schema
• Requires less storage space and data redundancy than a highly denormalized schema and thus data is easier to update
Large lookup tables can negatively affect query performance when searching tables and requiring
DISTINCT operations to be performed
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Now that you have gained an understanding of data modeling and the roles of facts and attributes, you can learn about these same schema objects in terms of how they exist in the MicroStrategy environment. As facts and attributes are the cornerstones of the reports you intend to create using
MicroStrategy, it is essential to understand the structure of each of these schema objects before creating a project.
Supporting data internationalization
MicroStrategy supports the internationalization of your data into the languages required for your users. This allows data to be displayed in various languages that can reflect the user’s language preferences.
To provide data in various languages you must include the translated data in your database. The strategy you use to include translated data in your database depends on many factors. Some guidelines are provided below to help define your strategy so that internationalization can be supported and integrated easily into your MicroStrategy projects:
•
Internationalization through tables and columns or databases, page 56
•
Supporting various character sets within databases, page 62
For a complete overview of internationalization in MicroStrategy, see the
Supplemental Reference for System Administration.
Internationalization through tables and columns or databases
MicroStrategy supports data internationalization through two different techniques. You can either provide translated data through the use of extra tables and columns, or you can provide separate databases to store your translated data. These techniques are described below:
•
Using tables and columns for internationalization, page 57
•
Using separate databases for internationalization, page 60
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Using tables and columns for internationalization
You can support data internationalization in your database by using separate tables and columns to store your translated data. You can use various combinations of tables and columns to support and identify the translated data in your database.
For example, the MicroStrategy Tutorial project includes a Month of Year attribute which retrieves its primary information in English from the
LU_MONTH_OF_YEAR table shown below:
To support displaying the name of each month in multiple languages, you can include the translated names in a separate column, one for each required language, within the same table. Each column can use a suffix to identify that the column contains translated data for a certain language. The same
LU_MONTH_OF_YEAR table with translated data for the Spanish and German languages is shown below:
The data for Spanish is included in a MONTH_OF_YEAR_NAME column with the suffix _ES, and the data for German is included in a
MONTH_OF_YEAR_NAME column with the suffix _DE.
As an alternative to supplying translations by using separate columns in the same table, you can create separate tables for your translations. Each table can share the same column name for the same data in different languages. In
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Spanish and German tables:
The data for Spanish is included in a LU_MONTH_OF_YEAR table with the suffix _ES, but the MONTH_OF_YEAR column shares the same column name as in the English LU_MONTH_OF_YEAR table. The data for German uses the same technique and is stored in a LU_MONTH_OF_YEAR table with the suffix
_DE.
You can also use both techniques (separate tables and extra columns in one table) to store and identify your translated data. This can be helpful to distinguish the language used for each table and column. It can also be helpful if you have a primary language stored in one table, and you store all internationalizations in an internationalization table. For example, you can
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Project Design Guide Warehouse Structure for Your Logical Data Model 3 store the Spanish and German data in the same internationalization table, as shown below:
In this scenario, the LU_MONTH_OF_YEAR_LANG table includes all translations in all languages other than the primary language, for the
MONTH_OF_YEAR_NAME column. Each column is assigned a suffix to identify the language of the translated data.
Be aware of the following:
•
In the examples above, suffixes on tables and columns are used to identify the language of the translated data. While it is not a requirement to use suffixes for these identification purposes, it is the easiest method to define and support in MicroStrategy. Using prefixes or other naming conventions requires you to use some functions to recognize the location of the translated data.
•
If your project supports data internationalization, you cannot use logical views as lookup tables for attributes that use translated data. For information on logical views, see
.
For information on defining your project to use tables and/or columns to enable data internationalization, see
Enabling data internationalization through SQL queries, page 96
.
Supporting data internationalization
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Using separate databases for internationalization
You can support data internationalization in your database by using separate databases for each supported language. A user can then be granted access, through connection mappings, to the database that contains their preferred language.
For example, the MicroStrategy Tutorial project includes a Month of Year attribute which retrieves its primary information in English from the
LU_MONTH_OF_YEAR table shown below:
For the purposes of this example, you can assume this data is stored in a database named Tutorial (English). You also provide your projects in
Spanish and German, which means you must have a database for Spanish and a database for German. Each database contains the same table structure,
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This method of data internationalization requires that the same data is available in each internationalized database.
If your project supports data internationalization, you cannot use logical views as lookup tables for attributes that use translated data.
For information on logical views, see
For information on defining your project to use separate databases to enable
data internationalization, see
Enabling data internationalization through connection mappings, page 98
.
Supporting data internationalization
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Supporting various character sets within databases
Languages require a wide range of character sets to represent data. To support the languages you plan to use in your MicroStrategy projects, you must use databases that support the required character sets and are configured accordingly. To determine whether your database supports the character sets required for the languages you want to support, refer to your third-party database documentation. For best practices information on supporting internationalization in MicroStrategy, see the Supplemental
Reference for System Administration.
Supporting map data and Geo Location
MicroStrategy can display data as part of an interactive map, which can include important information along with additional geographical information. This geographical data can be displayed using features such as the Map visualization available with Visual Insight, or through using the Map widget. This can quickly show you analysis such as the profit of stores on the east coast.
The Map widget can also use geographical data to return the location of a
MicroStrategy Mobile user, which can be used to perform additional analysis. For example, the current location can be used to return detailed information on the company’s five closest distribution centers. This
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Project Design Guide Warehouse Structure for Your Logical Data Model 3 information can then be displayed in a map format on the device, as shown in the example below:
To utilize these geographical location features in MicroStrategy, you must have location data stored in your data source. MicroStrategy requires latitude and longitude data to recognize a geographical location.
You can use various methods to develop a list of geographical locations. The procedure below uses a third-party free utility to determine valid latitude and longitude values.
The third-party product discussed in this document is manufactured by vendors independent of MicroStrategy. MicroStrategy makes no warranty, express, implied, or otherwise, regarding this product, including its performance or reliability.
To create location data to support mapping features in MicroStrategy
To create location data
In the data source of your choosing, you must create the location data for the locations that can be recognized when using mapping features in
MicroStrategy. This location data must include both a latitude and longitude, along with any descriptive information required to be
Supporting map data and Geo Location
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Warehouse Structure for Your Logical Data Model Project Design Guide displayed for each location. This procedure uses a third-party free utility to determine valid latitude and longitude points for given addresses.
1
In a web browser, go to the following URL:
http://www.gpsvisualizer.com/geocoder/
The GPS Visualizer’s Address Locator opens.
2
In the Input field, type the address to determine the latitude and longitude for. For example, 1600 Pennsylvania Avenue Washington DC
20500.
You can return latitude and longitude for multiple addresses at the same time. This procedure provides the steps on how to return the latitude and longitude for a single address.
3
In the Source drop-down list, select Google.
4
Select the Include extra fields in output (precision, city, country, etc.) check box to include additional information about the location. This additional information is not required, but it can provide additional details that can be displayed as part of the location.
5
Click Start geocoding. When the page has refreshed, click Create a GPX
file
. A new page opens with xml script. The latitude and longitude values are embedded in the xml as “lat” and “lon,” respectively. In the example picture below, these values are highlighted.
6
Include the values in quotation marks after lat= and lon= as the latitude and longitude of the location, respectively. You must store this data as numerical values that can support the decimal precision of these values.
7
Include any additional information in your data source for each location that you store a latitude and longitude for. This can be information returned from the geographical location search as well as data pertinent to your needs. For example, if you are storing addresses for distribution centers, you can include the name of the distribution center as well as any other pertinent information.
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8
Repeat the previous steps in this procedure for all locations that you want to support when using mapping features in MicroStrategy.
If you do not have an entry for a given latitude and longitude point, no information can be returned for that geographical location.
To integrate location data with the Map widget
9
In MicroStrategy, create an attribute that can return all the location data for each entry. Separate attribute forms should be created for latitude and longitude, and should also support the numeric data of these values. The latitude and longitude attribute forms can serve as values to identify each attribute element. Attributes that use multiple attribute forms as their ID form are referred to as compound attributes. For additional details on creating an attribute, see
Chapter 7, The Context of Your Business Data:
. For information on using two attribute forms to identify each
attribute element, see
Attributes with multiple ID columns: Compound attributes, page 285
.
10
If you have additional descriptive information that you want to make available for display, create additional attribute forms for that descriptive information.
11
After creating this attribute, you can:
•
Create a Map widget to display this location data. For information on creating a Map widget, see the MicroStrategy Web Help.
•
Use Visual Insight to create a map visualization to display this location data. For information on using Visual Insight to create dashboards that contain mapping visualizations, see the
MicroStrategy Web Help.
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4
4.
C
REATING AND
C
ONFIGURING
A
P
ROJECT
Introduction
Once you create a logical model of your business data and arrange the data within the data warehouse, you are ready to create a project in
MicroStrategy.
This chapter guides you through the first few major steps involved in creating a project in MicroStrategy. For definitions and descriptions of the components within the MicroStrategy platform that allow you to create and
analyze your business intelligence applications, see
Architecture and the MicroStrategy Platform
To see a sample project, access the MicroStrategy Tutorial provided with the MicroStrategy platform. The Tutorial is a sample data warehouse and demonstration project you can use to learn about the various features of the MicroStrategy platform. It is ready to be used and requires no additional configuration tasks. For more information about the Tutorial, refer to the MicroStrategy Basic Reporting Guide.
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Creating and Configuring a Project Project Design Guide
To view the structure of the MicroStrategy Tutorial, see
Overview of project creation
The following procedure describes the main steps to create a MicroStrategy project. These steps provide you with a high-level view of the project creation process. Bear this process in mind as you proceed through the rest of this guide.
The section
Project connectivity components, page 80
defines some of the basic terminology used in project creation in MicroStrategy
Developer. It is intended to familiarize you with some of the terms discussed in this guide.
1
Creating the metadata repository
The metadata repository contains the objects and definitions associated with your project. It acts as the intermediary between your business data and your reporting environment. Therefore, the first step in the project creation process is to create a metadata repository. For detailed instructions, see
Creating the metadata repository, page 84
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2
Connecting to the metadata repository and data source
Once the metadata repository is created and populated with initialization data, you must establish connections to both the metadata repository and
data source. For detailed instructions, see
Connecting to the metadata repository and data source, page 84
3
Creating a production project, page 85
Having created a metadata repository and established the necessary connections between the different parts of your MicroStrategy environment, you are ready to create the basic definition of your project.
For detailed instructions, see
Creating a production project, page 85
.
4
Schema objects such as facts and attributes are the basic components of the logical structure of a project. The business data your user community wants to report on is represented by schema objects in MicroStrategy.
Therefore, it is necessary to set up schema objects before reports can be
created. This step is covered in
Creating facts and attributes, page 101
this chapter.
You can use Query Builder or Freeform SQL to create schema objects as you design reports. For more information for these features, see the MicroStrategy Advanced Reporting Guide.
5
Configuring additional schema-level settings
Once you create the initial schema objects, you can configure additional schema-level settings that allow you to add complexity and depth to objects in your project and to the project as a whole. For example, you can create advanced facts and attributes to retrieve specific result sets. You can also use attributes to create time-series analysis schema objects called transformations and implement various tools to optimize and maintain your project over time. For information about:
•
Creating and modifying simple and advanced facts, page 190
.
•
Advanced attribute creation, see
Adding and modifying attributes, page 232
.
•
Hierarchies and hierarchy creation, see
Hierarchies to Organize and Browse Attributes
•
Transformations and transformation creation, see
Creating Transformations to Define Time-Based and Other
Overview of project creation
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Creating and Configuring a Project Project Design Guide
•
Project optimization and maintenance, see
Chapter 8, Optimizing and Maintaining Your Project
Strategies to include supplemental data in a project
The steps listed in
above relate to the process of
creating a project which connects to a database or other data source such as a text file or Excel file.
MicroStrategy also supports strategies to include supplemental data in a
MicroStrategy project. These strategies include:
•
Connecting to data stored in SAP BW, Microsoft Analysis Services,
Hyperion Essbase, and IBM Cognos TM1 systems. When integrated with
MicroStrategy, these systems are referred to as MDX cube sources. You can connect to any of these MDX cube sources to report and analyze the data concurrently within a project that also connects to a database, or you can create a standalone connection to your MDX cube source (see the
MicroStrategy MDX Cube Reporting Guide).
•
Using MicroStrategy Web to import data from different data sources, such as a file on your computer or Dropbox, a database table, the results of a SQL query, or Facebook with minimum project design requirements.
This capability of using MicroStrategy Web to import data, referred to as
Data Import, is described in detail in
Including personalized data and developing proofs-of-concept
Including personalized data and developing proofs-of-concept
The Data Import feature lets you supplement the primary data of your project in the following ways:
For information on creating the primary data for your project, follow the steps provided in
Overview of project creation, page 68
•
Include personalized data from various data sources. Along with the primary data provided in the project, your personalized data can then be reported on using all the standard MicroStrategy reporting and analysis features. This personalized data can also be displayed with the primary data in your project using documents.
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•
Develop a reporting environment as part of a proof-of-concept. Data
Import is a powerful proof-of-concept tool due to its ability to quickly integrate data into MicroStrategy, without requiring the full data modeling requirements that are described in this guide as part of creating a project.
MicroStrategy automatically maps attributes, metrics, and other objects to the data included in a MicroStrategy project using Data Import. These objects are created as managed objects.
A managed object can be removed once it is no longer referenced by another object in the project. The removal of unused managed objects is usually performed by an administrator. For more information on removing a database instance and its related managed objects, see the
System Administration Guide.
While managed objects allow you to quickly integrate data using Data
Import, the data is not directly related to the rest of your project schema.
This means that only data integrated using a single Data Import process can be included together on a given report; no additional attributes, metrics, or other objects from the project can be included.
However, rather than allowing MicroStrategy to automatically create managed objects for the attribute data, you can map the data to existing attributes in the MicroStrategy project that are part of the relational schema.
The benefits of using project attributes to define the data integrated into
MicroStrategy using the Data Import feature is described in
Mapping data to project attributes
Dashboards provide an additional option that lets you include reports that analyze data from different data sources together inside a single dashboard.
This can be accomplished by including the reports as separate datasets of the dashboard, and then each dataset can be displayed separately on the dashboard as a report, graph report, widget, or other analysis tool.
Dashboards provide a method to include both data from the rest of your project and personalized data imported using the Data Import feature, into a single analysis view. For example, the dashboard below displays various
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Creating and Configuring a Project Project Design Guide information about a current customer’s telephone service plan and their potential to churn.
Some of the data on the document could come from the primary project, such as the churn prediction, revenue risk indicator, and peak and off-peak minute usage. Additional details such as the contract usage details and the customer demographics could come from separate data sources, each included into the project using Data Import. Because documents can present separate sets of data in a single view or location, your regular project data and personalized data can be displayed to analysts as if the data were integrated.
For important prerequisites and tuning information for the Data Import feature, refer to the System Administration Guide. For more information on how to use the Data Import feature, refer to the MicroStrategy Web Help.
Mapping data to project attributes
During the process of importing data into MicroStrategy using the Data
Import feature, you can use the automatically generated managed object attributes to identify and define the levels of your data.
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Alternatively, you can manually map the data to existing attributes in the
MicroStrategy project that are part of the relational schema. Mapping the data in this way replaces the managed objects that are used to represent the data with attributes in the MicroStrategy project. Mapping imported data to attributes that are part of a relational schema has the following benefits:
•
Report designers can integrate the logical model of the project with the imported data, thus creating a relation between the two sets of data. Data can then be joined across sources within a document. In addition, document features such as selectors and group-by, which can restrict data displayed on a document based on attributes, are also applied.
For example, a document includes a report that uses Data Import and a standard report, which both use the same Year attribute. The document also includes a selector based on the Year attribute. Since both reports map year data to the same Year attribute, the selector can restrict the data for both reports. If the Data Import report used a managed object for its year data, the selector would not be applied for that report on the document.
•
Administrators can search for dependents and manage access control lists (ACLs) for attributes that map both to the data warehouse and another data source.
•
MicroStrategy security filters can be applied to attributes in reports and documents that use imported data. For example, you can map a column, integrated into MicroStrategy through the use of the Data Import feature, to the Year attribute in your project. If a user with a security filter on Year runs a report or document that uses this import data that contains Year, the security filter on Year is applied.
Mapping data to project attributes is performed during the process to integrate data into MicroStrategy using the Data Import feature. During this process, you must select valid attribute forms for the columns of data to represent as project attributes, while meeting with the following requirements:
•
The ID form of the project attribute must be mapped to the column in the
Data Import data source that you have created to relate the two systems of data. The columns must share the same data type. Numeric data types such as Integer are commonly used to represent the ID forms for attributes.
•
Ensure that all other columns mapped to attribute forms of project attributes include data that is relevant to the attribute form. This data must be relevant in terms of the data type as well as the content. If there are any data inconsistencies between the imported column data and project attribute form, using a project attribute can cause reporting
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Creating and Configuring a Project Project Design Guide issues. For example, data from two Microsoft Excel spreadsheets is shown below.
One spreadsheet is a good match to map the attribute forms of the
Customer project attribute to the columns of the spreadsheet, because the data in the spreadsheet is consistent with the project attribute’s data in the project database. However, the other spreadsheet has data inconsistencies and therefore should use a managed object to represent the data instead of mapping the attribute forms of the Customer attribute to the data.
The process of integrating data using the Data Import feature is explained in the MicroStrategy Web Help. Refer to the Help for steps on how to complete this process, including how to map the data to project attributes.
Integrating geographical information with your imported data
During the process of importing data into MicroStrategy using the Data
Import feature, you can integrate additional geographical information with your imported data. This can improve the depth of geographical information available for your data, and can also allow for easier integration with
MicroStrategy mapping features such as the Map widget and map
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MicroStrategy is shown below.
When importing your data, the column headers and the data itself are analyzed by MicroStrategy to determine what type of geographical information, if any, is available. MicroStrategy attempts to recognize data related to the geographical categories for Area Code, County, Country, State,
City, Zip code, Location, Latitude, and Longitude.
MicroStrategy can automatically recognize geographical data that meets the following criteria:
•
If the following names are used for the column headers of your data:
Area Code
: Includes data that resembles three digit area codes.
County
: Includes data for United States counties, such as Cooke,
Tioga, and Jackson.
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Country
: Includes data such as United States, Germany, and Italy.
MicroStrategy can also recognize data that resembles various country abbreviations, codes, and synonyms.
State
: Includes data such as Virginia, California, and Oklahoma.
MicroStrategy can also recognize data that resembles various state abbreviations, codes, and synonyms.
City
: Includes data such as New York, Paris, and Tokyo.
MicroStrategy can also recognize data that resembles various city abbreviations, codes, and synonyms.
Zipcode
: Includes data resembling five digit US postal codes.
Location
: Includes locations, resembling one of the following formats:
– Address, City, State, Country
For example, 1850 Towers Crescent Plaza, Tysons Corner,
Virginia, United States
– Address, City, State
For example, 1850 Towers Crescent Plaza, Tysons Corner, Virginia
– City, State
Tysons Corner, Virginia
Latitude
: Includes data that resembles latitude values. In addition to recognizing latitude values as separate attributes, latitude values can also be automatically applied as attribute forms of any of the geographical categories listed above. This capability is described below.
Longitude
: Includes data that resembles longitude values. In addition to recognizing longitude values as separate attributes, longitude values can also be automatically applied as attribute forms of any of the geographical categories listed above. This capability is described below.
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If a column of data is recognized as related to one of these geographical categories, additional geographical data can be included as part of importing the data into MicroStrategy:
•
New attributes can be automatically created and populated with data for
Country, State, and City. These options are available if any one of these geographical categories is recognized:
If a column of data is recognized as Zipcode, attributes for City of
Zipcode, State of Zipcode, and Country of Zipcode can be automatically created as part of the importing process.
If a column of data is recognized as City, attributes for State of City and Country of City can be automatically created as part of the importing process.
If a column of data is recognized as State, an attribute for Country of
State can be automatically created as part of the importing process.
If you already have columns of data for any of these geographical categories, when importing your data you can choose to not create these attributes and instead use the attributes that are created based off of your data.
•
New attribute forms can be automatically created to define the latitude and longitude values for any Area Code, County, Country, State, City, and
Zipcode. Be aware of the following if you select to automatically create these latitude and longitude values:
The latitude and longitude values for an Area Code, County, Country,
State, City, or Zipcode are determined by the files City.csv,
AreaCode.csv, County.csv, Country.csv, State.csv, and
ZipCode.csv. These files are included as part of an Intelligence
Server installation. You can modify these tables to change the latitude and longitude values for a given location, as well as add additional locations that can then have latitude and longitude values automatically assigned when importing data into MicroStrategy. Any changes to these .csv files are reflected when a user is connected to the associated Intelligence Server to import their data into
MicroStrategy.
The State.csv file includes alternative types of regions such as provinces, districts, and so on.
By default, latitude and longitude values for counties and zip codes are only provided for locations within the United States, as defined by the County.csv and ZipCode.csv files, respectively.
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If an Area Code, County, Country, State, City, or Zipcode is recognized, the first valid latitude and longitude values that are found in the .csv tables are automatically assigned to the latitude and longitude attribute forms. This can assign incorrect latitude and longitude values in some scenarios.
For example, if the city of Springfield is recognized in your data, the first matching latitude and longitude values found in the City.csv file are for Springfield, Colorado. However there are multiple
Springfields in the United States.
To avoid automatically assigning incorrect latitude and longitude values, use the following best practices:
– You can modify the .csv tables to only include the locations relevant to your data.
– For locations within the United States, rather than including city information directly in your data, you can instead include only zip code information. Using this zip code data, MicroStrategy can accurately determine the latitude and longitude of the location, and you can also select to create City of Zipcode, State of Zipcode, and Country of Zipcode attributes.
Once you import your geographical data into MicroStrategy, you can then use the Map widget and map visualizations to display and analyze the geographical data. For steps to create and configure the Map widget, refer to the MicroStrategy Mobile Design and Administration Guide. For steps to create and use map visualizations as part of Visual Insight, refer to the
MicroStrategy Web Help.
Creating additional time-related data for your imported data
During the process of importing data into MicroStrategy using the Data
Import feature, you can allow MicroStrategy to create additional time-related data for your imported data. This can help to define your data more specifically when it comes to time information.
When importing your data, the contents of the data is analyzed to determine whether it is in a format that supports dates or times. MicroStrategy can automatically recognize date and time data that meets the following criteria:
•
The data uses a valid date or date and time format. Date data can include month, day, and year information in the format MM/DD/YYYY, where
MM is a two digit month, DD is a two digit day, and YYYY is a four digit year. Date and time data can include month, day, and year information in
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If a column of data is recognized as including date or time data, additional time data can be included as part of importing the data into MicroStrategy:
•
The following new attributes can be automatically created and populated with data if date data is recognized:
Year
: Includes data such as 2010, 2011, and 2012.
Quarter
: Includes data such as Q1 2011, Q2 2011, and Q3 2011
Quarter of Year
: Includes data such as Q1, Q2, and Q3.
Month
: Includes data such as January 2011, February 2011, and
March 2011.
Month of Year
: Includes data such as January, February, and March.
Week
: Includes data such as Week 50, 2011, Week 51, 2011, and Week
52, 2011.
Week of Year
: Includes data such as 50, 51, and 52.
Day of Month
: Includes data such as 28, 29, and 30.
Day of Week
: Includes data such as Sunday, Monday, Tuesday.
Date
: Includes data such as 1/27/2011, 1/28/2011, and 1/29/2011.
If you already have columns of data for any of these date categories, when importing your data you can choose to not create these attributes and instead use the attributes that are created based off of your data.
•
The following new attributes can be automatically created and populated with data if time data is recognized:
Hour
: Includes data such as 12, 13, and 14.
Minute
: Includes data such as 57, 58, and 59.
Second
: Includes data such as 57, 58, and 59.
If you already have columns of data for any of these time categories, when importing your data you can choose to not create these attributes and instead use the attributes that are created based off of your data.
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•
All of the attributes listed above can be automatically created and populated with data if date and time data is recognized.
Once you import your data into MicroStrategy, you can then include any of the time attributes that were automatically created during the import process on your reports, documents, and dashboards. For steps to analyze the data you have imported using Data Import, refer to the MicroStrategy Web Help.
Project connectivity components
This section defines some of the basic terminology used in project creation in
MicroStrategy Developer. It is intended to familiarize you with some of the terms discussed in this guide.
MicroStrategy metadata
All schema objects, application objects, configuration objects, and project settings are stored in the MicroStrategy metadata. Metadata is stored in a relational database with a predefined structure. The RDBMS for the metadata and warehouse do not need to be the same.
You can find the list of supported RDBMS platforms in the Readme that is installed with MicroStrategy products. To view the Readme, from the Start menu select Programs, then MicroStrategy
Documentation
, and then select Readme.
Metadata shell
Before you can populate the metadata repository with data, the necessary tables to hold the data must be present. The
metadata shell
is the set of blank tables that are created when you initially implement a MicroStrategy business intelligence environment.
You create the metadata shell with the MicroStrategy Configuration Wizard, which creates the blank tables and populates some of the tables with basic initialization data.
This first step in the project creation process is outlined in
Creating the metadata repository, page 84
.
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Project source
The
project source
is a configuration object which represents a connection to a metadata repository. In MicroStrategy Developer, the project source appears in the Folder List with an icon that varies depending on the type of connection it represents. A connection to a metadata repository is achieved in one of two ways:
• Direct
or two-tier mode (
)
: Connects to the metadata by specifying a
DSN, login, and password to a metadata repository.
It is highly recommended that you never use direct mode connection in a production environment. MicroStrategy strongly suggests you always connect to the metadata through Intelligence
Server because of the security and scalability it provides. You should not connect directly to the metadata unless you are implementing a prototype environment.
• Server
or three-tier mode(
)
: Connects to the metadata by pointing to an Intelligence Server definition, which in turn governs and validates the connection to the metadata. The project metadata is the first tier,
MicroStrategy Developer is the second tier, and Intelligence Server is the third tier. Intelligence Server manages all connections to databases, enforces security, and ensures metadata integrity. For these reasons,
Intelligence Server is a necessary part of any production project.
A four-tier connection is a Server (three-tier) connection in conjunction with MicroStrategy Web deployed on a web server.
The following diagram illustrates server connectivity between a
MicroStrategy metadata repository, Intelligence Server, and MicroStrategy
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Developer. This is the type of connection used to create a production-ready project in MicroStrategy.
After the connection to the metadata is established, every object definition you create within this project source is stored in this metadata. This includes application objects, schema objects, and configuration objects from any number of projects defined within this project source (see
MicroStrategy metadata, page 8
for definitions of these object types).
A project source connects to a single metadata repository. However, the same metadata repository can be accessed by multiple project sources. A project source may contain any number of projects.
Database instance
The
database instance
is a configuration object that represents a connection to a data source. When you define a project, you specify the data source location by creating and selecting a database instance with the appropriate connection parameters.
For information on database instances, see the MicroStrategy Installation
and Configuration Guide.
Connecting to a data source through a database instance is explained in detail in
Connecting to a data source, page 85
.
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Creating and Configuring a Project 4
A project is where you build and store all schema objects and information you need to create application objects such as reports in the MicroStrategy environment. A project also represents the intersection of a data source, metadata repository, and user community. For more information on what a project is in MicroStrategy, see
MicroStrategy project, page 13
.
Summary of project connectivity
With a firm understanding of the MicroStrategy metadata, project sources, database instances, and projects, you can begin to build an understanding of how these various pieces work together to provide an integrated business intelligence environment as shown in the following diagram.
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Creating the metadata repository
Your first step in project creation is to create a metadata repository. This repository stores all the objects necessary to support your project.
You can create an empty metadata repository in the database location of your choice using the Metadata Tables option in the Configuration Wizard.
Before proceeding to the next section, make sure your metadata repository exists in a non-Microsoft Access database. An Access database is unsuitable for a production project.
Create a metadata repository using the guidelines outlined in the
Configuring and Connecting Intelligence Server chapter of the
MicroStrategy Installation and Configuration Guide.
When you create the metadata repository, MicroStrategy creates a default configuration in the repository. The default configuration populates the tables with the basic data required for the metadata, such as the default project folder structure and basic connection information.
These tables are populated with your project information during the project creation step in the Project Creation Assistant, outlined in
Creating a production project, page 85
For instructions on creating a metadata repository in a database, see the MicroStrategy Installation and Configuration Guide.
Connecting to the metadata repository and data source
Once you have created a metadata repository, your next step is to connect
MicroStrategy Developer to the metadata repository and to your data source.
Connecting to the metadata repository
You connect to the metadata repository in MicroStrategy Developer or Web through a project source. Recall that a project source is a pointer to a metadata repository. It connects either through a DSN that points to the
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Server which, in turn, points to the metadata repository location.
To configure Intelligence Server and establish a server connection between the metadata, Intelligence Server, and MicroStrategy Developer, follow the steps in the MicroStrategy Installation and Configuration Guide.
Connecting to a data source
A data source contains the business data from which you intend to gain analytical insight. Once you connect to the metadata repository through
Intelligence Server, your next step is to create a connection to the data source to which your project can connect. You connect to the data source by creating a database instance in MicroStrategy Developer.
Create a database instance using the procedures outlined in the Configuring
and Connecting Intelligence Server chapter of the MicroStrategy
Installation and Configuration Guide.
MicroStrategy includes an extension to Intelligence Server referred to as
MultiSource Option. With this feature, you can connect a project to multiple data sources. This allows you to integrate all your information from various databases and other relational data sources into a single MicroStrategy project for reporting and analysis purpose. For information on connecting a
project to multiple data sources, see
Accessing multiple data sources in a project, page 318
.
Note the following:
•
If you do not have the MultiSource Option, your projects can only connect to a single database instance.
•
MicroStrategy also allows you to connect to your SAP BW,
Microsoft Analysis Services, and Hyperion Essbase data sources.
For information about connecting to these MDX cube sources, see the MicroStrategy MDX Cube Reporting Guide.
Creating a production project
You can now begin building the MicroStrategy project that connects to the metadata repository and data source. Project creation involves creating a basic project definition and creating your project’s first schema objects.
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This section guides you through the creation of a production-ready
MicroStrategy project with the Project Creation Assistant or Architect.
It is assumed you intend to implement Intelligence Server in your business intelligence environment as the means of connecting to your project as opposed to using a direct, (two-tier) setup. To create a direct connection, see the Installation and Configuration Guide.
Creating a project using the Project Creation Assistant or Architect in
MicroStrategy Developer provides advanced functionalities and a greater complexity to your project that can support a production environment. It allows you to create a new project and add the following objects to it or to an existing project:
•
Tables
•
Facts
•
Attributes
With the Project Creation Assistant or Architect, you create and configure a project and some of the essential schema objects that reside within it. The intended audience for these tools includes experienced project creators who have planned all their facts, attributes, and data relationships. This information is covered elsewhere in this guide. For a listing of information
covered in specific chapters, see
One of the many benefits of the Project Creation Assistant and Architect is their ability to create multiple schema objects at one time. Since you can efficiently add multiple tables and develop numerous attributes and facts, it is especially useful for large projects which contain many tables and schema objects. With the Project Creation Assistant or Architect, you can also create attributes with many-to-many relationships.
Planning your project
Before using the Project Creation Assistant or Architect, you should plan your project and consider the following:
•
The logical data model you intend to use for this project; logical data
Chapter 2, The Logical Data Model
.
•
The tables to use in the project; physical warehouse schema models are covered in
Chapter 3, Warehouse Structure for Your Logical Data
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•
The facts to include in the project and the data types used to identify them; facts are covered in
Chapter 6, The Building Blocks of Business
.
•
The attributes to create in the project and the data types used to identify them, including:
The description column name for each attribute.
Any other attribute forms for each attribute.
The child attributes for each attribute.
Attributes are covered in
Chapter 7, The Context of Your Business Data:
•
Whether to use Project Creation Assistant or Graphical Architect. Both
options for creating a project are compared in
below.
Architect versus Project Creation Assistant
MicroStrategy has two tools that each provide unique ways to create a project: the Project Creation Assistant and Architect.
The Project Creation Assistant provides a linear, wizard-style workflow to create a project. This helps to guide you through the various steps required in project creation in a logical order. In general, the Project Creation Assistant provides separate steps to add and define the objects required for a project.
While you can add and define multiple tables, facts, and attributes for your project, each is provided as a separate step in the step-by-step creation of your project.
Also, Project Creation Assistant is intended to be used for the initial creation of your project, and thus cannot be used to modify an existing project. Once a project has been created, you must use Architect or the individual schema editors and wizards to modify the project.
Architect provides a centralized interface which allows you to define all the required components of your project and perform the various tasks to create a project.
When creating projects using Architect, including at least some tables in your project is a first step to include some information in your project. With
Architect, once tables are added to your project you have much more
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Creating and Configuring a Project Project Design Guide flexibility in the order in which you create your project. While you are creating your project in Architect, you can easily switch between adding tables, creating facts, creating attributes, defining relationships between attributes, creating user hierarchies, and any other required project creation tasks. While performing these tasks, Architect provides a visual representation of your project, which helps to provide an intuitive workflow.
Both tools provide options to automatically create facts and attributes based on the columns and data available in your data source. Automatic fact and attribute creation can save a substantial amount of time in the creation of a project.
As summarized above, Project Creation Assistant and Architect provide two unique workflows to support the creation of a project. To help determine which tool you should use to create your project, the table below compares these two tools:
Project Creation
Task or Feature
Project Creation Assistant Architect
The workflow of creating a project
Can be used to modify a project
Can create user hierarchies
Automatic creation of facts and attributes
Provides a linear, wizard-style workflow to add tables, attributes, and facts to a project
Provides a flexible workflow to add objects to a project in an order that suits your requirements from within a centralized interface
Can only be used for the initial creation of a project
Can be used to both create a project and make modifications at any time in a project’s life cycle
User hierarchies cannot be created User hierarchies can be created
Can automatically create facts and attributes based on rules
Can automatically create facts and attributes based on rules
Can create initial project object
The initial project object can be created
You must first create the initial project object using the Project Creation
Assistant before using Architect
Creating a new project using the Project Creation Assistant
Once you have planned your project and completed the prerequisites, you can use the Project Creation Assistant to build the project and populate the metadata based on the data structures present in your data warehouse.
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The steps of the Project Creation Assistant are:
1
Initialize/create the project.
Initializing the project means giving the project a name and selecting the metadata repository in which to create the project—that is, the project source. It also includes defining which languages are available in the project for the internationalization of metadata object information.
After you specify these settings, the shell of a project is created in the metadata. This configures the folder structure and default connectivity settings. Be aware that this process can take some time to complete.
2
Select tables from the Warehouse Catalog.
In this step, you use the Warehouse Catalog to specify which data warehouse tables to include in your project.
3
Create facts.
4
Create attributes.
You should complete all the steps in the Project Creation Assistant at the same time. While you can save an incomplete project definition, you cannot finish creating it later with the Project Creation Assistant.
Instead, you must complete it using the appropriate interface, such as the Warehouse Catalog, Fact Creation Assistant, or Attribute Creation
Assistant.
To create a new project using the Project Creation Assistant
1
Log in to a project source in MicroStrategy Developer.
To create a project source which connects to your data through
Intelligence Server, see the Configuring and Connecting Intelligence
Server chapter of the MicroStrategy Installation and Configuration
Guide.
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2
From the Schema menu, select Create New Project. The Project
Creation Assistant opens, as shown below:
3
Click Create project. The New Project page opens.
4
Define the project configurations listed below:
• Name
: A name for the project. This name is used to identify a project within a project source.
• Description
: An optional description for the project. This description can give a brief overview of the purpose of the project as compared to your other projects.
• Default document directory
: The default document directory for a project is the directory location to store all HTML documents. For more details on how to setup HTML documents for a project, see the
MicroStrategy Installation and Configuration Guide.
• Enable the guest user account for this project
: Select this check box to allow users to log in to a project without any user credentials.
Users can then to connect to Intelligence Server with a limited set of privileges and permissions (defined by the Public group).
• Enable Change Journal for this project
: Select this check box to enable the Change Journal for the project. An entry is automatically
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Guide.
• Languages
: Click this button to define languages that are available for metadata objects in this project. The languages you select are also languages that are available for the internationalization of your metadata objects. If a language is available for a project, you can provide object names, descriptions, and other information in various languages. For example, in a project you can provide the names of attributes, metrics, folders, and other objects in multiple languages.
For information on how to provide translated strings for metadata objects such as attributes, metrics, folders and so on, see the
Supplemental Reference for System Administration.
MicroStrategy provides translated strings for common metadata objects in the default available languages listed. For example, translated strings are available for the name and description of the
Public Objects folder as well as other common objects for each language listed as available by default. If you add other languages not listed, you must supply all translated strings for common metadata objects.
Be aware of the following:
•
When you create a new project, a language check ensures that the language settings of the user profile of the local machine
(the CURRENT_USER registry key), the language of the local machine (the LOCAL_MACHINE registry key), and the Project locale property match. When these properties do not match, it can lead to inconsistencies in the language display. The language check prevents these inconsistencies and ensures that the language display is consistent across the project.
•
These language options are not related to supporting the integration of translated data from your data source into
MicroStrategy. Information on defining your data source to support data internationalization is provided in
Supporting data internationalization, page 56
.
If you plan to use translated data from your data source with attributes in a project, you must define how data internationalization is enabled before creating attributes.
Enabling data internationalization is described in
Enabling data internationalization for a project, page 94
5
Click OK to create the project object.
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6
Proceed to the next section below (
Adding tables using the Warehouse
) to determine the tables to be used in your project.
Adding tables using the Warehouse Catalog
The warehouse tables for a project determine the set of data available to be analyzed in the project. You use the Warehouse Catalog to add warehouse tables to your project. The Warehouse Catalog lists all the tables in the data source to which you are connected through your database instance and to which your database login has read privileges.
The Warehouse Catalog queries the data source and lists the tables and columns that exist in it. From this list, you select the lookup, fact, and relationship tables to use in your new project. You should also include all other tables needed to complete your project, including transformation tables, aggregate tables, and partition mapping tables.
MicroStrategy schema objects such as attributes, facts, and tables are abstractions built on top of the tables and columns in the data source. Once tables are selected from the data source and added to your project, they become schema objects known as logical tables in MicroStrategy. Logical tables are representations of the tables that are available in the data
warehouse, and are discussed in detail in
.
The database login you use must have read privileges so you are able to view the tables in the selected warehouse. Database instances and database logins are MicroStrategy objects that determine the warehouse to which a project connects. To learn more about these objects, refer to the MicroStrategy Installation and Configuration
Guide.
To add and remove tables to the project using the Warehouse Catalog
1
In the Project Creation Assistant, select Select tables from the
Warehouse Catalog
. The Warehouse Database Instance dialog box opens.
2
Select a database instance from the drop-down list and click OK. The database instance selected in this dialog box determines which data source is accessed. The Warehouse Catalog opens.
If you have the MultiSource Option, you can add tables from multiple data sources into your project. For information on adding tables from
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Accessing multiple data sources in a project, page 318
You can edit your database instance by clicking Edit.
3
The left side of the Warehouse Catalog lists all available tables and the number of rows each table contains. The list on the right shows all the tables currently being used in the project, if any:
4
From the left side, select the tables you want to add to the Warehouse
Catalog, and click > to add the selected tables. Click >> to add all the listed tables.
5
To remove tables from your project, select them from the right side and click < to remove them. Click << to remove all the tables from your project.
Warehouse Catalog options
6
Right-clicking any table provides you with additional Warehouse Catalog functionality.
For example you can view rows in a table, specify a table prefix, copy a table, or specify a database instance for a table. For more information on
these abilities and how to use them, see
Managing warehouse and project tables, page 299
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7
To set advanced options, you can click Options on the Warehouse
Catalog toolbar.
For example, you can change the database instance, customize how tables and columns are read from the database system catalog, display extra table and row information, and decide whether schema objects are mapped automatically or manually. For more information on these abilities and how to use them, see
Modifying data warehouse connection and operation defaults, page 304
.
8
In the toolbar, click Save and Close to save your changes to the
Warehouse Catalog. The table definitions are written to the metadata.
This process can take some time to complete.
After exiting the Project Creation Assistant, you can still access the
Warehouse Catalog to add additional tables. For steps to access the
Warehouse Catalog to add tables to a project, see
Adding and removing tables for a project, page 297
.
The next step in the Project Creation Wizard involves creating schema objects: facts and attributes. Follow the instructions outlined in
Creating facts and attributes, page 101
Configuring additional schema-level settings, page 101
to learn how to create these schema objects and configure
additional schema-level settings for those objects.
Creating a new project using Architect
As an alternative to using the Project Creation Assistant that provides a wizard-style process to create a project, you can use Architect to define all the required components of your project from a centralized interface. Architect also provides a visual representation of your project as you create it, which helps to provide an intuitive workflow. For information on using Architect to create a production project, see
Creating projects using Architect, page 106
Enabling data internationalization for a project
MicroStrategy supports the internationalization of your data into the languages required for your users. This allows translated data from your data source to be integrated into a MicroStrategy project. This data can then be displayed in the various languages that reflect a user’s language preferences.
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More specifically, data internationalization allows attribute element data to be displayed in various languages. For example, the Month of Year attribute element data is supplied in both English and German, as shown in the reports below.
The attribute elements for the Month of Year attribute can be supplied in multiple languages through data internationalization. For example, the
January, February, and March attribute elements are displayed as Januar,
Februar, and März for users that are defined to view data in German.
Data internationalization is different than metadata internationalization. Metadata internationalization allows various
MicroStrategy metadata object names, descriptions, and other strings to be supplied in various languages. For example, in a project you can provide the names of attributes, metrics, folders, and other objects in multiple languages. For information on how to provide translated strings for metadata objects such as attributes, metrics, folders and so on, see the Supplemental Reference for System Administration.
MicroStrategy supports data internationalization through two different techniques. You can either provide translated data through the use of extra tables and columns, or you can provide separate databases to store your
translated data. For a description of these two methods, see
Supporting data internationalization, page 56
If you plan to use internationalized data with your attributes, you should define how data internationalization is enabled before creating attributes. By defining how internationalized data is represented in your data source,
Architect, the Attribute Creation Wizard, and the Fact Creation Wizard can recognize internationalized data when automatically creating attributes. This can save you the time that would be required to go back and modify attributes to recognize and use the internationalized data.
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To support one of these data internationalization methods, follow one of the procedures described below:
•
Enabling data internationalization through SQL queries, page 96
•
Enabling data internationalization through connection mappings, page 98
Enabling data internationalization through SQL queries
If you have configured your data source to use tables and columns to identify your internationalized data, you can define your MicroStrategy project to create SQL queries to return data in the required languages. The SQL queries are automatically generated to return the information in a user’s selected language based on the tables and columns you identify as containing translated data.
For information on configuring your data source to use tables and columns
to identify translated data, see
Using tables and columns for internationalization, page 57
To enable data internationalization through SQL queries, you must define the tables and columns used for your internationalized data, as described in the procedure below.
Prerequisites
•
A project has been created.
To enable data internationalization through SQL queries
1
In MicroStrategy Developer, log in to a project.
2
Right-click the project and select Project Configuration. The Project
Configuration Editor opens.
3
In the Categories list, expand Languages, and then select Data.
4
Select the Enable data internationalization check box, and then select the SQL based option.
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Select the check box next to a language to include it as a language enabled for translated data.
To add other languages to enable data internationalization, perform the steps below: a Click Add. The Available Languages dialog box opens. b Clear the Display metadata languages only check box. c Select the check box for languages to enable for data internationalization and click OK to return to the Project
Configuration Editor.
6
For each language, include suffixes for the columns and tables that contain your translated data.
Click ... in the Column Pattern for a language, to include suffixes for columns. Click ... in the Table Pattern for a language, to include suffixes for tables. For example, if your data source includes Spanish data in columns that end in _ES, type _ES in the Column Pattern.
The Column Pattern and Table Pattern options expect suffixes to identify your internationalized columns and tables. If you use prefixes or other naming conventions, you can use the functions listed below to identify the columns and tables that contain translated data:
•
LStrCut(string s, integer x): Removes x characters from the beginning of the character string s, and returns the remaining character string. For example, LStrCut(“Apple”,2) would return ple.
•
RStrCut(string s, integer x): Removes x characters from the end of the character string s, and returns the remaining character string. For example, RStrCut(“Apple”,2) would return App.
•
Concat(string s1, string s2): Appends the character string
s2 to the end of the character string s1, and returns the resulting character string. For example, Concat(“App”,”le”) would return
Apple.
The functions listed above can be used together to support various column and table naming conventions. You can use the parameter #0 to pass the column or table name into the function. To support a prefix rather than a suffix you can use the syntax listed below:
Concat(“Prefix”,#0)
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For example, to use a prefix of ES_ to identify columns that contain
Spanish data, you can use the syntax listed below:
Concat(“ES_”,#0)
7
Click OK to save your changes and close the Project Configuration Editor.
Enabling data internationalization through connection mappings
You can support data internationalization in your database by using separate databases for each translation. A user can then be granted access, through connection mappings, to the database that contains their preferred language.
For information on configuring your data source to use separate databases
and connection mappings to identify internationalized data, see
Using separate databases for internationalization, page 60
.
To enable data internationalization through separate databases and connection mappings, you must define the databases used for each language, as described in the procedure below.
Prerequisites
•
A project has been created.
To enable data internationalization through separate databases and connection mappings
1
In MicroStrategy Developer, log in to a project.
2
Right-click the project and select Project Configuration. The Project
Configuration Editor opens.
3
In the Categories list, expand Languages, and then select Data.
4
Select the Enable data internationalization check box, and then select the Connection mapping based option.
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5
Select the check box next to a language to include it as a language enabled for translated data.
To add other languages to enable data internationalization, perform the steps below: a Click Add. The Available Languages dialog box opens. b Clear the Display metadata languages only check box. c Select the check box for languages to enable for data internationalization and click OK to return to the Project
Configuration Editor.
6
For each language, from the Database Connection drop-down list, select a database connection to use for the language. The database connection determines the data source that stores the translated data and the login information to connect to the data source. You can leave this drop-down list blank to use the database connection for the default database instance of the project.
7
Click OK to save your changes and close the Project Configuration Editor.
Using read only or edit mode for schema editors
When opening a schema editor such as Architect, the Attribute Editor, the
Fact Editor, and so on, you can choose to open the editor in read only mode or edit mode. These two modes allow some users to view the definition of schema objects (read only mode), while one user can make changes to the schema objects (edit mode).
To create and modify a project, you must be able to edit the schema objects of a project. If another user is editing a schema object, this can lock the schema from being edited by other users. This maintains the integrity of the project schema.
However, if some users only want to use the various schema editors to view the definitions of project schema objects to learn more information about them, they can use read only mode. This allows another user to modify the schema, while other users can view the schema objects without making any modifications.
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The mode can be chosen in the Read Only dialog box that opens when opening any schema editor, which has the following options:
• Read Only
: Read only mode allows you to view the schema objects without locking the schema from other users. However, you cannot make any changes to the schema object.
If you open a schema editor in read only mode, the entire project is enabled to use read only mode. This means that every schema editor automatically opens in read only mode. However, from the Schema menu in Developer, you can clear the Read Only option to switch to edit mode.
If you have a schema editor open, you must close and re-open the schema editor to switch to edit mode for that schema editor.
If you are working in read only mode, you cannot access schema editors that require the ability to make updates to the project, which includes the
Attribute Creation Wizard, Fact Creation Wizard, Warehouse Catalog,
MDX Cube Catalog, and the options to update the project schema.
• Edit
: Edit mode provides the standard capabilities of modifying schema objects, and it locks the schema from being modified by all other users.
Therefore, only one user can be using edit mode for a project at a given time.
If you open a schema editor in edit mode, the entire project is enabled to use edit mode. This means that every schema editor automatically opens in edit mode. However, from the Schema menu in Developer, you can select the Read Only option to switch to read only mode. If you have a schema editor open, it is also switched to read only mode automatically and you cannot make any changes to schema objects.
You must use edit mode to access schema editors that require the ability to make updates to the project, which includes the Attribute Creation
Wizard, Fact Creation Wizard, Warehouse Catalog, MDX Cube Catalog, and the options to update the project schema.
If someone has already locked the schema for a project, a message is displayed and you can only open the schema editor in read only mode.
• Remember my selection as a default preference for future sessions
:
When this option is selected, the mode you select is defined as the default mode to use when opening any schema editor, and you are not prompted again to choose the mode for any schema editors. If you clear this check box, the mode you select is used for all schema editors only until you end the user session and close Developer.
If you select this check box, this becomes the default mode for all schema editors and you cannot change the default mode.
However, you can switch between read only mode and edit mode
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Read Only Mode
option.
Creating facts and attributes
This step in the project creation process involves using the Project Creation
Assistant or Architect to create two kinds of schema objects: facts and attributes.
Before you create facts and attributes, however, it is important to understand what facts and attributes are and the defining characteristics of each. This
Chapter 6, The Building Blocks of Business Data:
Chapter 7, The Context of Your Business Data: Attributes
Configuring additional schema-level settings
The final step in the project creation process involves configuring additional schema-level settings to add more analytical depth to your schema objects and optimize the project as a whole. These settings include:
• Fact definitions
: The Fact Editor allows you to create, edit, and configure facts one at a time. This is covered in
Creating and modifying simple and advanced facts, page 190
.
Architect also allows you to create, edit, and configure any and all facts for your project. This is covered in
Creating and modifying facts, page 138
• Attribute definitions
: The Attribute Editor allows you to create and edit attributes, attribute relationships, attribute forms, and attribute form
expressions for attributes one at a time. This is covered in
Adding and modifying attributes, page 232
Architect also allows you to create and edit any and all attributes, attribute relationships, attribute forms, and attribute form expressions for your project. This is covered in
Creating and modifying attributes, page 151
Defining attribute relationships, page 174
• User hierarchies
: The Hierarchy Editor allows you to create user hierarchies, which facilitate access to attribute and element browsing and
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Chapter 9, Creating Hierarchies to Organize and Browse Attributes
.
Architect also allows you to create any and all user hierarchies for your
Creating and modifying user hierarchies, page 181
• Advanced configurations
: These objects include transformations, aggregate tables, and partitioning and partition mappings:
The Transformation Editor allows you to create transformations, which are schema objects used for time-series analysis.
Transformations are covered in
Transformations to Define Time-Based and Other Comparisons
.
The tools used to create aggregate tables and partitions are the
Warehouse Catalog, the Metadata Partition Mapping Editor, and the
Warehouse Partition Mapping Editor. This information is covered in
Chapter 8, Optimizing and Maintaining Your Project
.
Now that you have completed most of the key steps in creating a new project, proceed to the chapters referenced above to complete the next steps in the project creation process.
Deploying your project and creating reports
After you create a project, you can deploy it to your user community using
MicroStrategy Web. Keep in mind, however, that if you completed only the steps in this chapter, the project you deploy will contain only basic facts and attributes. Proceed to the chapters listed above to add analytical depth and more functionality to your project.
Facts and attributes provide the backbone of the reports and documents created by report designers. Facts are used to create metrics, and metrics and attributes are essential components of reports.
Metrics, and other report objects such as filters, custom groups, and prompts, are beyond the scope of this guide. For a complete discussion of metrics, filters, reports, and other report objects, refer to the MicroStrategy
Basic Reporting Guide and the MicroStrategy Advanced Reporting Guide.
To learn more about how to deploy your project using MicroStrategy Web, refer to the MicroStrategy Installation and Configuration Guide.
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You can also begin creating reports in MicroStrategy Developer and
MicroStrategy Web. For information about creating reports in MicroStrategy
Developer, refer to the MicroStrategy Basic Reporting Guide; for creating reports in MicroStrategy Web, see the MicroStrategy Web Help.
Note the following:
•
MicroStrategy allows you to connect to your SAP BI, Microsoft
Analysis Services, and Hyperion Essbase data sources. For information about connecting to MDX Cube sources, see the
MicroStrategy MDX Cube Reporting Guide.
•
For information on how to use your own customized SQL statements to create reports, refer to the MicroStrategy Advanced
Reporting Guide.
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5
5.
C
REATING A
P
ROJECT
U
SING
A
RCHITECT
Introduction
MicroStrategy includes a project design tool known as Architect. Architect allows you to define all the required components of your project from a centralized interface. Architect also provides a visual representation of your project as you create it, which helps to provide an intuitive workflow.
Architect is provided in addition to the Project Creation Assistant, which is a wizard-style tool that steps you through the process of creating a project.
Rather than providing a step-by-step process to create a project, Architect allows you to see your project take shape as you create it. For a comparison of
Architect and the Project Creation Assistant, see
.
Architect provides a wide range of project creation and modification tasks, which are covered in the sections of this chapter listed below:
•
Creating and modifying projects, page 106
•
Adding, removing, and administering tables, page 126
•
Creating and modifying facts, page 138
•
Creating and modifying attributes, page 151
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•
Defining attribute relationships, page 174
•
Creating and modifying user hierarchies, page 181
Project Design Guide
Creating and modifying projects
Architect allows you to complete all tasks related to initial project creation as well as modifications required over the full life cycle of a project, which includes:
•
Creating projects using Architect, page 106
•
Modifying projects using Architect, page 109
•
Defining project creation and display options, page 111
•
Using the Architect toolbar, page 125
Creating projects using Architect
Rather than using the Project Creation Assistant that provides a step-by-step process to create a project, you can use Architect to visually perform the initial creation of a project. However, before you can begin using Architect, you must first create the project object with the Project Creation Assistant and define various Architect project creation options.
Prerequisites
•
Before creating a project, you must connect to a metadata repository and to a data source, as described in the sections listed below:
Connecting to the metadata repository, page 84
Connecting to a data source, page 85
You should also review the information in
before creating a project using Architect.
To create a new project using Architect
1
Log in to a project source in MicroStrategy Developer.
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To create a project source which connects to your data through
Intelligence Server, see the Configuring and Connecting Intelligence
Server chapter of the MicroStrategy Installation and Configuration
Guide.
2
From the Schema menu, select Create New Project. The Project
Creation Assistant opens, as shown below:
3
Click Create project. The New Project page opens.
4
Define the project configuration settings listed below:
• Name
: A name for the project. This name is used to identify a project within a project source.
• Description
: An optional description for the project. This description can give a brief overview of the purpose of the project as compared to your other projects.
• Default document directory
: The directory location to store all
HTML documents. For more details on how to set up HTML documents for a project, see the Installation and Configuration
Guide.
• Enable the guest user account for this project
: Select this check box to allow users to log in to a project without any user credentials.
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Users can then to connect to Intelligence Server with a limited set of privileges and permissions (defined by the Public group).
• Enable Change Journal for this project
: Select this check box to enable the Change Journal for the project. An entry is automatically included in this journal when any object in a project is modified, allowing you to keep track of any changes to your project. For information on the Change Journal, see the System Administration
Guide.
• Languages
: Click this button to define languages that are available for metadata objects in this project. The languages that you select are also languages that are available for the internationalization of your metadata objects. If a language is available for a project, you can provide object names, descriptions, and other information in various languages. For example, in a project you can provide the names of attributes, metrics, folders, and other objects in multiple languages.
For information on how to provide internationalized data for metadata objects such as attributes, metrics, folders, and so on, see the Supplemental Reference for System Administration.
MicroStrategy provides internationalized data for common metadata objects in the available, default languages listed. For example, data is available for the name and description of the Public Objects folder as well as other common objects for each language listed as available by default. If you add other languages, you must supply all internationalized data for common metadata objects.
Be aware of the following:
•
When you create a new project, a language check ensures that the language settings of the user profile of the local machine
(the CURRENT_USER registry key), the language of the local machine (the LOCAL_MACHINE registry key), and the Project locale property match. If these properties do not match, it can lead to inconsistencies in the language display. The language check prevents these inconsistencies and ensures that the language display is consistent across the project.
•
These language options are not related to supporting the integration of internationalized data from your data source into
MicroStrategy. Information on defining your data source to support data internationalization is provided in
Supporting data internationalization, page 56
.
If you plan to use internationalized data from your data source with attributes in a project, you must define how data internationalization is enabled before creating attributes.
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Enabling data internationalization is described in
Enabling data internationalization for a project, page 94
5
Click OK to create the project object.
6
Click the arrow for Architect. The Warehouse Database Instance dialog box opens.
To continue creating the project with the Project Creation
Creating a new project using the Project Creation
.
7
Select a database instance from the drop-down list. The database instance selected in this dialog box determines which data source is accessed.
8
Click OK. MicroStrategy Architect opens.
9
From the Architect Button, select Settings. The MicroStrategy Architect
Settings dialog box opens.
10
Define the various options for how Architect displays data, automatically creates and maps schema objects, loads the Warehouse Catalog, and updates the project’s schema.
Reviewing and defining these options before using Architect can save you a lot of time when creating and modifying projects. For information on all
Using the Architect toolbar, page 125, page 106
.
11
Click OK. You can now begin to add tables to your project, and create attributes, facts, user hierarchies, and so on. These tasks are listed below: a
b
c
d
Defining attribute relationships, page 174
e
Creating user hierarchies, page 181
Modifying projects using Architect
After creating your project, modifications to schema objects can be completed in separate editors including the Fact Editor, Attribute Editor,
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Hierarchy Editor, and so on. These editors provide all of the simple and advanced schema object features, allowing you to create and modify schema objects one-by-one.
Architect provides a single integrated environment in which you can make project-wide changes as well as create or modify individual schema objects.
Rather than creating or modifying schema objects one-by-one, you can create and modify multiple schema objects for your project. Architect also allows you to add tables to your project and create or modify attributes, facts, and user hierarchies all from the same interface.
Modifying your project using Architect also allows you to lock the schema of your project, preventing users from encountering reporting issues or returning outdated data during periods of scheduled project maintenance.
When opening a project with Architect, basic information is loaded for all the objects in a project. These objects include attributes, facts, tables, and so on. Once the project is open and accessible in Architect, additional information for the objects are loaded incrementally as well as on an as-needed basis. This allows Architect to load projects faster than if all information for a project was loaded when first opening the project in Architect.
The procedure below provides steps to modify a project using Architect.
Prerequisite
•
A project has been created.
To modify a project using Architect
1
In MicroStrategy Developer, log in to a project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s
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•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Architect Button, select Settings. The MicroStrategy Architect
Settings dialog box opens.
5
Define the various options for how Architect displays data, automatically creates and maps schema objects, loads the Warehouse Catalog, and updates the project’s schema.
Reviewing and defining these Architect options before using Architect can save you a lot of time when creating and modifying projects. For information on all the options available, see
Using the Architect toolbar, page 125, page 106
6
Click OK to return to Architect. You can now begin to add or remove tables to your project and create and modify attributes, facts, user hierarchies, and so on:
•
Adding, removing, and administering tables, page 126
•
Creating and modifying facts, page 138
•
Creating and modifying attributes, page 151
•
Defining attribute relationships, page 174
•
Creating and modifying user hierarchies, page 181
Defining project creation and display options
Architect provides various options that determine how you can create and modify projects. Reviewing and defining these options before using Architect can save you time when creating and modifying projects.
The options you can define determine how Architect displays data, automatically creates and maps schema objects, loads the Warehouse
Catalog, and updates the project’s schema. Some of these options are available in the MicroStrategy Architect Settings dialog box. For steps to access this dialog box, see
Accessing the Architect options, page 113
available options are described in the sections listed below:
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•
Controlling the view that is displayed when starting Architect, page 113
•
Disabling the ability to add tables, page 120
•
Automatically updating the project schema, page 120
•
Creating metrics based on the facts of a project, page 121
You can also access various Architect options from the Architect toolbar, as described below:
•
From the Home tab:
In the View area, click the arrow icon ( ) to access the following configurations:
–
Displaying columns and attribute forms in tables, page 117
In the Auto Arrange area, click the arrow icon ( ) to access the following configurations:
–
Defining the layout of the Architect working area, page 124
•
From the Design tab:
In the Auto Recognize area, click the arrow icon ( ) to access the following configurations:
–
Automating the creation of facts and attributes, page 113
–
Automatically mapping columns to existing attribute forms and facts, page 116
–
Automatically defining attribute relationships, page 122
In the Editors area, click the Edit logical size of tables icon (shown below) to access the Logical Size Editor, which provides access to the following configurations:
–
Defining logical sizes for tables, page 124
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Accessing the Architect options
You can access the MicroStrategy Architect Settings dialog box from
Architect. In Architect, from the Architect Button, select Settings. The
MicroStrategy Architect Settings dialog box opens.
Controlling the view that is displayed when starting Architect
Architect allows you to choose whether the Project Tables View or the
Hierarchy View is displayed at startup. You can configure this behavior using the Choose the default open view drop-down list. This drop-down list is available in the Configuration tab of the MicroStrategy Architect Settings dialog box. You can choose from the options listed below to determine what view is displayed when Architect opens:
• Last Used
: Select this option to display the view that was used last during the most recent use of Architect.
• Project Tables View
: Select this option to display the Project Tables
View.
• Hierarchy View
: Select this option to display the Hierarchy View.
Automating the creation of facts and attributes
You can save time during the schema creation process of designing a project by allowing Architect to automatically create attributes and facts. Architect can create attributes and facts automatically when you add tables to your project. The attributes and facts are created based on data types, database column names, primary and foreign keys, and other schema creation heuristics.
You can define how attributes and facts are created when tables are added to your project by defining the automatic column recognition rules. To access the options listed below, from the Design tab, in the Auto Recognize area, click the arrow icon ( ):
• Do not auto recognize
: Select this option to disable the automatic creation of attributes and facts when tables are added to your project using Architect.
This can be a good option to use if you are updating a project in which you have already defined the bulk of the project schema. In this scenario, it prevents Architect from automatically defining attributes and facts that
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• Auto recognize
: Select this option to enable the automatic creation of attributes and facts when tables are added to your project using Architect.
This option can save time during the schema creation process of designing a project by allowing Architect to automatically create attributes and facts.
When selecting this option, facts are created for database columns that use numeric data types and are not used for attribute forms. Attributes and attribute forms are created based on various schema creation heuristics and the rules that you define with the Advanced Options listed below:
Separator
: Type the character used as a separator in your database column names. For example, a database column name such as
USER_ID uses the underscore character (_) as a separator.
Attribute naming rule
: Type database column name suffixes that identify that the column should be mapped to a new attribute as the identity form. For example, the suffix ID is commonly used for database columns that are mapped to attributes as an identity form.
Use a semicolon (;) to separate suffixes that are to be mapped to new attributes.
You can also define how the attribute name is created. Use the vertical bar (|) to define what the suffix is replaced with in the resulting attribute name. The text to the left of the | character is the suffix, and the text to the right of the | character is what replaces the suffix in the attribute name that is created.
For example, including ID|; creates new attributes for any database columns that use the suffix ID, and removes the ID suffix from the attribute name. When a table that uses a column such as USER_ID is imported into the project, a new attribute named User is created.
Including DT|DATE; creates new attributes for any database columns that use the suffix DT, and replaces the DT suffix with DATE when creating an attribute name. When a table that uses a column such as
YEAR_DT is imported into a project, a new attribute named Year Date is created.
Attribute form naming rule
: Type database column name suffixes that identify that the column should be mapped to a new attribute form. For example, the suffix DESC is commonly used for database columns that are mapped to description attribute forms.
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Use a semicolon (;) to separate suffixes that are to be mapped to new attribute forms.
You can also define how the attribute form name is created. Use the vertical bar (|) to define what the suffix is replaced with in the resulting attribute form name. The text to the left of the | character is the suffix, and the text to the right of the | character is what replaces the suffix in the attribute form name that is created.
For example, including DSC|DESC; creates new attribute forms for any database columns that use the suffix DSC, and replaces the DSC suffix with DESC when creating an attribute form name. When a table that uses a column such as PRODUCT_DSC is imported into a project, a new attribute form named Product Desc is created.
In addition to using these rules to define attributes and attribute forms, selecting the Auto recognize option also employs other schema creation heuristics:
– The automatic column mapping rules described in
Automatically mapping columns to existing attribute forms and facts, page 116
are employed to map columns to existing attribute forms that use the columns in their definitions.
– An attribute is created for any column defined as a primary or foreign key, and the column name for the primary key is used to define the attribute name. The column name for the primary key is used to define the attribute name even if the primary key column is not included in the project.
– Every column must be mapped to either a fact or an attribute. If none of the schema creation heuristics or the rules you define can determine whether to create a fact or attribute for the column, an attribute is created for the column.
Auto recognize form format
: Select this check box to define when attribute form formats are applied to newly created attribute forms.
Attribute form formats control how the form is displayed and how filters are defined. For example, specifying a format type of Big
Decimal allows users to preserve precision when qualifying on a form with more than 15 digits. For more detailed information on each form format type, see
You can define rules on when to apply attribute form formats by using the Options listed below:
– Default form format: Select the form format to use for forms that do not fit any of the other form format rules. The default selection is Text. You cannot define the rules for when to use the default
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– Form format: Select a form format, and define the rules for applying the form format to newly created attribute forms. The rules for when to apply a certain form format are defined by the options listed below. If you supply neither a column data type nor a column naming rule for a form format, the form format is never automatically applied to a newly created attribute form.
– Column data type: Select the column data types relevant to a form format. Only columns with one of the selected data types are considered to be defined as the associated form format. For example, the Number form format is defined to be used for columns with decimal, double, float, integer, numeric, and real data types by default. If you do not select relevant data types for a form format, only the column naming rules described below are used to determine when to apply the associated form format.
– Naming rules: Type the column names relevant to a form format, separated by a semicolon (;). Only columns with one of the provided column names are considered to be defined as the associated form format. For example, the Picture form format is defined to be used for columns with Picture, Image, Icon,
Drawing, Figure, Photo, and Print column names by default. If you do not type relevant column names for a form format, only the column data type rules described above are used to determine when to apply the associated form format.
An attribute form can only have one form format. The rules for each form format should be defined so that they are all mutually exclusive. If an attribute form meets the rule requirements of more than one form format, the first form format in the Form format list is applied to the form.
• Display message if objects are not recognized
: If this check box is selected, which it is selected by default, a message is displayed during the automatic creation of schema objects if some of these objects are not created. By clearing this check box, this message is never displayed.
Automatically mapping columns to existing attribute forms and facts
You can save time during the schema creation process of designing a project by allowing Architect to automatically map columns to attribute forms and
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You can enable the automatic mapping of columns to attribute forms and facts in your project when tables are added to your project by selecting the
Use automatic column mapping
check box. To access this option, from the
Design tab, in the Auto Recognize area, click the arrow icon ( ).
When this option is enabled and tables are added to your project, the column expressions included in the table are compared to the column expressions used in attribute forms and facts. If an attribute form or fact is found that matches the column expression, then the column is mapped to that attribute form or fact. For example, the MicroStrategy Tutorial project maps the
Revenue fact to the column TOT_DOLLAR_SALES. If automatic column mapping is enabled and you use Architect to add a table that includes the
TOT_DOLLAR_SALES column to the project, the TOT_DOLLAR_SALES column for the table is automatically mapped to the Revenue fact.
The Use automatic column mapping option is particularly helpful to automatically map columns in newly added tables to existing facts and attributes, without creating any new facts or attributes. To map columns in newly added tables to existing facts and attributes, as well as create new facts and attributes based on various rules, you should enable the Auto recognize option, as described in
Automating the creation of facts and attributes, page 113
While enabling or disabling this option, you can also select or clear the
Display message if objects are not recognized
check box. If this check box is selected, which it is selected by default, a message is displayed during the automatic creation of schema objects if some of these objects are not created. By clearing this check box, this message is never displayed.
Displaying columns and attribute forms in tables
When you add tables to your project using Architect, you can view the various schema objects included in the table as well as the columns that are used to define the schema objects.
The display of data available in the tables included in your project can be defined using the options listed below.
To access the options listed below, from the Home tab, in the View area, click the arrow icon ( ).
•
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Physical Tables View area
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Prior to selecting options in the Project Tables view area, you must choose to display the data available in tables in either physical view or logical view.
This can be defined using the following Architect toolbar options:
Display table physical view
: On the toolbar, select this option to display the columns that are available in the table. Columns are displayed in the form Column_Name : Column_Data_Type. For example, the
YR_CATEGORY_SLS table from the MicroStrategy Tutorial project shown below is displayed in physical view:
•
If you display tables in physical view, the options in the Project Tables
View area have no affect on the display of data within the tables in
Architect.
Display table logical view
: On the toolbar, select this option to display the columns that are available in the table and how they relate to
MicroStrategy schema objects. The LU_YEAR and LU_REGION tables from the MicroStrategy Tutorial project shown below are used to
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If you display tables in logical view, the options in the Project Tables View area can be used to modify the display of data within tables in Architect.
These options are described below:
Display available columns on logical tables
: Select this logical view option to display columns that are available in the table but are not used in any schema objects in the project. Columns are displayed in the form Column_Name : Column_Data_Type.
In the LU_YEAR table shown above, selecting this option displays the
PREV_YEAR_ID : INTEGER column which has not been mapped to a schema object.
You can also display this information for an individual table by right-clicking a table, pointing to Properties, then pointing to
Logical View
, and selecting Display Available Columns.
Selecting this option also allows you to select the option described below:
– Display columns used for data internationalization: Select this option to display columns that are available in the table that used for data internationalization. Columns are displayed in the form
Column_Name : Column_Data_Type.
In the LU_REGION table shown above, selecting this option displays the various REGION_NAME columns that are used to translate the name of a region into various languages.
For information on supporting internationalized data in a data
Supporting data internationalization, page 56
.
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Display attribute forms on logical tables
: Select this option to display attribute forms that are mapped to columns of the table.
Attribute forms are displayed in the form Attribute_Form_Name :
Attribute_Form_Category (Column_Name).
In the LU_YEAR table shown above, selecting this option displays the
ID form and the Date form for the attribute Year.
You can also display this information for an individual table by right-clicking a table, pointing to Properties, then pointing to
Logical View
, and selecting Display Attribute Forms.
Visible Links area
• Maximum number of visible links per table row
: Define the number of link lines that are displayed when you select a column, fact, attribute, or attribute form in a table. When selecting one of these objects in a table, a line is drawn to each occurrence of this object in other tables included in the project. For example, selecting the Year attribute in the LU_YEAR table displays a line that connects to every other occurrence of the Year attribute in other tables.
Disabling the ability to add tables
By default, Architect allows you the flexibility to browse the Warehouse
Catalog to add new tables to your project. However, it can be beneficial to disable the ability to add tables to your project if your project includes all the required tables. This prevents any unnecessary tables from being added to the project, which can trigger the creation of unnecessary schema objects. It also provides better performance while using Architect since all the tables in the Warehouse Catalog do not have to be made available.
You can disable the ability to add new tables to your project using Architect by selecting the Disable loading warehouse catalog check box. This option is available in the Configuration tab of the MicroStrategy Architect Settings dialog box. Any tables not included in the project are hidden from view in
Architect.
Automatically updating the project schema
Changes made in Architect affect the schema of your project. By default, the schema of your project is updated when you save your changes and exit
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Architect. This ensures that your project is updated to reflect your modifications.
Updating your project schema every time that you exit Architect can be disabled. The schema update process can require a substantial load on your
Intelligence Server and require a considerable amount of time to complete.
You may also have other project updates that you plan to perform after using
Architect. In these scenarios, you can disable the project schema update process, and instead execute a schema update manually at the desired time.
You can disable the project schema update process from occurring when closing Architect by clearing the Update schema after closing Architect check box. This option is available in the Configuration tab of the
MicroStrategy Architect Settings dialog box.
Creating metrics based on the facts of a project
Architect allows you to create metrics based on the facts created for a project.
This can reduce the time it takes to create the basic metrics for your project.
The options to create metrics based on the facts of your project are available in the Metric Creation tab of the MicroStrategy Architect Settings dialog box.
On this tab, you can allow the automatic creation of metrics using the aggregation functions listed below:
• Avg
: To create metrics that perform an average calculation on the fact expression.
• Sum
: To create metrics that perform a summation calculation on the fact expression.
• Count
: To create metrics that perform a count calculation on the fact expression.
• Min
: To create metrics that perform a minimum calculation on the fact expression.
• Max
: To create metrics that perform a maximum calculation on the fact expression.
• Var
: To create metrics that perform a variance calculation on the fact expression.
• Advanced Options
: Click Advanced Options to open the Advanced
Options dialog box, which lets you define metric naming conventions. A metric naming convention can be defined for each aggregation function listed above. When a metric is created for an aggregation function, the metric naming convention for that aggregation function is used to define
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Average %1
If you create a fact named Cost and select to create metrics with the Avg aggregation function, a metric with the name Average Cost is created by substituting the fact name for the %1 characters in the metric naming convention.
When a fact is created for a project, metrics are created for the fact using the selected aggregation functions. A separate metric is created to support each aggregation of a fact. The metrics are created in the Public Objects/
Metrics folder of a MicroStrategy project.
Automatically defining attribute relationships
Architect allows you to create attribute relationships based on the design of the data in your data source. This can help reduce the time required to create the relationships between the attributes within a project.
To access the options to automatically create attribute relationships between the attributes within a project option, from the Design tab, in the Auto
Recognize area, click the arrow icon ( ). You can select from the following options to automatically create attribute relationships:
• Do not automatically create relations
: Attribute relationships are not automatically created based on the design of the data in your data source.
For information on manually defining attribute relationships with
Architect, see
Defining attribute relationships, page 174
.
• Automatically create relations in System Hierarchy
: Attribute relationships are automatically created based on the design of the data in your data source as you add tables to your project. These attribute relationships are created automatically the first time you switch to
Hierarchy View after an applicable attribute has been added to the project. To manually execute the action of automatically defining attribute relationships you can use the System Hierarchy dialog box, as described in
Automatically defining attribute relationships, page 178
Attribute relationships are created based on the rules that you select in the Advanced Options, as described below:
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Based on Primary Keys/Foreign Keys
: Creates attribute relationships based on the primary keys and foreign keys defined on your tables. Each attribute that acts as a foreign key of a table is defined as a parent attribute of each attribute that acts as a primary key of the same table. The attribute relationship is defined as a one-to-many relationship from the foreign key attribute (parent attribute) to the primary key attribute (child attribute).
Based on lookup tables
: Creates attribute relationships based on lookup tables that do not include primary key or foreign key information. To define a table as a lookup table for an attribute, see
. Each attribute that defines a table as its lookup table is defined as a child attribute of all other attributes in the same table, that do not define the table as its lookup table. Each attribute relationship is defined as a one-to-many relationship from the parent attribute to the child attribute.
Based on sample data from the table
: Creates attribute relationships for attributes that share the same lookup table. To define a table as a lookup table for an attribute, see
Architect analyzes sample data for the table. The attributes with fewer distinct values are defined as parents of the attributes with more distinct values, using a one-to-many relationship from the parent attribute to the child attribute. For example, a lookup table includes four rows of data, which include data related to year and quarter. Each row includes the same year (for example, 2009), but the quarter changes for each row (Q1, Q2, Q3, Q4). In this case, the Year attribute is created as a parent of the Quarter attribute.
Show preview result
: Displays the attribute relationships that can be automatically created in a Results Preview dialog box. From this dialog box, you can select which attribute relationships should be created, and which attribute relationships should be excluded. This is the default behavior. If you clear the Show preview result check box, all of the potential attribute relationships are created automatically without first displaying them in the Results Preview dialog box.
After all relationships are determined by the rules that you selected,
Architect performs a final analysis on the attribute relationships that are to be created. Any attribute relationships that are found to be redundant are not created. This ensures that attribute relationships are created that properly reflect the design of the data in your data source. For information on modifying the attribute relationships that are created, see
Defining attribute relationships, page 174
.
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• Display message if objects are not recognized
: If this check box is selected, which it is selected by default, a message is displayed during the automatic creation of attribute relationships if some of these attribute relationships are not created. By clearing this check box, this message is never displayed.
Defining the layout of the Architect working area
Architect allows you to define the layout of the working area for the Project
Tables View and the Hierarchy View in Architect using the auto arrange options on the Architect toolbar. You can also define the margins of this working area using the options listed below:
To access the options listed below, from the Home tab, in the Auto
Arrange
area, click the arrow icon ( ).
• Margin to the left
: Type the number of pixels to use as a margin for the left side of the Architect working area. This provides white space at the left margin of the working area, which can provide visual separation between the working area and the other panes and options available on the Architect interface.
• Margin to the right
: Type the number of pixels to use as a margin for the right side of the Architect working area. This provides white space at the right margin of the working area, which can provide visual separation between the working area and the other panes and options available on the Architect interface.
Defining logical sizes for tables
Architect allows you to view and define the logical sizes for the tables in a project using the Logical Size Editor. The logical table size determines which logical table to use to answer a query when multiple logical tables would provide the same data. The table with the lowest logical table size is used, as a lower logical table size means the table has a higher level of aggregation.
The performance of accessing a table with a higher level of aggregation is usually better than accessing a table with a lower level of aggregation.
To access the options listed below, in the Editors area, click the Edit
logical size of tables
icon (shown below) to access the Logical Size
Editor.
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The Logical Size Editor displays several columns, as follows:
• Size locked
: Displays whether a table’s size is locked (selected) or unlocked (cleared). All tables have their size unlocked by default. When a table’s logical size is locked the table is excluded from the logical table size calculation when a schema update is performed. This helps to retain any table sizes that are manually defined.
• Table name
: Displays the name of each table in the warehouse.
• Size value
: Displays the current size value for each table. Architect initially assigns logical table sizes based on an algorithm that takes into account the number of attribute columns and the various levels at which they exist in their respective hierarchies. See
Defining logical table sizes, page 452
for details on how table sizes are calculated.
You can click in the Size value cell for a table to manually define the logical size for the table. You can then lock the table size for the table to ensure that this manual value is retained.
• Row count
(optional): This column displays the number of rows in each table. Click the Display each table row count in the editor icon ( ) to display the Row count column.
Using the Architect toolbar
Architect provides a set of toolbar icons, shown in the image below, to facilitate the tasks of creating and modifying a project.
The options that are available on the toolbar are determined by whether you are in Project Tables View or Hierarchy View. To review detailed information on each toolbar option, with Architect open, press F1 to open the Architect
Help and search for the topic Architect: Toolbar options.
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Adding, removing, and administering tables
The warehouse tables for a project determine the set of data available to be analyzed in the project. You can use Architect to add, remove, update, and manage tables for your project.
You can also use the Warehouse Catalog to add tables to and remove tables from your project, as described in
Architect displays all of the available data sources for the project in the
Warehouse Tables pane, as shown below.
If the Warehouse Tables pane is not displayed in Architect, from the
Home
tab, in the Panels area, click Show the Warehouse tables
section
. The Warehouse Tables pane is only available in the Project
Tables View of Architect.
Within each data source is a list of all the tables in the data source to which you are connected through a database instance. From this list, you select the lookup, fact, and relationship tables to use in your new project. You should also include all other tables needed to complete your project, including transformation tables, aggregate tables, and partition mapping tables.
The database login you use must have read privileges so you are able to view the tables in the selected data source. Database instances and
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Guide.
Using Architect, you can perform the following tasks to add, remove, update, and manage tables for your project:
•
Displaying data sources in Architect, page 127
•
•
•
Updating, modifying, and administering tables, page 131
•
Organizing project tables: Layers, page 137
Displaying data sources in Architect
You can define which data sources in your system are displayed in Architect.
Once displayed, you can begin to add tables from the data source into your project.
Prerequisites
•
A database instance has been created for the data source. For information on database instances and examples on how to create them, see the
Installation and Configuration Guide.
•
You are creating or modifying a project using Architect. For instructions, see
Creating and modifying projects, page 106
.
To display data sources in Architect
1
With a project open in Architect, from the Architect Button, select
Select Database Instance
. The Select Database Instance dialog box opens.
2
From the list of data sources, you can display or hide a data source:
•
Select a check box for a data source to display it in Architect. Once displayed, you can begin to add tables from the data source into your project.
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Clear a check box for a data source to hide it in Architect. You cannot hide a data source that is used in a project.
3
Click OK to save your changes and return to Architect.
Adding tables
Before you can begin creating attributes, facts, and hierarchies for your project, you must add tables to your project.
Along with making data available in your project, adding tables to your project can also trigger the creation of attributes and facts, and the mapping of columns to attributes and facts. For information on defining how attributes and facts are created and mapped when adding tables to your project, see
Automating the creation of facts and attributes, page 113
Automatically mapping columns to existing attribute forms and facts, page 116
Once tables are selected from the data source and added to your project, they become schema objects known as logical tables in
MicroStrategy. Logical tables are representations of the tables that are available in the data warehouse, and are discussed in detail in
.
The procedure below provides steps to add tables to your project using
Architect.
Prerequisites
•
You are creating or modifying a project using Architect. For instructions, see
Creating and modifying projects, page 106
.
•
The ability to add tables using Architect is enabled. For information on enabling and disabling the ability to add tables using Architect, see
Disabling the ability to add tables, page 120
.
•
If changes have been made to the tables within the tables’ data source, you must update the structure of the tables in MicroStrategy to ensure they can be added to a project successfully. For steps to update table
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To add tables to a project using Architect
1
With a project open in Architect, select the Project Tables View.
2
From the Warehouse Tables pane, expand a data source.
If you have the MultiSource Option, you can add tables from multiple data sources into your project. For information on adding tables from multiple data sources into your project with the Warehouse Catalog or
Architect, see
Accessing multiple data sources in a project, page 318
.
3
Right-click a table, and then select Add Table to Project. The table is added to the project and included in the Project Tables View of Architect.
To view a sample of the data within a table, right-click the table and select Show Sample Data.
4
If you have selected to automatically create attributes, attribute forms, and facts based on schema creation heuristics (see
Automating the creation of facts and attributes, page 113
Automatically mapping columns to existing attribute forms and facts, page 116
), the Results
Preview dialog box opens.
Attributes, attribute forms, and facts that can be created based on the columns of the table added to the project are displayed. Select the check box for each object to create in the table when the table is added to the project. If more than one attribute form is available for creation for an attribute, you must select the ID form. Any other attribute forms for that attribute are optional. Click OK to complete the processes of adding the table to the project and creating any selected attributes, attribute forms, and facts.
5
Once you have imported tables for your project, you can continue with other project design tasks, which include: a
Creating and modifying facts, page 138
b
Creating and modifying attributes, page 151
c
Defining attribute relationships, page 174
d
Creating and modifying user hierarchies, page 181
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Removing tables
You can remove tables from your project to keep your project from becoming cluttered with tables that are no longer required for your project. You can remove a table from a project using Architect by accessing Project Tables
View
, right-clicking a table, and selecting Delete.
However, you cannot remove a table from a project if schema objects in the project are dependent on the table. For example, an attribute is dependent on the table that is set as the lookup table for the attribute.
When you attempt to remove a table that has dependent objects, you can view a list of dependent objects for the table. You must first delete all dependent objects from the project before you can delete the table.
Removing tables from a project that have been removed from a data source
When tables that are included in a project are removed from the data source that they were available in, you can use Architect to remove these tables from the project. This allows your project to display an accurate list of tables that are included in the project from the selected data source.
The steps below show you how to perform this task using Architect. To remove these tables using the Warehouse Catalog, see
Removing tables from the Warehouse Catalog that have been removed from their data source, page 302
.
If tables that were not included in a project are removed from the data source, these tables are automatically removed from the display of available tables in Architect.
To remove tables from a project that have been removed from a data source
1
In MicroStrategy Developer, log in to a project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit
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Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
If the Warehouse Tables pane is not displayed, from the Home tab, in the
Panels
area, click Show the Warehouse tables section.
5
In the Warehouse Tables pane, expand the database instance for the data source, which has had tables removed. The Warehouse Catalog dialog box opens. If this dialog box does not open, there are no tables that need to be removed from the project.
6
Select the check box for a table to remove it from the project.
7
After you have selected all the tables to delete, click OK to remove the tables that were selected to be deleted and return to Architect.
8
If a message is returned that a table cannot be removed because objects depend on it, you can click Yes to review a list of dependent objects. To remove the table from the project, all dependent objects must be deleted.
9
From the Home tab, in the Save area, click Save and Update Schema to save your changes.
Updating, modifying, and administering tables
With Architect, you can update and manage the tables in your project to ensure that the data in your project is up to date and accurate, as described in the sections listed below:
•
•
Modifying and viewing table definitions, page 132
•
Modifying data warehouse connection and operation defaults, page 136
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Updating tables
With Architect, you can update individual tables or all of the tables for a data source at once. This ensures that the data available in your project is up to date with any changes made to the tables in the data source. The procedure below describes how to update tables using Architect.
Prerequisite
•
You are creating or modifying a project using Architect. For instructions, see
Creating and modifying projects, page 106
.
To update tables using Architect
1
With a project open in Architect, select the Project Tables View.
2
From the Warehouse Tables pane, you can update all tables for a data source or update individual tables as described below:
•
To update all tables for a data source, right-click a data source, and select Update. All the tables for the data source are updated to reflect their definitions in the data source.
•
To update an individual table, expand a data source, right-click a table, and select Update Structure. The table is updated to reflect its definition in the data source.
Modifying and viewing table definitions
Once a table is added to a project, you can modify and view table definitions using the Properties pane in Architect. To view the various properties and contents of a table in Architect, from the Tables tab of the Properties pane, select the table from the drop-down list. The YR_CATEGORY_SLS table of the
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MicroStrategy Tutorial project shown below is used as an example of how you can modify and view tables definitions using Architect.
When you select a table in Architect, the Properties pane allows you to modify and view table definitions as described below.
You can select a property in the Properties pane to view a description of the property. The description is displayed at the bottom of the
Properties pane.
•
Defining and viewing table definitions: Definition section, page 134
•
Modifying attributes in a table: Mapped Attributes section, page 135
•
Modifying facts in a table: Mapped Facts section, page 135
•
Modifying column names and data types in a table: Member Columns section, page 136
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Defining and viewing table definitions: Definition section
When you select a table in Architect, the Definition section of the Properties pane displays the various properties for the table. These properties and how to use them are described below:
• ID
: The identifier of the table. You cannot modify this value.
• Name
: The name of the table in a MicroStrategy project. By default, the name is inherited from the table name in the data source.
• Description
: The description of the table. A description can help explain the purpose of a table in a project.
• Hidden
: Specifies whether the table is defined as hidden. Select the check box to set the value to True, which defines the table as hidden.
Objects that are hidden are not displayed to a user unless the user has changed his or her Developer Preferences and selected the Display
hidden objects
check box. Therefore, defining an object as hidden does not necessarily prevent users from viewing or accessing an object. The best way to prevent users from viewing or accessing an object is to restrict the user permissions for it.
• Location
: The location of a table in a project.
• Database Name
: The name of the table in the data source. If the name of a table has changed in the data source, you can type the new name for the table in this property. This allows a MicroStrategy project to locate a table after its name has changed in its data source.
• Row Count
: The number of rows in the table. To calculate a table’s row count, right-click the table and select Calculate Row Count.
The Calculate Row Count option is displayed only if the data source for the table is expanded in the Warehouse Tables pane.
• Table Name Space
: The table name space for a table in a data source.
For information on table name spaces, see
Ignoring table name spaces when migrating tables, page 310
.
• Logical Size
: The logical size of a table, which is based on an algorithm that takes into account the number of attribute columns in a table and the various levels at which they exist in their respective hierarchies. You can also type a logical size to manually change the logical size of a table.
Logical table sizes are a significant part of how the MicroStrategy SQL
Engine determines the tables to use in a query.
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• Logical size locked
: Specifies whether the logical size of a table can be modified. Select the check box to set the value to True, which locks the table’s logical table size.
• Primary DB Instance
: The primary database instance of a table.
If your project supports mapping tables in a project to tables in multiple data sources, you can select Primary DB Instance and click the ...
(browse) button to open the Available Database Instances dialog box.
From this dialog box, you can view the table’s data sources. You can also change the database instance (which is associated with a data source) that is used as the primary database instance for the table. For information on adding tables from multiple data sources into your project with the
Warehouse Catalog or Architect, see
Accessing multiple data sources in a project, page 318
.
Modifying attributes in a table: Mapped Attributes section
When you select a table in Architect, the Mapped Attributes section of the
Properties pane displays the attributes that are mapped to columns in the table. From the Properties pane, you can select a column mapped to an attribute form and click the ... button to open the Modify Form Expression dialog box. From this dialog box, you can modify the attribute form expression.
For information on creating and modifying attribute forms in Architect, see
Creating and modifying attributes, page 151
. For information on attribute
forms and how to create and modify them from the Attribute Editor, see
Column data descriptions and identifiers: Attribute forms, page 244
Modifying facts in a table: Mapped Facts section
When you select a table in Architect, the Mapped Facts section of the
Properties pane displays the facts that are mapped to columns in the table.
From the Properties pane, you can select a column mapped to a fact and click
...
(the browse button) to open the Modify Fact Expression dialog box. From this dialog box, you can modify the fact expression.
For information on creating and modifying facts in Architect, see
Creating and modifying facts, page 138
. For information on facts and how to create
and modify them from the Fact Editor, see
Chapter 6, The Building Blocks of
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Modifying column names and data types in a table: Member Columns section
When you select a table in Architect, the Member Columns section of the
Properties pane displays the columns that are available in the table. From the
Properties pane, you can select a column and click the ... button to open the
Column Editor dialog box. From this dialog box, you can modify the column name and data type.
You can modify the column name and data type if this information has changed in the data source. This allows a MicroStrategy project to be able to locate a column after it has been renamed in the data source.
Modifying data warehouse connection and operation defaults
You can specify various settings for data warehouse connection and operation defaults using Architect. These settings are part of the Warehouse
Catalog options described in
Modifying data warehouse connection and operation defaults, page 304
The procedure below describes how to access a subset of the Warehouse
Catalog options from Architect.
Prerequisite
•
You are creating or modifying a project using Architect. For instructions, see
Creating and modifying projects, page 106
.
To modify data warehouse connection and operation defaults
1
With a project open in Architect, select the Project Tables View.
2
From the Warehouse Tables pane, right-click a data source and select
Warehouse Catalog Options
. The Warehouse Catalog options dialog box opens.
3
When accessed from Architect, only a subset of these Warehouse Catalog settings are displayed, including:
• Warehouse Connection
: These options allow you to modify the database instance and database login used to connect the data warehouse to a project. For information on these options, see
Data warehouse connection and read operations, page 305
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• Read Settings
: These options allow you to customize the SQL that reads the Warehouse Catalog for every platform except Microsoft
Access. For information on these options, see
Data warehouse connection and read operations, page 305
.
• Table Prefixes
: These options allow you to specify whether table prefixes are displayed in table names and how prefixes are automatically defined for tables that are added to the project. For
information on these options, see
Displaying table prefixes, row counts, and name spaces, page 309
4
Once you are finished defining Warehouse Catalog options, click OK to save your changes and return to Architect.
Organizing project tables: Layers
You can improve the organization and clarity of your project in Architect by creating groups of tables that can be easily accessed and focused on. These groups of tables are referred to as
layers
, and they can help organize
MicroStrategy projects that require a large number of tables.
In the Project Tables View of Architect, you can select one or more tables and define the group of tables as a layer. This layer can be accessed from the
Layers drop-down list to focus on only the tables included in the layer. Any modifications performed while viewing a layer are applied to the project as a whole.
For example, you can select all of the fact tables in your project and create a new layer named Fact Tables. This allows you to quickly focus on only the fact tables included in your project.
The All Project Tables layer is a default layer that includes all tables included in a project. This layer cannot be deleted.
The procedure below describes how to create a layer in Architect.
To create a layer in Architect
1
In MicroStrategy Developer, log in to a project.
2
From the Schema menu, select Architect.
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3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, select all the tables to include in a layer.
To remove a table from a layer, right-click the table, and select
Remove From Layer
.
5
From the Home tab, in the Layer area click the Create New Layer option. A dialog box to name the layer opens.
6
In the Please enter the layer name field, type a name for the layer and click OK. You are returned to Architect and the new layer is displayed in the Project Tables View.
7
Use the Layers drop-down list to switch between layers.
Creating and modifying facts
Facts are one of the essential elements within the business data model. They relate numeric data values from the data warehouse to the MicroStrategy reporting environment. Facts generally represent the answers to the business questions on which users want to report. For conceptual information on
Chapter 6, The Building Blocks of Business Data: Facts
.
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This section describes how to use Architect to create and modify facts, which includes:
•
•
Creating and modifying multiple facts, page 142
Before you create facts for your project, you can select the type of metrics that are created automatically when a fact is created for a project. This can reduce the time it takes to create the basic metrics for your project. For information on configuring Architect to
automatically create these basic metrics, see
Creating metrics based on the facts of a project, page 121
Creating facts
With Architect you can create facts as part of your initial project design effort as well as throughout the entire life cycle of a project.
To save the time it takes to create all the facts required for your project, you can allow Architect to automatically create facts when tables are added to your project. When tables are added to the project using Architect, facts are created for columns in tables that use numeric data types and are not used for attribute forms. To enable this automatic fact creation, see
Automating the creation of facts and attributes, page 113
.
The procedure below describes how to create a fact using Architect.
Prerequisites
•
The procedure below assumes you have already created a project object and added tables to the project. For information on creating a project
Creating projects using Architect, page 106
.
To create a fact using Architect
1
In MicroStrategy Developer, log in to a project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit
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Creating a Project Using Architect Project Design Guide mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select the table that includes a column or columns to use in a fact definition.
5
Right-click the table and select Create Fact. A dialog box opens to name the fact.
Rather than creating facts by manually creating a fact expression, you can allow Architect to automatically create simple facts defined on one column. To do this: a Right-click the table, point to Recognize, and then select Facts. The
Results Preview dialog box opens. b If facts are displayed, these facts can be created as described below:
– Facts can be created for columns in tables that use numeric data types and are not used for attribute forms, as described in
Automating the creation of facts and attributes, page 113
.
– Facts can be created by automatically mapping columns to facts already defined in your project, as described in
Automatically mapping columns to existing attribute forms and facts, page 116
Select the check box for a fact to create the fact. c Click OK. The facts you selected for creation are created within the table. If you use this option to create simple facts, you can then skip to
To define fact expressions and column aliases, page 142
6
Type a name for the fact, and click OK. The Create New Form Expression dialog box opens to create a fact expression.
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7
From the Available columns pane, drag and drop a column into the
Form expression pane.
You can include multiple columns as well as use numeric constants and mathematical operators and functions to create a fact expression. For
information on creating various types of fact expressions, see
Mapping physical columns to facts: Fact expressions, page 197
.
8
In the Mapping area, select Automatic or Manual:
• Automatic
mapping means that all of the tables in the project with the columns used in the fact expression are selected as possible source tables for the fact. You can then remove any tables mapped automatically and select other tables.
• Manual
mapping means that all of the tables in the project with the columns used in the fact expression are located but are not selected as possible source tables for the fact. You can then select which of those tables are used as source tables for the fact. Other scenarios in which you should use the manual mapping method include:
If you are creating a constant expression that is not based on a physical column in a project table, you must select the tables to apply the constant expression to.
If the same column name does not contain the same data across different tables, manually select the appropriate source tables for each fact.
For example, suppose you have a column named Sales, which exists in both the Fact_Sales table and the Fact_Discount table. In the
Fact_Sales table, the Sales column contains revenue data.
However, in the Fact_Discount table, the Sales column contains discount data. In other words, although the column name is the same in both tables (Sales), the columns contain different fact data in each table. When creating the Revenue fact, you must select the Manual mapping method so you can select the Fact_Sales table as a source table for the Revenue fact. When creating the Discount fact, you must select the Manual mapping method so you can select the
Fact_Discount table as a source table for the Discount fact. If you use the Automatic mapping method in both cases, the MicroStrategy
SQL Engine may use the incorrect column for the facts.
9
Click OK to close the Create New Form Expression dialog box and create the fact. The fact is displayed in the table used to create the fact.
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To define fact expressions and column aliases
10
You can continue to define the fact by right-clicking the fact and selecting
Edit
. The Fact Editor opens. Use the tabs of the Fact Editor to define fact expressions and create column aliases as described below:
• Definition
: This tab allows you to define fact expressions. Fact definitions are discussed in
How facts are defined, page 196
.
• Column Alias
: This tab allows you to create a column alias for the
fact. Column aliases are discussed in
Fact column names and data types: Column aliases, page 203
.
Note the following:
•
For detailed information about the options on each tab within the Fact Editor, refer to the MicroStrategy Developer Help
(formerly MicroStrategy Desktop Help).
•
You cannot create fact level extensions using Architect. For information on how to create fact level extensions, see
Modifying the levels at which facts are reported: Level extensions, page 206
11
When your changes are complete, click OK to return to Architect.
12
From the Home tab, in the Save area, click Save and Update Schema to save your changes.
Creating and modifying multiple facts
With Architect, you can create and modify multiple facts in your project quickly from an integrated interface. Architect allows you to create and modify facts in most of the same ways as the Fact Editor.
You cannot create fact level extensions using Architect. For
information on how to create fact level extensions, see
Modifying the levels at which facts are reported: Level extensions, page 206
.
For conceptual information on facts as well as detailed examples, see
Chapter 6, The Building Blocks of Business Data: Facts
. Refer to the list below for steps to perform various fact definitions using Architect:
•
Modifying facts with the Properties pane, page 143
•
Creating fact expressions, page 145
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•
Creating and modifying fact column names and data types: Column aliases, page 149
Modifying facts with the Properties pane
Once facts are created, you can modify and view fact definitions using the
Properties pane in Architect. To view the various properties of a fact in
Architect, from the Facts tab of the Properties pane, select the fact from the drop-down list. The Cost fact of the MicroStrategy Tutorial project shown below is used as an example of how you can modify and view facts using
Architect.
When selecting a fact in Architect, the Properties pane allows you to modify and view facts as described below.
You can select a property in the Properties pane to view a description of the property. The description is displayed at the bottom of the
Properties pane.
•
Defining and viewing attribute definitions: Definition section, page 157
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•
Modifying attribute forms: Forms sections, page 158
Project Design Guide
Defining and viewing fact definitions: Definition section
When you select a fact in Architect, the Definition section of the Properties pane displays the various properties for the fact. These properties and how to use them are described below:
• ID
: The identifier of the fact. You cannot modify this value.
• Name
: The name of the fact in the MicroStrategy project.
• Description
: The description of the fact. A description can help explain the purpose of a fact in a project.
• Hidden
: Specifies whether the fact is defined as hidden. Select the check box to set the value to True, which defines the table as hidden.
Objects that are hidden are not displayed to a user unless the user has changed his or her Developer Preferences and selected the Display
hidden objects
check box. Therefore, defining an object as hidden does not necessarily prevent users from viewing or accessing an object. The best way to prevent users from viewing or accessing an object is to restrict the user permissions for it.
• Location
: The location of the fact in a project.
Modifying fact expressions: Fact Expressions section
When you select a fact in Architect, the Fact Expressions section of the
Properties pane displays the tables that the fact is included in, as well as the fact expression used for the fact in that table.
In the Cost fact example shown in
Modifying facts with the Properties pane, page 143
, TOT_COST is displayed as the fact expression for most of the tables. However, the Cost fact uses a derived fact expression in the
ORDER_DETAIL table (derived fact expressions are described in
Creating derived facts and fact expressions, page 145
). The Cost fact also uses a different column name in the ORDER_FACT table (heterogeneous column
Creating facts with varying column names:
Heterogeneous column names, page 147
).
From the Properties pane, you can select a column mapped to a fact and click the ... (browse) button to open the Modify Fact Expression dialog box.
From this dialog box, you can modify the fact expression.
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Creating fact expressions
A fact expression represents a mapping to specific fact information in the data source. For conceptual information on fact expressions, see
Mapping physical columns to facts: Fact expressions, page 197
.
Fact expressions are commonly created by mapping a column to a fact, as described in
. With Architect, you can also create
and define facts as listed below:
•
Creating derived facts and fact expressions, page 145
•
Creating facts with varying column names: Heterogeneous column names, page 147
Creating derived facts and fact expressions
A derived fact has its value determined by an expression that contains more than just a column in a table. Any operation on a column such as adding a constant, adding another column’s values, or setting the expression to be an absolute value creates a derived fact. In other words, you are creating a fact from information that is available in the data warehouse.
For more information on derived facts and derived fact expressions, see
Derived facts and derived fact expressions, page 198
describes how to create a derived fact using Architect, and follows the
Example: creating derived facts, page 199
.
To create a derived fact using Architect
1
In MicroStrategy Developer, log in to a project.
For the example scenario, log in to the MicroStrategy Tutorial project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit
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Creating a Project Using Architect Project Design Guide mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select a table that includes a column or columns to use in a fact definition.
For the example scenario, select the ORDER_DETAIL table.
5
Right-click the table and select Create Fact. The Create New Form
Expression dialog box opens.
6
From the Available columns pane, drag and drop a column into the
Form expression pane.
For the example scenario, drag and drop the QTY_SOLD column to add it to the Form expression pane.
To complete the derived fact expression
A derived fact expression includes a combination of columns, numerical constants, and mathematical operators. The steps below continue the example scenario to provide a guideline of how to create derived fact expressions.
7
With the cursor in the Form expression pane, click * (the multiplication operator) to add it to the expression.
8
From the Available columns pane, double-click the UNIT_PRICE column to add it to the end of the fact expression.
9
Under Mapping method, select Automatic.
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10
Click Validate to check whether the syntax of the expression is correct.
The expression should appear as shown below:
11
Click OK. You are returned to Architect. The derived fact is given a default name and is displayed in the ORDER_DETAIL table.
To change the default name of the fact, right-click the fact in the table and select Rename. Type the new name in the dialog box that appears and click OK to save and return to Architect.
12
From the Home tab, in the Save area, click Save and Update Schema to save your changes.
Creating facts with varying column names: Heterogeneous column names
In your warehouse, the same fact can access columns with different column names. MicroStrategy allows you to identify heterogeneous fact column names for each fact. With heterogeneous column names, you can refer the same fact to multiple columns with different column names and from different tables that identify the same quantitative value.
For more information on heterogeneous column names, see
Facts with varying column names: Heterogeneous column names, page 201
procedure below describes how to use Architect to create a fact with heterogeneous column names, and follows the example scenario provided in
Example: Mapping heterogeneous fact columns, page 201
To create a fact with heterogeneous column names using Architect
1
In MicroStrategy Developer, log in to a project.
For the example scenario, log in to the MicroStrategy Tutorial project.
2
From the Schema menu, select Architect.
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3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select a table that includes a column or columns to use in a fact definition.
For the example scenario, select the ORDER_FACT table. This is one of the tables in which a heterogeneous fact column for the Units Sold fact exists.
5
Right-click the table and select Create Fact. The Create New Form
Expression dialog box opens.
6
From the Available columns pane, drag and drop a column into the
Form expression pane.
For the example scenario, drag and drop the QTY_SOLD column to add it to the Form expression pane.
7
In the Mapping method area, select Automatic.
8
Click OK. You are returned to Architect and the derived fact appears in the ORDER_FACT table.
9
Right-click the new fact and select Edit. The Fact Editor opens and the fact expression you just created appears in the Expressions pane.
To add additional columns for heterogeneous facts
Now you must add the other heterogeneous fact column as separate expression for the fact.
10
Click New. The Create New Fact Expression dialog box opens.
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11
From the Source table drop-down list, select the CITY_CTR_SALES table. This is the other table that contains a heterogeneous fact column for the Units Sold fact.
12
From the Available columns pane, double-click the TOT_UNIT_SALES column to add it to the Fact expression pane on the right.
13
In the Mapping method area, select Automatic.
14
Click OK. You are returned to the Fact Editor and the fact expression that you just created appears in the Expressions pane. Now the fact that you are creating maps correctly to its heterogeneous fact columns.
15
Click OK. You are returned to Architect. The fact is given a default name and is now displayed in the table.
To change the default name of the fact, right-click the fact in the table and select Rename. Type the new name in the dialog box that appears and click OK to save and return to Architect.
16
From the Home tab, in the Save area, click Save and Update Schema to save your changes.
Creating and modifying fact column names and data types:
Column aliases
A column alias specifies both the name of the column to be used in temporary tables and the data type to be used for the fact. By default, the data type for a fact is inherited from the data type of the column on which the fact is defined in the data warehouse.
For information on column aliases, see
Fact column names and data types:
. The procedure below describes how to create a column alias using Architect.
Prerequisites
•
This procedure assumes you have already created a fact with a valid fact expression.
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To create a column alias for a fact using Architect
1
In MicroStrategy Developer, log in to the project source that contains the fact to create a new column alias for.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
Right-click the fact and select Edit. The Fact Editor opens.
5
Select the Column Alias tab.
6
In the Column alias area, click Select. The Column Editor - Column
Selection dialog box opens.
7
Select New to create a new column alias. The Column Editor - Definition dialog box opens.
8
You can modify the following properties for the column alias:
• Column name
: The name for the column alias. This name is used in any SQL statements which include the fact column.
• Data type
: The data type for the fact. For a description of the different data types supported by MicroStrategy, see
•
Depending on the data type selected, you can specify the byte length, bit length, precision, scale, or time scale for your column alias. For a detailed description on each of these properties, see the
MicroStrategy Developer Help (formerly the MicroStrategy Desktop
Help).
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9
Click OK to save your changes and return to the Column Editor - Column
Selection dialog box.
10
Click OK. You are returned to the Fact Editor.
11
Click OK. You are returned to Architect.
12
From the Home tab, in the Save area, click Save and Update Schema to save your changes.
Creating and modifying attributes
Business data represented by facts can offer little insight without the presence of business concepts and context, which take the form of attributes in MicroStrategy. Attributes provide the business model with a context in which to report on and analyze facts. While knowing your company’s total sales is useful, knowing where and when the sales took place provides the kind of analytical depth that users require on a daily basis. For conceptual information on attributes, see
Chapter 7, The Context of Your Business
.
This section describes how to use Architect to create and modify attributes, which includes:
•
•
Creating and modifying multiple attributes, page 155
These sections focus on creating attributes and attribute forms. To create and define attribute relationships with the Hierarchy View in Architect, see
Defining attribute relationships, page 174
.
Creating attributes
With Architect you can create attributes as part of your initial project design effort as well as throughout the entire life cycle of a project.
To save the time that it takes to create all the attributes required for your project, you can allow Architect to automatically create attributes when tables are added to your project. When tables are added to the project using
Architect, attributes are created based on the automatic column recognition rules that you define in Architect. To enable and define this automatic
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Automating the creation of facts and attributes, page 113
The procedure below describes how to create an attribute using Architect.
Prerequisites
•
The procedure below assumes you have already created a project object and added tables to the project. For information on creating a project
Creating projects using Architect, page 106
.
To create an attribute using Architect
1
In MicroStrategy Developer, log in to a project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select a table that includes a column or columns to use in an attribute definition.
5
Right-click the table and select Create Attribute. The Create New Form
Expression dialog box appears.
Rather than creating attributes by manually creating an attribute expression, you can allow Architect to automatically create simple attributes defined on one column. To do this: a Right-click the table, point to Recognize, and then select Attributes.
The Results Preview dialog box opens.
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– Attribute forms can be created based on these automatic column recognition rules that you define, as described in
Automating the creation of facts and attributes, page 113
– Attribute forms can be created by automatically mapping columns to attribute forms already defined in your project, as described in
Automatically mapping columns to existing attribute forms and facts, page 116
Select the check box for an attribute form to create the attribute form.
If more than one attribute form is available for creation for an attribute, you must select the ID form. Any other attribute forms for that attribute are optional. For example, to create an attribute description form you must select the description form along with the
ID form for the attribute. c Click OK. The attribute forms you selected for creation are created within the table. If you use this option to create simple attributes, you can then skip to
To define attribute lookup tables, form expressions, and column aliases, page 154
6
Create a form expression for the ID form of the new attribute being created, as described below:
•
To create a simple attribute form expression (
Attribute form expressions, page 248
), drag a column from the Available columns pane to the Form expression pane.
•
To create a more advanced attribute form expression, use a combination of any of the following techniques:
– Enter constants in double quotes.
– To create a function using the Insert Function Wizard, click f(x) in the Form expression toolbar.
– To insert an operator into the expression, click any operator in the
Form expression toolbar.
7
Click Validate to ensure that your expression is valid.
8
Under Mapping method, select Automatic or Manual:
• Automatic
mapping means that all of the tables in the project with the columns used in the attribute form expression are selected as
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• Manual
mapping means that all of the tables in the project with the columns used in the attribute form expression are located but are not selected as possible source tables for the attribute form. You can then select which of those tables are used as source tables for the attribute form.
Note the following:
•
The mapping method defaults to Automatic for the first attribute or attribute form expression you create. The system maps the expression to each of the source tables. For subsequent attributes, the default is Manual.
•
An expression that uses only a constant value cannot use the automatic mapping method.
To use an attribute for meaningful analysis, you must verify that it includes a column from a fact table in its definition, or is related to an attribute that does. For example, to use the Month attribute for analysis, you must create a relationship to the Day attribute, which must include the DAY column from the fact table in its definition.
To create relationships between attributes, see
Defining attribute relationships, page 174
9
Click OK to close the Create New Form Expression dialog box and create the attribute. The attribute is given a default name and is displayed in the table.
To change the default name of the attribute, right-click the attribute in the table and select Rename. Type the new name in the dialog box that appears and click OK to save and return to
Architect.
To define attribute lookup tables, form expressions, and column aliases
10
You can continue to define the attribute by right-clicking the ID form for the attribute and selecting Edit. The Modify Attribute Form dialog box opens.
11
From the Source tables pane, select a table and click Set as Lookup to set it as the lookup table for the attribute. A lookup table acts as the main table which holds the information for an attribute. If you chose manual mapping, select the check boxes of the tables to map to the attribute form.
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12
You can use the tabs of the Modify Attribute Form dialog box to define attribute form expressions and create column aliases as described below:
• Definition
: This tab allows you to define attribute form expressions.
Attribute forms are discussed in
Column data descriptions and identifiers: Attribute forms, page 244
.
• Column Alias
: This tab allows you to create a column alias for the fact. Column aliases are discussed in
Modifying attribute data types:
.
For detailed information about the options on each tab within the
Modify Attribute Form dialog box, refer to the MicroStrategy
Developer Help (formerly MicroStrategy Desktop Help).
13
When your changes are complete, click OK to return to Architect.
14
From the Home tab, in the Save area, click Save and Update Schema to save your changes.
Creating and modifying multiple attributes
With Architect, you can create and modify multiple attributes in your project quickly from an integrated interface. Architect allows you to create and modify attributes in most of the same ways as the Attribute Editor.
For conceptual information on attributes as well as detailed examples, see
Chapter 7, The Context of Your Business Data: Attributes
below for steps to perform various attribute definitions using Architect:
•
Modifying attributes with the Properties pane, page 156
•
Creating attribute form expressions, page 160
•
Creating and modifying attribute data types: Column aliases, page 164
•
Creating attributes with multiple ID columns: Compound attributes, page 166
•
Modifying how to use attributes to browse and report on data, page 168
•
Specifying attribute roles: Attributes that use the same lookup, page 170
•
Supporting data internationalization for attribute elements, page 172
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Modifying attributes with the Properties pane
Once attributes are created, you can modify and view attribute definitions using the Properties pane in Architect. To view the various properties of an attribute in Architect, from the Attributes tab of the Properties pane, select the attribute from the drop-down list. The Category attribute of the
MicroStrategy Tutorial project shown below is used as an example of how you can modify and view attributes using Architect.
When selecting an attribute in Architect, the Properties pane allows you to modify and view attributes as described below.
You can select a property in the Properties pane to view a description of the property. The description is displayed at the bottom of the
Properties pane.
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•
Defining and viewing attribute definitions: Definition section, page 157
•
Modifying attribute forms: Forms sections, page 158
Defining and viewing attribute definitions: Definition section
When you select an attribute in Architect, the Definition section of the
Properties pane displays the various properties for the attribute. These properties and how to use them are described below:
• ID
: The identifier of the attribute. You cannot modify this value.
• Name
: The name of the attribute in a MicroStrategy project.
• Description
: The description of the attribute. A description can help explain the purpose of an attribute in a project.
• Hidden
: Specifies whether the attribute is defined as hidden. Select the check box to set the value to True, which defines the table as hidden.
Objects that are hidden are not displayed to a user unless the user has changed his or her Developer Preferences and selected the Display hidden objects check box. Therefore, defining an object as hidden does not necessarily prevent users from viewing or accessing an object. The best way to prevent users from viewing or accessing an object is to restrict the user permissions for it.
• Location
: The location of the attribute in a project.
• Lock Type
: Specifies how you can browse attribute elements within the
System Hierarchy in the Data Explorer. You have the following options:
Locked
: No elements of the attribute are shown within the System
Hierarchy in the Data Explorer. For example, if the attribute Year is locked, no elements for Year display in the Data Explorer when Year is expanded from the System Hierarchy.
Unlocked
: All elements of the attribute are shown within the System
Hierarchy in the Data Explorer. For example, if the attribute Year is unlocked, all elements of Year (such as 2005, 2006, and 2007) display in the Data Explorer when Year is expanded from the System
Hierarchy.
Limit
: Incrementally retrieves the number of elements set for the attribute. For example, if the limit for the attribute Year is set to one, the years 2005, 2006, and 2007 are retrieved one-by-one as they are requested.
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• Lock Limit
: If you choose the Limit lock type above, you can define the number of elements to incrementally retrieve and display within the
System Hierarchy in the Data Explorer.
• Apply Security Filters
: Enables and disables the use of security filters in element requests. This setting also applies to the use of security filters for creating an element cache.
This setting covers situations where only certain attributes need the security filters for element requests. For example, if you have an external-facing data warehouse for your suppliers, security filters can be used on attributes in the product dimension so one supplier cannot see another supplier's products. However, since security is not necessary on attributes in the Time dimension, security filters do not need to be applied and the element cache can be shared.
• Enable Element Caching
: Enables and disables element caching at the attribute level. By caching the elements of an attribute, the elements are returned quickly from a cache when browsing the attribute elements.
This is particularly helpful for attributes that rarely or never have a modification to the elements available for the attribute. The volatility of the elements within different attributes can fluctuate greatly. For example, the Order Number attribute may have elements that change once a day (depending on the warehouse load), while the Product
Number attribute may only have elements that change once a week or once a month.
Modifying attribute forms: Forms sections
When you select an attribute in Architect, the Forms sections of the
Properties pane display information about the attribute forms for the attribute. The properties for attribute forms and how to use them are described below:
•
Attribute form category
: The first property for an attribute form reflects the category used for the attribute form. In the example shown above in
Modifying attributes with the Properties pane, page 156
, ID is displayed as the first property. You can select the attribute form property and click the ... button to modify the attribute form.
If the attribute form uses a form group to combine multiple attribute forms, you can modify the separate attribute forms that are included in the form group. For information on creating form groups in Architect, see
Creating attributes with multiple ID columns: Compound attributes, page 166
.
• Name
: The name of the attribute form in a MicroStrategy project.
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• Geographical Role
: Defines how the attribute form can be used as geographical data with various MicroStrategy mapping features. When using Data Import, this type of geographical information can be automatically detected. For details on how Data Import is able to assign geographical roles to your data, see
Integrating geographical information with your imported data, page 74
.
• Shape File
: Defines the shapes used to display the attribute form on various MicroStrategy mapping features. For details on using shape files to define the display of attribute forms as part of the Image Layout widget, refer to the Dashboards and Widgets Creation Guide.
• Category
: The category used for the attribute form, which can help group or identify attribute forms. From the drop-down list, select the category to use for the attribute form. For information on how the category helps group attribute forms, see
Attribute form expressions, page 248
• Format
: The format of the attribute form, which controls how the form is displayed and how filters are defined. From the drop-down list, select the format to use for the attribute form. For information on the format of attribute forms, see
Attribute form expressions, page 248
• Report Sort
: Defines the default sort order of the attribute form when it is included in a report. From the drop-down list, you can choose from
Ascending
, Descending, or None. For information on how attribute forms are sorted when multiple attribute forms of a single attribute
define a default sort order, see
Default sorting of multiple attribute forms on reports, page 247
.
• Browse Sort
: Defines the default sort order of the attribute form when it is viewed in the Data Explorer. From the drop-down list, you can choose from Ascending, Descending, or None. The Data Explorer is discussed in
Enabling hierarchy browsing in reports: Data Explorer, page 364
.
• Use as Browse Form
: Defines whether the attribute form can be displayed in the Data Explorer. To allow an attribute form to be displayed in the Data Explorer, select the check box to set the value to True. The
Data Explorer is discussed in
Enabling hierarchy browsing in reports:
.
• Use as Report Form
: Defines whether the attribute form is displayed on reports by default. To define an attribute form to be displayed on reports by default, select the check box to set the value to True.
• Supports Multiple Languages
: Defines whether the attribute form’s information can be displayed in multiple languages using data
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Enabling data internationalization for an attribute form is described in
Supporting data internationalization for attribute elements, page 172
The ID form of an attribute does not have this option as these forms are used strictly for identification purposes.
• Column Alias
: The column alias of the attribute form, which allows you to define a new data type that you can use in place of the default data type for a given attribute form. You can select the Column Alias property and click the ... button to modify the attribute form’s column alias. For information on column aliases for attribute forms, see
Modifying attribute data types: Column aliases, page 258
• Attribute Expressions
: The expressions used for the attribute form. You can select an attribute expression and click the ... button to modify the attribute form expression.
Creating attribute form expressions
An attribute expression represents a mapping to specific attribute information in the data source. For conceptual information on attribute
forms and attribute form expressions, see
Column data descriptions and identifiers: Attribute forms, page 244
Attribute form expressions, page 248
.
Attribute form expressions are commonly created by mapping a column to an attribute form, as described in
. With
Architect, you can also create and define attributes as listed below:
•
Creating derived attribute form expressions, page 160
•
Joining dissimilar column names: Heterogeneous mappings, page 162
Creating derived attribute form expressions
Derived expressions are created using a combination of warehouse columns, mathematical operators, functions, and constants. While simple expressions have their value determined by just one column in a warehouse table, derived expressions are defined using one or more columns as well as other operators and values. Any operation on a column (such as adding a constant, adding another column, or setting the expression to be an absolute value) creates a derived expression.
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For information on derived attribute form expressions, see
. The procedure below describes how to create a derived attribute form expression using Architect, and follows the example scenario provided in
Example: creating an attribute form with a derived expression, page 251
To create an attribute form with a derived expression using Architect
1
In MicroStrategy Developer, log in to a project.
For the example scenario, log in to the MicroStrategy Tutorial project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select a table that includes a column or columns to use in an attribute definition.
For the example scenario, select the LU_CUSTOMER table.
5
Right-click the Customer attribute and select New Attribute form. The
Create New Form Expression dialog box opens.
6
Double-click the CUST_LAST_NAME column to add it to the Form expression pane on the right.
7
In the Form expression pane, place the cursor to the right of
[CUST_LAST_NAME] and type + “, “ +.
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Double-click the CUST_FIRST_NAME column to add it to the Form expression pane on the right. Your expression should be defined as shown below.
9
Select Manual as the mapping method.
10
Click OK to return to Architect. The new attribute form is displayed as part of the Customer attribute in the LU_CUSTOMER table.
11
In the Properties pane, locate the new attribute form.
12
In the Name field, type Last Name, First Name.
13
From the Category drop-down list, select None since Last Name, First
Name is neither the ID form of Customer nor the primary description form.
14
Because this is only an example, you can close Architect without saving your changes.
Joining dissimilar column names: Heterogeneous mappings
Heterogeneous mapping allows Intelligence Server to perform joins on dissimilar column names. If you define more than one expression for a given form, heterogeneous mapping automatically occurs when tables and column names require it.
For information on heterogeneous mappings for attributes, see
Joining dissimilar column names: Heterogeneous mappings, page 254
. The procedure below describes how to create an attribute form with a heterogeneous column mapping using Architect, and follows the example scenario provided in
Joining dissimilar column names: Heterogeneous mappings, page 254
.
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To create an attribute form with a heterogeneous column mapping using
Architect
1
In MicroStrategy Developer, log in to a project.
For the example scenario, log in to the MicroStrategy Tutorial project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select a table that includes a column or columns to use in an attribute definition.
For the example scenario, select the LU_DAY table.
5
Right-click the Customer attribute and select New Attribute form. The
Create New Form Expression dialog box opens.
6
Double-click the DAY_DATE column to add it to the Form expression pane on the right.
7
Select Automatic as the mapping method.
8
Click OK to return to Architect. The new attribute form is displayed as part of the Day attribute in the LU_DAY table.
9
Right-click the new attribute form and select Edit. The Modify Attribute
Form dialog box opens.
10
Click New. The Create New Form Expression dialog box opens.
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11
From the Source table drop-down list, select the ORDER_DETAIL table.
12
Double-click the ORDER_DATE column to add it to the Form expression pane on the right.
13
Select Automatic as the mapping method.
14
Click OK. The Create New Attribute Form dialog box opens.
Notice that there are now two expressions for the attribute form definition, both of which use different tables as the source of their information. You can continue this process to add as many heterogeneous columns as part of one attribute form as necessary.
15
Click OK to return to Architect.
16
In the Properties pane, locate the new attribute form.
17
In the Name field, type Date Example.
18
From the Category drop-down list, select None since this is an example scenario.
19
Because this is only an example, you can close Architect without saving your changes.
Creating and modifying attribute data types: Column aliases
A column alias is a new data type that you can specify in place of the default data type for a given attribute form. Column aliases allow you to specify a more appropriate data type that can help avoid errors in your SQL. They can also help you take more advantage of the data in your data warehouse.
For information on column aliases for attributes, see
Modifying attribute data types: Column aliases, page 258
. The procedure below describes how to create a column alias using Architect, and follows the example scenario provided in
Modifying attribute data types: Column aliases, page 258
.
Prerequisites
This procedure assumes you have already created an attribute with a valid attribute expression for which to create a new column alias.
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To create a column alias for an attribute using Architect
1
In MicroStrategy Developer, log in to a project.
For the example scenario, log in to the MicroStrategy Tutorial project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select a table that includes an attribute to create a column alias for.
5
Right-click the attribute form to create a column alias for, and select Edit.
The Modify Attribute Form dialog box opens.
6
Select the Column Alias tab.
7
In the Column alias area, click Select. The Column Editor - Column
Selection dialog box opens.
8
Select New to create a new column alias. The Column Editor - Definition dialog box opens.
9
You can modify the following properties for the column alias:
• Column name
: The name for the column alias. This name is used in any SQL statements which include the fact column.
• Data type
: The data type for the fact. For a description of the different data types supported by MicroStrategy, see
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•
Depending on the data type selected, you can specify the byte length, bit length, precision, scale, or time scale for your column alias. For a detailed description on each of these properties, see the
MicroStrategy Developer Help (formerly MicroStrategy Desktop
Help).
10
Click OK to save your changes and return to the Column Editor - Column
Selection dialog box.
11
Click OK to save your changes and return to the Modify Attribute Form dialog box.
12
Click OK to return to Architect.
13
From the Home tab, in the Save area, click Save and Update Schema to save your changes.
Creating attributes with multiple ID columns: Compound attributes
A compound attribute is an attribute with multiple columns specified as the
ID column. This implies that more than one ID column is needed to uniquely identify the elements of that attribute. Generally, you build a compound attribute when your logical data model reflects that a compound key relationship is present. In the relational database, a compound key is a primary key that consists of more than one database column.
For information on compound attributes, see
Attributes with multiple ID columns: Compound attributes, page 285
. The procedure below describes how to create a compound attribute using Architect, and follows the example scenario provided in
Example: Creating compound attributes, page 286
.
To create a compound attribute using Architect
1
In MicroStrategy Developer, log in to a project.
For the example scenario, log in to the MicroStrategy Tutorial project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit
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Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select a table that includes the columns to use for a compound attribute.
For the example scenario, select the LU_DIST_CTR table.
5
Right-click the table and select Create Attribute. The Create New Form
Expression dialog box opens.
6
Double-click the COUNTRY_ID column to add it to the Form expression pane on the right.
7
Select Automatic mapping method.
8
Click OK to return to Architect. The new attribute is displayed in the
LU_DIST_CTR table.
To rename the attribute, right-click the attribute and select
Rename
.
9
In the Properties pane, locate the new attribute form.
10
In the Name field, type ID 1.
11
Right-click the attribute and click New Attribute form to create the other attribute ID form. The Create New Form Expression dialog box opens.
12
Double-click the DIST_CTR_ID column to add it to the Form expression pane on the right.
13
Select Automatic mapping method.
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14
Click OK to return to Architect. The new attribute form is displayed in the
LU_DIST_CTR table.
You can change the default name of the fact by right-clicking it in the table and selecting Rename.
15
In the Properties pane, locate the new attribute form.
16
In the Name field, type ID 2.
17
In the Category drop-down list, select ID. A message about creating a form group is displayed.
You can also create a form group by dragging and dropping one attribute form onto another attribute form for the same attribute.
18
Click Yes to create a form group. The two attribute forms are included in a form group.
For more information on using form groups to create compound
Attributes with multiple ID columns: Compound attributes, page 285
.
19
In the first Name field for the attribute form, type ID.
20
Because this is only an example, you can close Architect without saving your changes.
Modifying how to use attributes to browse and report on data
Once attributes are built, they are used in two primary ways—browsing and reporting. Users browse through attributes to locate an attribute to use on a report, and users place an attribute on a report to display details about the particular attribute and how it relates to fact data. Each attribute can be displayed in a variety of forms so you must specify the default display of each of the attributes in the project. You can do this on a report-by-report basis, but you still must specify the global, or project-wide, default for each attribute.
For information on modifying the attribute forms used for reporting and browsing, see
Using attributes to browse and report on data, page 288
procedure below describes how to define attribute form display using
Architect. The steps below follow the example scenario provided in
Defining how attribute forms are displayed by default, page 289
.
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To display an attribute form in reports and in the Data Explorer using
Architect
1
In MicroStrategy Developer, log in to a project.
For the example scenario, log in to the MicroStrategy Tutorial project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select a table that includes the attribute to define how its attribute forms are displayed by default.
For the example scenario, select the LU_DIST_CTR table, which includes the attribute Distribution Center.
5
Select an attribute.
For the example scenario, select the Distribution Center attribute.
6
In the Properties pane, locate the attribute form.
For the example scenario, locate the ID 2 attribute form.
7
You can define the following display options:
• Report Sort
: Defines the default sort order of the attribute form when it is included in a report. From the drop-down list, you can choose from Ascending, Descending, or None. For information on how attribute forms are sorted when multiple attribute forms of a single attribute define a default sort order, see
Default sorting of multiple attribute forms on reports, page 247
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• Browse Sort
: Defines the default sort order of the attribute form when it is viewed in the Data Explorer. From the drop-down list, you can choose from Ascending, Descending, or None. The Data
Explorer is discussed in
Enabling hierarchy browsing in reports:
.
• Use as Browse Form
: Defines whether the attribute form can be displayed in the Data Explorer. To allow an attribute form to be displayed in the Data Explorer, select the check box to set the value to
True
. The Data Explorer is discussed in
Enabling hierarchy browsing in reports: Data Explorer, page 364
.
• Use as Report Form
: Defines whether the attribute form is displayed on reports by default. To define an attribute form to be displayed on reports by default, select the check box to set the value to True.
8
You can also define the default sort order for attributes on reports and the
Data Explorer. For information on attribute form sorting options, see
Displaying forms: Attribute form properties, page 246
9
Because this is only an example, you can close Architect without saving your changes.
Specifying attribute roles: Attributes that use the same lookup
Attribute roles allow you to use the same data to define and support two separate attributes. If you identify that one of your attributes needs to play multiple roles, you must create an attribute in the logical model for each of the roles. This ensures that a report with attributes playing multiple roles returns correct data.
For information on attribute roles, see
Attributes that use the same lookup table: Attribute roles, page 276
. The procedure below describes how to
specify attribute roles using explicit table aliasing. The steps below follow the
Explicitly aliasing tables to specify attribute roles, page 282
You can also define attribute roles using automatic role recognition, which utilizes MicroStrategy VLDB properties and is described in
Using automatic attribute role recognition, page 280
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To create attribute roles with explicit table aliasing using Architect
This procedure provides steps to re-create the example of explicit table
Explicitly aliasing tables to specify attribute roles, page 282
. The LU_STATE table is not included in the MicroStrategy Tutorial project. However, you can use the same high-level procedure and concepts as guidelines to create attribute roles in your project setup.
1
In MicroStrategy Developer, log in to the project to create attribute roles with explicit table aliasing.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate and select the LU_STATE table that includes the attribute to define attribute roles for.
5
Right-click the LU_STATE table and select Create Table Alias. An
LU_STATE(1) table is created.
6
Right-click LU_STATE(1), select Rename, and type LU_STATE_STORE.
7
Right-click the LU_STATE table and select Create Table Alias. An
LU_STATE(1) table is created.
8
Right-click LU_STATE(1), select Rename, and type
LU_STATE_VENDOR
.
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Create the attributes
9
Right-click the LU_STATE_STORE table and select Create Attribute.
The Create New Form Expression dialog box opens.
10
In the Available columns pane, double-click STATE_ID, which identifies the attribute role.
11
Select Manual mapping and click OK. You are returned to Architect and the new attribute is created in the LU_STATE_STORE table.
12
Right-click the new attribute, select Rename, and type State Store.
13
Right-click the State Store attribute table and select New Attribute
form
. The Create New Form Expression dialog box opens.
14
Map any other columns to attribute forms for the State Store attribute.
You must make sure to map any State Store attribute forms to columns from the LU_STATE_STORE table.
15
Click OK and save the State Store attribute.
16
Create a Vendor State attribute with the same sub-procedure (
Create the attributes, page 172
) used to create State Store above, except you must
use the LU_STATE_VENDOR table instead of the LU_STATE_STORE table.
Supporting data internationalization for attribute elements
MicroStrategy supports the internationalization of your data into the languages required for your users. This allows attribute element data to be displayed in various languages that reflect the user’s language preferences.
For information on enabling data internationalization for attribute elements
and an example of its benefits, see
Supporting data internationalization for attribute elements, page 241
. The procedure below describes how to enable data internationalization for attribute elements using Architect.
Prerequisites
•
The internationalized data must be stored in your data source, as described in
Supporting data internationalization, page 56
•
The project must enable data internationalization, as described in
Enabling data internationalization for a project, page 94
.
•
An attribute has been created.
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To enable or disable data internationalization for attribute forms using
Architect
1
In Developer, log in to a project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Project Tables View, locate an attribute.
5
From the Properties pane, locate an attribute form.
6
Select the Support multiple languages check box to set the value to
True
, which enables data internationalization for the attribute form. You can clear the check box and set it to False, which disables internationalization for the attribute form.
The ID form of an attribute does not have this option as these forms are used strictly for identification purposes.
7
From the Home tab, in the Save area, click Save and Close to save your changes and close Architect.
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Defining attribute relationships
After you have created attributes for your project, you can define attribute relationships to determine how the engine generates SQL, how tables and columns are joined and used, and which tables are related to other tables.
You link directly related attributes to each other by defining parent-child relationships. Attribute elements, or the actual data values for an attribute, dictate the relationships that you define between attributes.
The parent-child relationships that you create determine the system hierarchy within the project.
The four types of direct relationships that can exist between attributes include one-to-one, one-to-many, many-to-one, and many-to-many. For information on these direct relationships and steps to define them with the
Attribute Editor, see
Attribute relationships, page 262
You can also use Architect to define relationships between attributes.
Architect can provide a more intuitive and helpful workflow that allows you to view and modify multiple attributes as you define attribute relationships.
For example, in the MicroStrategy Tutorial project, the Time hierarchy includes relationships between the attributes Year, Quarter, Month of Year,
Month, and Day. With Architect, rather than defining parent and child relationships for one attribute at a time, you can define the relationships between these attributes at the same time in a visual environment.
The steps below show you how to manually define parent and child relationships between attributes, and also provides an example scenario of creating the relationships between the attributes Year, Quarter, Month of
Year, Month, and Day in the MicroStrategy Tutorial project.
You can also allow Architect to automatically define attribute relationships
based on the design of your data, as described in
Automatically defining attribute relationships, page 178
.
Prerequisite
•
Attributes have been created for your project.
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To define attribute relationships
1
In MicroStrategy Developer, log in to a project.
For the example scenario, log in to the MicroStrategy Tutorial project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Hierarchy View, on the Home tab, in the drop-down list on the
Hierarchy
area of the toolbar, select System Hierarchy View. The system hierarchy is displayed.
5
Prior to defining any relationships, you should gather the attributes that you want to relate to each other in the same area within the Hierarchy
View of Architect. For example, the attributes Year, Quarter, Month of
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Year, Month, and Day in the MicroStrategy Tutorial project are gathered close together in the Hierarchy View, as shown below.
To create an attribute relationship
6
Select an attribute that is to be a parent attribute in an attribute relationship. Drag from the middle of the attribute to an attribute that is to be a child of the parent attribute selected. A one-to-many relationship line is created between the two attributes.
For example, in the image below a relationship is created between the
Year and Quarter attributes in which Year is a parent attribute of Quarter.
In the example above, the Year attribute is the parent attribute of
Quarter.
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7
Attribute relationships created in this way are created as one-to-many relationships by default. To modify the relationship type, right-click the relationship line and select from one of the relationship types listed below:
• One-to-one
• One-to-many
• Many-to-one
• Many-to-many
For information on these relationship types, see
Attribute relationships, page 262
8
A table in which both attributes exist is chosen as the table to support the attribute relationship. To modify the relationship table, right-click the relationship line, point to Relationship table, and select a table.
To define attribute relationships with other techniques
If you are finished defining attribute relationships, you can save your changes and close Architect. The rest of this procedure describes how to define attribute relationships with other techniques and completes the example scenario.
9
Right-click the Quarter attribute, and select Edit Children Relations.
The Children Relations dialog box opens and lists attributes that can be related to the Quarter attribute.
10
For the Month attribute, in the Relationship type drop-down list, select
One-to-many
. You can select any of the available relationship types from the Relationship type drop-down list to create the required relationship.
11
For the Month attribute, in the Relationship table drop-down list, keep the default of LU_MONTH.
12
Keep the Relationship type drop-down list at None for the other attributes listed. Quarter is not directly related to any of these attributes.
13
Click OK to close the Children Relations dialog box and create the attribute relationship between Quarter and Month.
14
Drag from the middle of the Month of Year attribute to the Month attribute. A one-to-many relationship line is drawn between the two attributes.
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Drag from the middle of the Month attribute to the Day attribute. A one-to-many relationship line is drawn between the two attributes.
This completes the definition of the required attribute relationships, as shown below.
Automatically defining attribute relationships
In addition to manually defining attribute relationships (see
Defining attribute relationships, page 174
) you can also allow Architect to
automatically define attribute relationships based on the design of your data in your data source.
The steps below show you how to automatically define attribute relationships based on the design of your data in your data source.
Prerequisite
•
You have created attributes for your project (see
Creating and modifying attributes, page 151
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To automatically define attribute relationships
1
In MicroStrategy Developer, log in to a project.
2
From the Schema menu, select Architect.
3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Hierarchy View, on the Home tab, in the drop-down list on the
Hierarchy
area of the toolbar, select System Hierarchy View. The system hierarchy is displayed.
5
Right-click within the area that displays the attributes for your project, and select Recognize Relationships. The System Hierarchy dialog box opens.
6
You can select from the following options to automatically define attribute relationships:
• Based on Primary Keys/Foreign Keys
: Creates attribute relationships based on the primary keys and foreign keys defined on your tables. Each attribute that acts as a foreign key of a table is defined as a parent attribute of each attribute that acts as a primary key of the same table. The attribute relationship is defined as a one-to-many relationship from the foreign key attribute (parent attribute) to the primary key attribute (child attribute).
• Based on lookup tables
: Creates attribute relationships based on lookup tables that do not include primary key or foreign key information. To define a table as a lookup table for an attribute, see
. Each attribute that defines a table as its lookup table is defined as a child attribute of all other attributes in the
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Creating a Project Using Architect Project Design Guide same table, that do not define the table as its lookup table. Each attribute relationship is defined as a one-to-many relationship from the parent attribute to the child attribute.
• Based on sample data from the table
: Creates attribute relationships for attributes that share the same lookup table. To define a table as a lookup table for an attribute, see
Architect analyzes sample data for the table. The attributes with fewer distinct values are defined as parents of the attributes with more distinct values, using a one-to-many relationship from the parent attribute to the child attribute. For example, a lookup table includes four rows of data, which include data related to year and quarter. Each row includes the same year (for example, 2009), but the quarter changes for each row (Q1, Q2, Q3, Q4). In this case, the Year attribute is created as a parent of the Quarter attribute.
7
Once you have selected the appropriate options, click OK.
•
If you selected the Show preview results check box to preview the results of automatically defining attribute relationships (see
Automatically defining attribute relationships, page 122
Preview dialog box is displayed. From this dialog box, you can select which attribute relationships should be created and which attribute relationships should be excluded. After you have made all your selections, click OK to allow Architect to automatically define attribute relationships.
•
If you cleared the Show preview results check box (see
Automatically defining attribute relationships, page 122
creates all potential attribute relationships without first displaying them in the Results Preview dialog box.
After all relationships are determined by the rules that you selected,
Architect performs final analysis on the attribute relationships that are to be created. Any attribute relationships that are found to be redundant are not created. This ensures that attribute relationships are created that properly reflect the design of the data in your data source. For information on modifying the attribute relationships that are created, see
Defining attribute relationships, page 174
.
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Creating and modifying user hierarchies
User hierarchies provide flexibility in element browsing and report drilling.
They are groups of attributes and their relationships to each other, arranged in ways that make sense to a business organization. As the structure of your business intelligence system evolves, you can modify the design of a user hierarchy to include additional attributes or to limit user access to certain attributes.
For conceptual information on user hierarchies, see
Hierarchies to Organize and Browse Attributes
This section describes how to use Architect to create and modify user hierarchies.
Creating user hierarchies
With Architect you can create hierarchies as part of your initial project design effort as well as throughout the entire life cycle of a project.
This section discusses creating user hierarchies to support browsing and drilling on an attribute. The system hierarchy is created according to the relationships defined between the attributes in your project. It is automatically created based on the attribute relationships you define, as described in
Defining attribute relationships, page 174
.
The following procedure describes how to create a user hierarchy using
Architect.
Prerequisites
•
The procedure below assumes you have already created a project object and added tables to the project. For information on creating a project
Creating projects using Architect, page 106
.
To create a user hierarchy using Architect
1
In MicroStrategy Developer, log in to a project.
2
From the Schema menu, select Architect.
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3
If a message is displayed asking if you want to open Architect in read only mode or edit mode, select Edit and click OK to open Architect in edit mode so that you can make changes to the project. MicroStrategy
Architect opens.
Note the following:
•
If you are only given the option of opening Architect in read only mode, this means another user is modifying the project’s schema. You cannot open Architect in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
From the Hierarchy View, select an attribute to include in the hierarchy, and then click the New Hierarchy toolbar option. A dialog box to name the hierarchy opens.
5
In the Please enter the hierarchy name field, type a name for the hierarchy and click OK. You are returned to Hierarchy View with the attribute you selected included in the hierarchy.
6
To add additional attributes to the hierarchy, right-click within the
Hierarchy View
and select Add/Remove Attributes to Hierarchy. The
Select Objects dialog box opens.
7
In the Available objects pane, select the attributes to use in the hierarchy and click the arrow to add them to the Selected objects pane.
8
Click OK to close the Select Attributes dialog box and return to Architect.
The attributes you selected appear in Hierarchy View.
9
To create a browsing or drilling relationship, locate an attribute that is to be able to browse to and/or drill to another attribute. Drag from the middle of the attribute to another attribute. A browsing and/or drilling relationship is created between the two attributes.
10
To use the hierarchy as a drill hierarchy, right-click within the Hierarchy
View
and select Use As a drill hierarchy. If you clear this check box, the hierarchy is only used for browsing.
A drill hierarchy can be used for browsing as well as drilling. Drill hierarchies are discussed in
Drilling using hierarchies, page 365
.
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Each attribute in a user hierarchy has properties that affect how that attribute is displayed and accessed in a hierarchy. You can right-click an attribute and configure the properties listed below:
• Define Browse Attributes
: Defines the attributes to which users can browse to and/or drill to from the selected attribute. These relationships can also be defined by dragging and dropping from one attribute to another as is described earlier in this procedure.
• Define Attribute Filters
: Specifies whether the data retrieved and displayed when browsing the hierarchy should be complete or filtered by any specific criteria. Only data satisfying the filter criteria is displayed when browsing the hierarchy (see
Filtering attributes in a hierarchy, page 359
).
• Set As Entry Point
: Specifies whether the user can begin browsing in
this hierarchy using this attribute (see
• Element Display
: Determines the elements a user can see. The element display may be Locked, Unlocked, or Limited (see
Controlling the display of attribute elements, page 355
12
From the Home tab, click Save and Close to save your changes and close Architect.
13
You can save user hierarchies in any folder. However, to make the user hierarchy available for element browsing in the Data Explorer, you must place it in the Data Explorer sub-folder within the Hierarchies folder.
This is discussed in
Enabling hierarchy browsing in reports: Data
14
From the Schema menu, select Update Schema.
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Introduction
Facts are one of the essential elements within the business data model. They relate numeric data values from the data warehouse to the MicroStrategy reporting environment. Facts generally represent the answers to the business questions on which users want to report.
In the MicroStrategy environment, facts are schema objects created by and shared between MicroStrategy users. The facts you create in MicroStrategy allow users to access data stored in the data warehouse. Facts form the basis for metrics, which are used in the majority of analyses and reports that users can create with MicroStrategy.
Facts and attributes are necessary to define projects. In a MicroStrategy project, facts are numeric data and attributes are contextual data for the facts. For example, you want to analyze the amount of sales at a certain store during January. In this case, the amount of sales represents the fact, and the store and month represent attributes. As the project designer, you must
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The Building Blocks of Business Data: Facts Project Design Guide create projects that contain facts and attributes. Users can then use these facts and attributes as building blocks for metrics and reports.
Facts are stored in the data warehouse in
fact tables
. These fact tables are composed of different columns. Each cell in the columns represents a specific piece of information. When fact information is requested for a report in MicroStrategy, that column is accessed to retrieve the necessary data. This data is used to create a metric (such as profit) which is a business measure.
Facts are based on physical columns within tables in the data warehouse, as shown below.
186
Like other schema objects such as attributes, facts are logical MicroStrategy objects that correspond to physical columns and tables. Unlike attributes, facts do not describe data. Facts are the actual data values stored at a specific
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entry level
is the lowest set of attributes at which a fact is stored.
While creating facts is a major step in the initial creation of a project, it can often be necessary to modify and create facts throughout the life cycle of a project. The procedures to perform these tasks are discussed in the first section (
) of this chapter. The later sections discuss conceptual information on facts, as well as highlight some advanced fact design techniques and procedures.
Creating facts
A fact has two common characteristics: it is numeric and it is aggregatable.
Examples of facts include sales dollars, units sold, profit, and cost.
Data warehouses contain different types of facts depending on the purpose of the data. For example, facts such as Tenure and Compensation Cost could exist in a data warehouse that contains human resources data. Facts such as
Quantity and Item Cost could exist in a warehouse containing sales and distribution data.
It is important to understand how to define facts because facts are the basis for almost all metrics. Facts also allow you to create advanced metrics containing data that is not stored in the warehouse but can be derived by extending facts.
This section provides steps to create facts at different phases of the project design process, using different techniques and MicroStrategy interfaces:
•
Simultaneously creating multiple, simple facts, page 188
covers steps to create multiple, simple facts as part of the initial project design effort or later in a project’s life cycle with the Fact Creation Wizard.
You can also create multiple simple or advanced facts as part of the initial
project design effort using Architect, as described in
Creating and modifying simple and advanced facts using Architect, page 195
•
Creating and modifying simple and advanced facts, page 190
covers steps to add and modify both simple and advanced facts for an existing project.
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Simultaneously creating multiple, simple facts
During your initial project design effort, you can create multiple simple facts using the Project Creation Assistant, the Fact Creation Wizard, and
Architect. However, fact creation and modification can be done throughout the entire life cycle of a project. Facts can be created and modified using various techniques, utilizing the following MicroStrategy tools:
•
The Fact Creation Wizard is a step-by-step interface that is typically used when you first create a project. It allows you to create multiple facts in a single creation process.
The Project Creation Assistant utilizes the Fact Creation Wizard to help you create the facts for your initial project creation effort. You can also access the Fact Creation Wizard in MicroStrategy
Developer from the Schema menu.
•
The Fact Editor, which is discussed in
Creating and modifying simple and advanced facts, page 190
, is used to add advanced features to facts that already exist or to create new simple or advanced facts as your project evolves.
•
Architect, which is discussed in
Creating and modifying simple and advanced facts using Architect, page 195
, is used to create and modify simple and advanced facts in a visually integrated environment.
To create facts with the Fact Creation Wizard
This procedure is part of an initial project creation effort using the Project
Creation Assistant, which launches the Fact Creation Wizard to complete the
fact creation tasks. For steps to access the Project Creation Wizard, see
To create a new project using the Project Creation Assistant, page 89
also access the Fact Creation Wizard in MicroStrategy Developer from the
Schema
menu.
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1
In the Project Creation Assistant, select Create facts. The Fact Creation
Wizard opens, as shown below:
2
Click Define Rules to set some basic fact creation rules. The Fact
Creation Rules page opens.
Rules help automate and govern the fact creation process. If the naming conventions in your warehouse do not conform to the defaults in the Fact
Creation Rules page, you may need to change these rules.
3
The Column data type area allows you to select the column data types that are available as possible fact ID columns. Select the check boxes for the data types to be included when the wizard searches the data warehouse for available fact columns.
For example, if you select Character and Numeric and leave the remaining check boxes cleared, only columns whose data types are numeric or character-based are displayed in the Fact Creation Wizard as possible columns to use for your facts.
Unlike most attributes which can access multiple columns of description information, a fact does not have description information. Therefore, you can only select data types for the ID columns of your facts.
4
The Fact name area allows you to determine how to create default fact names, that is, whether to replace underscores in the fact name with spaces and whether the first letter is capitalized. Select the appropriate check boxes to create the desired default fact names.
5
Click OK to accept your rule changes and return to the Fact Creation
Wizard.
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Fact column selection
6
Click Next. The Column Selection page opens, with columns that are not currently being used in the project listed in the Available columns pane.
7
From the Available columns pane, select the fact columns to use for your facts and click > to add them to your project. Click >> to add all the listed columns.
Note the following:
•
You can rename any fact to make its name more user-friendly by right-clicking the fact and selecting Rename.
•
The Fact Creation Wizard cannot handle columns that hold the same information but have different column names (that is, heterogeneous columns). For more information about mapping facts to heterogeneous columns, see
Facts with varying column names: Heterogeneous column names, page 201
.
8
To remove fact columns from your project, select them from the Facts pane and click < to move them to the left side. Click << to remove all the columns in your project.
9
Click Next. The Finish page opens.
10
Review the summary information in the Finish page and click Finish to create the facts.
The selected fact definitions are stored in the metadata. To continue creating
a project with the Project Creation Assistant, see
Simultaneously creating multiple attributes, page 227
.
Creating and modifying simple and advanced facts
After you have created a project, you can use either the Fact Creation Wizard, the Fact Editor, or Architect to create and modify facts in your project:
•
With Architect you can:
Create simple facts
Create multiple facts quickly
Add a large number of facts during project creation
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Create simple and advanced facts
Edit existing facts and configure additional schema-level settings
With Architect, you can support all of the simple and advanced fact features that are available in the Fact Editor. Rather than focusing on one fact at a time with the Fact Editor, you can use Architect to create and modify multiple facts for a project at one time. For information on how to use Architect, see
Creating and modifying simple and advanced facts using Architect, page 195
•
With the Fact Creation Wizard you can:
Create simple facts
Create multiple facts quickly
Add a large number of facts during project creation
The Fact Creation Wizard can create multiple facts quickly and easily.
However, it limits you to creating simple facts and does not allow you to edit existing facts. Typically, you only use the Fact Creation Wizard as part of the initial project creation, for creating most of the facts for the
project. For steps to use the Fact Creation Wizard, see
Creating one or more simple facts with the Fact Creation Wizard, page 191
.
•
With the Fact Editor you can:
Create simple and advanced facts
Edit existing facts and configure additional schema-level settings
You can use the Fact Editor to edit existing facts and create fact expressions, column aliases, level extensions; map multiple or heterogeneous columns; and configure other settings. The Fact Editor allows you to modify one fact at a time, which can be helpful when only a few facts in a project need to be modified. For steps to use the Fact Editor, see
Creating simple and advanced facts with the Fact Editor, page 192
and
Modifying simple and advanced facts with the Fact Editor, page 194
.
Creating one or more simple facts with the Fact Creation
Wizard
Although the Fact Creation Wizard is primarily used to create most of a project’s facts during initial project creation, you can use it at any time to create one or more simple facts at the same time.
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To create facts with the Fact Creation Wizard
1
In MicroStrategy Developer, log in to the project source that contains your project and expand your project.
You must use a login that has Architect privileges. For more information about privileges, see the List of Privileges chapter in the Supplemental Reference for System Administration.
2
From the Folder List in MicroStrategy Developer, select the project to which to add additional facts.
3
From the Schema menu, select Fact Creation Wizard. The Fact
Creation Wizard opens.
To use the Fact Creation Wizard to add facts, follow the procedures
To create facts with the Fact Creation Wizard, page 188
.
Creating simple and advanced facts with the Fact Editor
As your project evolves, you can create additional facts and modify existing facts with the Fact Editor. You can also use the Fact Editor to add extensions to those facts and configure additional settings within them to support various analytical requirements.
The following procedure describes how to use the Fact Editor to create a simple fact based on a single fact column in a table.
To create a simple fact with the Fact Editor
1
In MicroStrategy Developer, log in to the project source that contains your project and expand your project.
You must use a login that has Architect privileges. For more information about privileges, see the List of Privileges chapter in the Supplemental Reference for System Administration.
2
From the Folder List in MicroStrategy Developer, select the project to which to add additional facts.
3
From the File menu, select New, and then Fact. The Fact Editor opens, with the Create New Fact Expression dialog box displayed on top of it.
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4
From the Source table drop-down list, select the source table for the fact.
The source table is the table or logical view that contains the fact column on which you want to base a new fact.
5
From the Available columns pane, drag and drop a column into the Fact expression pane.
You can include multiple columns as well as use numeric constants and mathematical operators and functions to create a fact expression. For
information on creating various types of fact expressions, see
Mapping physical columns to facts: Fact expressions, page 197
.
6
In the Mapping area, select Automatic or Manual:
• Automatic
mapping means that all of the tables in the project with the columns used in the fact expression are selected as possible source tables for the fact. You can then remove any tables mapped automatically or select other tables.
• Manual
mapping means that all of the tables in the project with the columns used in the fact expression are located but are not selected as possible source tables for the fact. You can then select which of those tables are used as source tables for the fact. Other scenarios in which you should use the manual mapping method include:
If you are creating a constant expression that is not based on a physical column in a project table, you must select the tables for which you want your constant expression to apply.
If the same column name does not contain the same data across different tables, manually select the appropriate source tables for each fact.
For example, suppose you have a column named Sales, which exists in both the Fact_Sales table and the Fact_Discount table. In the Fact_Sales table, the Sales column contains revenue data. However, in the Fact_Discount table, the Sales column contains discount data. In other words, although the column name is the same in both tables (Sales), the columns contain different fact data in each table. When creating the
Revenue fact, you must select the Manual mapping method so you can select the Fact_Sales table as a source table for the Revenue fact. When creating the Discount fact, you must select the Manual mapping method so you can select the Fact_Discount table as a source table for the Discount fact. If you use the Automatic mapping method in both cases, the MicroStrategy SQL Engine may use the incorrect column for the facts.
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Click OK to close the Create New Fact Expression dialog box.
8
Use the tabs of the Fact Editor to define fact expressions, create column aliases, and create extensions, as described below.
For detailed information about the options on each tab within the
Fact Editor, refer to the MicroStrategy Developer Help (formerly
MicroStrategy Desktop Help).
• Definition
: This tab allows you to define fact expressions. Fact definitions are discussed in
How facts are defined, page 196
.
• Column Alias
: This tab allows you to create a column alias for the
fact. Column aliases are discussed in
Fact column names and data types: Column aliases, page 203
.
• Extensions
: This tab allows you to create fact level extensions. Fact extensions are discussed in
Modifying the levels at which facts are reported: Level extensions, page 206
.
9
When your changes are complete, click Save and Close.
10
In the Save As dialog box, navigate to the location in which to save the fact. Enter a name for the fact and click Save. The fact is saved and the
Fact Editor closes.
11
From the Schema menu, select Update Schema to update the project schema.
Modifying simple and advanced facts with the Fact Editor
To modify an existing fact with the Fact Editor
1
In MicroStrategy Developer, open the folder that contains the fact to modify.
2
Double-click the fact to open the Fact Editor and edit the fact.
You can learn how to create more advanced facts in the various sections below.
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Creating and modifying simple and advanced facts using
Architect
Architect can be used to create and modify simple and advanced facts in a visually integrated environment. Architect allows you to view the tables, attributes, attribute relationships, facts, user hierarchies, and other project objects together as you design your project.
With Architect, you can support all of the simple and advanced fact features that are available in the Fact Editor. Rather than focusing on one fact at a time with the Fact Editor, you can use Architect to create and modify multiple facts for a project at one time. Review the chapters and sections listed below for information on Architect and steps to create and modify facts using Architect:
•
Chapter 5, Creating a Project Using Architect
•
Creating and modifying projects, page 106
•
Creating and modifying facts, page 138
The structure of facts
As shown in the diagram below, facts are made up of the following components:
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•
The fact definition is composed of one or more fact expressions. Every fact must have at least one expression. Fact definitions are discussed in detail in
How facts are defined, page 196
.
•
The column alias stores the column name MicroStrategy uses to generate SQL statements when creating temporary tables related to the fact. Every fact must have a column alias. MicroStrategy selects a default column alias depending on the type of fact, unless you create a new column alias. Column aliases are discussed in detail in
Fact column names and data types: Column aliases, page 203
•
Fact level extensions allow facts stored in the data warehouse at one level to be reported at an unrelated level. Extensions can also prevent a fact from being reported at a certain level, even though it is stored at that level. Level extensions are very effective for advanced data modeling scenarios. Level extensions are discussed in detail in
Modifying the levels at which facts are reported: Level extensions, page 206
.
You create facts in MicroStrategy Developer using the Fact Creation Wizard and the Fact Editor. During project creation with the Fact Creation Wizard, when you select the numeric column used to represent the fact, both the fact definition and column alias are automatically defined. Level extensions are optional.
For a discussion of the tools used to created facts and procedures on how to use them, see
How facts are defined
A fact definition contains properties that define a fact and its components.
The fact definition is composed of at least one fact expression and basic information about the fact, including the fact name, expression, and the source tables it uses.
The following table provides an example of a fact definition, which includes the fact’s name, expression, and source tables.
Fact
Name
Expression
Unit Price All_Sales
Source Tables
LU_ITEM
ORDER_DETAIL
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In the example, the fact expression maps the fact to the All_Sales columns in the LU_ITEM and ORDER_DETAIL tables in the warehouse. The fact expression contained in the definition represents how the fact is calculated by MicroStrategy. In this case, the fact expression is simply the name of the column which holds the fact data. However, some facts use more advanced expressions to perform calculations on multiple columns of data to return a single fact.
Facts can be found in multiple tables in a warehouse schema, and often must be calculated differently from one table to the next. While the Unit Price fact only has one expression, multiple expressions can exist within a fact definition.
Note the following:
•
Each fact expression relates to one or more related tables that contain the fact.
•
For each of the tables, fact expressions define how the fact is calculated.
Mapping physical columns to facts: Fact expressions
A
fact expression
maps facts to physical columns in the warehouse. These expressions can be as simple as a fact column name from the warehouse or as sophisticated as a formula containing multiple fact column names and numeric constants. Regardless of how it is defined, a fact expression represents a mapping to specific fact information in the warehouse. A fact definition must have one or more fact expressions.
The following image illustrates a column in the fact table and the associated fact expressions:
How facts are defined
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Valid fact expressions are formulas constructed from fact columns with or without numeric constants or mathematical operators. The mathematical operators that can be used in a fact expression are:
•
Addition (+)
•
Subtraction (-)
•
Multiplication (*)
•
Division (/)
You can use the Fact Editor to create fact expressions. These steps are covered in
Creating and modifying simple and advanced facts, page 190
A fact can be defined using an ApplySimple function. Apply functions are discussed in the Pass-Through Expressions appendix in the
MicroStrategy Advanced Reporting Guide.
Most facts represent physical columns in the data warehouse. However, some facts do not exist at all in the warehouse and are defined in other ways, as explained in the following sections.
Implicit facts and implicit fact expressions
Implicit facts are virtual or constant facts that do not physically exist in the database. An implicit fact indicates a fact table from which to retrieve data.
The implicit fact can have its expression defined as a constant value, although nothing is saved in a table column.
For example, you can use implicit fact expressions to create “temporary columns” in the database with a value of “1” for every row. These temporary columns allow you to keep track of how many rows are returned for a certain attribute. You may also find it helpful to use implicit facts when building metrics, where you can sum the column holding the constant to create a
COUNT. For example, if you want to build a metric defined as Sum(1), you can define a fact equal to the constant “1”. For detailed information about metrics, see the MicroStrategy Advanced Reporting Guide.
Derived facts and derived fact expressions
A derived fact has its value determined by an expression that contains more than just a column in a table. Any operation on a column such as adding a constant, adding another column’s values, or setting the expression to be an absolute value, creates a derived fact. In other words, you are creating a fact
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You can create a new fact, Sales, by creating the following derived fact:
Sales = Quantity_Sold * Price
One advantage of creating a derived fact is that a derived fact allows one consistent fact to exist in the project in lieu of having to retrieve multiple intermediary facts from multiple tables. Using a single fact saves storage space and limits the number of SQL passes used in queries.
Rather than creating a derived fact, you can create such analysis in
MicroStrategy with the use of metrics. Metrics allow you to perform calculations and aggregations on your fact data. For more information on what metrics are and how to create them, see the MicroStrategy Advanced
Reporting Guide.
Example: creating derived facts
The Cost fact in the MicroStrategy Tutorial contains the derived fact expression Qty_Sold * Unit_Cost. This expression implies that columns containing data about the quantity of items sold and the price of those units can be multiplied to produce a useful business calculation. In this case, the columns are used to answer the business question, “How much did it cost the company to create the items purchased by customers?”
The following procedure describes how to create a derived fact that uses the derived fact expression described above. You can also created derived facts that use derived fact expressions using Architect, which is described in
Creating derived facts and fact expressions, page 145
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To create a derived fact
1
In MicroStrategy Developer, log in to the MicroStrategy Tutorial project.
2
Navigate to the My Personal Objects folder, and open the My Objects folder.
3
From the File menu, point to New, and then select Fact. The Fact Editor opens, with the Create New Fact Expression dialog box displayed on top of it.
4
From the Source table drop-down list, select the ORDER_DETAIL table.
5
From the Available columns pane, double-click the QTY_SOLD column to add it to the Fact expression pane on the right.
To complete the derived fact expression
A derived fact expression includes a combination of columns, numerical constants, and mathematical operators. The steps below continue the example scenario to provide a guideline of how to create derived fact expressions.
6
With the cursor in the Fact expression pane, click * (multiplication operator) to add it to the expression.
7
From the Available columns pane, double-click the UNIT_PRICE column to add it to end of the fact expression.
8
Under Mapping method, select Automatic.
9
Click Validate to check whether the syntax of the expression is correct.
The expression should appear as shown below:
10
Click OK. The derived fact expression appears in the Fact expression pane in the Fact Editor.
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11
From the File menu, select Save As. The Save As dialog box opens.
12
Enter a name for the derived fact and click Save.
13
When you create a fact for your project, at this point, you must update the project schema. However, since this is only an example, it is not necessary to update the schema.
Facts with varying column names: Heterogeneous column names
In your warehouse, the same fact data can be included in columns with different column names. In the example below, two fact tables in a warehouse each contain columns for dollar sales. Table 1 contains a fact called Dollar_Sales. Table 2 includes a fact called Dollar_Sls. These two items represent the same information.
MicroStrategy allows you to identify heterogeneous fact column names for each fact. With heterogeneous column names, you can refer the same fact to multiple columns with different column names and from different tables that identify the same quantitative value.
In the example above, creating a heterogeneous fact column name for dollar sales informs the system that the Dollar_Sales and Dollar_Sls columns represent the same fact. When you call for the information in a report through the use of a metric, both fact columns are used in the SQL, resulting in an accurate representation of the fact in the report.
Example: Mapping heterogeneous fact columns
The Units Sold fact in MicroStrategy Tutorial consists of two fact columns in the warehouse, Qty_Sold and Tot_Unit_Sales. Although these fact columns have different names and exist in different fact tables, they represent the same data and are therefore both mapped to the Unit Sold fact.
You must map heterogeneous fact columns to their corresponding facts to ensure that accurate and complete data is displayed on reports.
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The following procedure describes how to create the Units Sold fact that already exists in MicroStrategy Tutorial. In the procedure, you create the
Units Sold fact and map its corresponding heterogeneous fact columns to it.
You can also use Architect to create a fact with heterogeneous column names,
Creating facts with varying column names:
Heterogeneous column names, page 147
.
To create a fact with heterogeneous column names
1
In MicroStrategy Developer, log in to the MicroStrategy Tutorial project.
2
Navigate to the My Personal Objects folder, and open the My Objects folder.
3
From the File menu, point to New, and then select Fact. The Fact Editor opens, with the Create New Fact Expression dialog box displayed on top of it.
4
From the Source table drop-down list, select the ORDER_FACT table.
This is one of the tables in which a heterogeneous fact column for the
Units Sold fact exists.
5
From the Available columns pane, double-click the QTY_SOLD column to add it to the Fact expression pane on the right.
6
In the Mapping method area, select Automatic.
7
Click OK. The Fact Editor opens and the fact expression you just created appears in the Fact expression pane.
Now you must add the other heterogeneous fact column as separate expression for the Units Sold fact.
8
Click New. The Create New Fact Expression dialog box opens.
9
From the Source table drop-down list, select the CITY_CTR_SALES table. This is the other table in which a heterogeneous fact column for the
Units Sold fact exists.
10
From the Available columns pane, double-click the TOT_UNIT_SALES column to add it to the Fact expression pane on the right.
11
In the Mapping method area, select Automatic.
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12
Click OK. The Fact Editor opens and the fact expression you just created appears in the Fact expression pane. Now the Units Sold fact you are creating maps correctly to its heterogeneous fact columns.
13
From the File menu, select Save As. The Save As dialog box opens.
14
Enter a name for the new fact and click Save.
15
When you create a fact for your project, at this point, you must update the project schema. However, since this is only an example, it is not necessary to update the schema.
Fact column names and data types: Column aliases
A
column alias
specifies both the name of the column to be used in temporary tables and the data type to be used for the fact.
By default, the data type for a fact is inherited from the data type of the column on which the fact is defined in the data warehouse. However, there are cases where you may need to change this.
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For example, you can define a fact to be the difference between two dates to perform a calculation such as the average number of days between a start and an end date. You could create this fact using the following expression:
ApplySimple("DateDiff(day,#0, #1)", [Start_Date_Id],
[End_Date_Id])
The expression syntax is specific to your database type. This syntax is specific to Microsoft SQL Server. The SQL you create may be different.
The data type for this fact is automatically set to a Date data type because the
Start_Date_ID and End_Date_ID have Date data types. However, the result of the calculation, that is, the difference between the two dates, is an integer.
This is used when a temporary SQL table needs to be created for the calculation. If you did not change the data type of the column alias, then the system uses a Date data type and tries to insert integer data into this column.
This can cause an error for some database platforms. To avoid the possibility of an error due to conflicting data types, you should modify the column alias for the fact to change the default Date data type to an Integer data type.
The procedure below describes how to use the Fact Editor to create column aliases. You can create column aliases using Architect, which is described in
Creating and modifying fact column names and data types: Column aliases, page 149
Prerequisite
•
This procedure assumes you have already created a fact with a valid fact expression for which to create a new column alias.
To create a column alias for a fact
1
In MicroStrategy Developer, log in to the project source that contains the fact to create a new column alias for.
2
Right-click the fact and select Edit. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Fact Editor in edit mode so that you can make changes to the fact. The
Fact Editor opens.
Note the following:
•
If you are only given the option of opening the Fact Editor in read only mode, this means another user is modifying the
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•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
3
Select the Column Alias tab.
4
In the Column alias area, click Modify. The Column Editor - Column
Selection dialog box opens.
5
Select New to create a new column alias. The Column Editor - Definition dialog box opens.
6
You can modify the following properties for the column alias:
• Column name
: The name for the column alias which is used in any
SQL statements which include the fact column.
• Data type
: The data type for the fact. For a description of the different data types supported by MicroStrategy, see
•
Depending on the data type selected, you can specify the byte length, bit length, precision, scale, or time scale for your column alias. For a detailed description on each of these properties, see the
MicroStrategy Developer Help (formerly MicroStrategy Desktop
Help).
7
Click OK to save your changes and return to the Column Editor - Column
Selection dialog box.
8
Click OK to save your changes and return to the Fact Editor.
9
Select Save and Close to save your changes.
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Modifying the levels at which facts are reported: Level extensions
Facts are stored at a particular business level in the warehouse. The level of a fact is defined by the attribute IDs present in the table. For example, the fact table shown below contains several attribute IDs, including Item and
Quarter. These attribute IDs imply that the fact is reported at the item and quarter levels by default.
Level extensions are necessary when facts are stored in the data warehouse at one level and reported at different levels. Every fact is tied to a set of attributes that may or may not satisfy all users’ reporting requirements. A fact extension is needed when a fact does not relate directly or indirectly to an attribute included on a report.
If the entry level of a fact is at the lowest level of a hierarchy, all attributes at a higher logical level in the hierarchy are available for use as well, without the use of level extensions. For example if you have a cost fact at the level of a date attribute in a time hierarchy, MicroStrategy can aggregate the cost fact data to the level of the year attribute because it is in the same hierarchy as the
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Lowering the level of fact data:
You can use level extensions to change a fact level and extend a fact level to a level in a completely different hierarchy. For example, you record a Discount fact at the Item/Date level. That is, discounts apply to particular items on particular days. To see if some call centers are selling significantly more items at a discount than other call centers, you have to extend the level of the
Discount fact to the Call Center level, which is an attribute from a different hierarchy.
Level extensions define how facts can be extended, lowered, or disallowed to other attributes across the schema. By creating a level extension, you are allowing facts or attributes that have been captured at one level to be extended to other levels to meet reporting requirements.
Level extensions are not required like the fact definition and column alias, and they tend to be used only in specific cases.
Before a metric containing a fact can be used with an attribute that is not in or related to the attribute’s entry level, a level extension must be defined for the fact. This is because if a fact is stored at a level unrelated to an attribute on a report, a level extension must exist to relate the fact data to the attribute. Otherwise, there is no way to make a connection between the fact data and the attribute.
You can create fact level extensions by using any of the following methods:
•
Defining a join on fact tables using table relations, page 208
•
Defining a join on fact tables using fact relations, page 212
•
Forcing facts to relate to attributes: Using cross product joins, page 214
•
Lowering the level of fact data: Fact degradations, page 215
•
Disallowing the reporting of a fact at a certain level, page 220
You can find complete descriptions for each of these methods in the online help for the Level Extension Wizard in the Fact Editor.
You can use the Fact Editor to create level extensions.
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Defining a join on fact tables using table relations
A table relation defines a join on tables. When you specify a table to join with a fact, you are creating a table relation to extend a fact. A fact extension can be used to relate a fact to an attribute using a fact table. The join is important as the table contains an attribute in the entry level and the attribute to which to extend.
For example, the MicroStrategy Tutorial project includes a Freight metric.
This metric has a table relation fact extension to the Item attribute. Since the
ORDER_FACT table that defines Freight does not include the identity column for the Item attribute, the Freight fact cannot be reported at the Item level. A fact extension is required to view freight values for each item included in an order. In this example, the ORDER_DETAIL table is used to create the Freight fact extension to Item because:
1
The ORDER_FACT and ORDER_DETAIL tables both contain the Order attribute’s identity column to join the tables, and ORDER_DETAIL contains the Item attribute’s identity column to extend the fact to Item.
2
The Freight fact cannot simply be joined with a table containing Item information to return a meaningful freight value for each item. An allocation expression is required to extend Freight to the Item level.
Notice that the ORDER_FACT and ORDER_DETAIL tables include
Order-level Units Sold and Item-level Units Sold columns respectively.
These two columns are used to allocate the fact expression in the procedure below.
The following procedure steps through how to create the fact extension that has been created for the Freight fact of the Tutorial project. The procedure also describes general principles of creating fact extensions which you can use to create fact extensions for the facts in your project.
To define a fact extension with a table relation
1
In Developer, log in to the MicroStrategy Tutorial project.
2
Browse to the Facts folder and double-click the Freight fact to edit it. If a message is displayed asking if you want to use read only mode or edit
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Note the following:
•
If you are only given the option of opening the Fact Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Fact Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
3
Click the Extensions tab.
4
Select Extension to Item and click Modify. The Level Extension Wizard opens.
To create a new fact extension you would click New. However, this example steps through how the Freight fact extension Extension to
Item was created.
5
Read the Welcome statement and click Next. The General Information page opens.
To lower, extend, or disallow the fact entry level
6
Enter a name and a description for your fact extension (already provided). Then select whether you want to:
• Lower the fact entry level
: define a fact degradation (see
Lowering the level of fact data: Fact degradations, page 215
)
• Extend the fact entry level
: define a fact extension on a table relation, dynamic fact relation, or a cross product join
• Disallow partially or completely the fact entry level
: define a fact extension that does not allow a fact to be reported at a certain level
(see
Disallowing the reporting of a fact at a certain level, page 220
)
For this example you are creating a fact extension on a table relation, so select Extend the fact entry level, and click Next. The Extended
Attributes page opens.
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To select attributes to extend the fact to
7
Select the attributes you want to extend the fact to, allowing the fact to be reported at the new level. For this example Item is already selected. Click
Next
. The Extension Type page opens.
To extend the fact so that it can be reported at any level in a hierarchy, choose the lowest level attribute in that hierarchy.
To select the type of fact extension
8
Select how you want to extend the fact:
• Specify the relationship table used to extend the fact
: select a relationship table and join attributes.
• Select the relationship table dynamically
: select a fact and join attributes. This allows the MicroStrategy Engine to select the table that includes the fact and join attributes you choose to create the fact extension (see
Defining a join on fact tables using fact relations, page 212
).
• Perform the extension through a cross product
: select to apply a cross product join (see
Forcing facts to relate to attributes: Using cross product joins, page 214
For this example select Specify the relationship table used to extend
the fact
, and click Next to continue defining your fact extension on a table relation. The Table Selection page opens.
To select the table, join attributes, and define the allocation expression
9
Select the table used to extend the fact to the new level. For this example, the ORDER_DETAIL table is already selected. Click Next. The Join Type page opens.
10
Select whether to allow Intelligence Server to dynamically select what attributes to perform the join, or manually select the attributes. Since you know that you want to join the ORDER_FACT and ORDER_DETAIL tables using the Order attribute, select Order and click Next. The Join
Attributes Direction page opens.
11
You can choose to join using the attribute, or join using the attribute and its children. In this case Order has no children, so you do not have to click the Join against arrow to change the default. Click Next. The Allocation page opens.
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12
Enter an allocation expression that calculates the fact at the new level.
For this example, the allocation expression is already provided,
((Freight * [Item-level Units Sold]) / [Order-level
Units Sold]).
Take a moment to review the allocation expression. Notice that the expression returns an average freight amount per item of an order.
Therefore, the extension of Freight provides an estimate of the freight for each item of an order, not an exact calculation. A more detailed description of why this occurs follows this procedure.
13
Click Finish to create the fact extension.
When the engine processes a report containing Order, Item, and Freight, it joins ORDER_FACT and ORDER_DETAIL and considers the resulting table as one logical fact table at the Item, Day, Order, Employee, Promotion level.
The SQL generated for the report containing Order, Item, and Freight
(metric mapped to the Freight fact) is: select a11.[ORDER_ID] AS ORDER_ID, max(a11.[ORDER_DATE]) AS ORDER_DATE, a12.[ITEM_ID] AS ITEM_ID, max(a13.[ITEM_NAME]) AS ITEM_NAME, sum(((a11.[FREIGHT] * a12.[QTY_SOLD]) / a11.[QTY_SOLD])) AS WJXBFS1 from [ORDER_FACT] a11, [ORDER_DETAIL] a12,
[LU_ITEM] a13 where a11.[ORDER_ID] = a12.[ORDER_ID] and a12.[ITEM_ID] = a13.[ITEM_ID] group by a11.[ORDER_ID], a12.[ITEM_ID]
The SQL statement above is for an Access database. The SQL for your reports may vary depending on the type of DBMS that you use.
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To view how the fact extension is an estimation of freight values for each item of an order, review the values of the first order with an extra metric that calculates the number of each item type in an order shown below.
Notice that the Freight metric averages the amount of freight per item in an order. The larger freight values occur because more than one of the item type was included in the order. This illustrates how fact extensions often provide an estimation of values at a different level rather than an exact calculation. If you want to provide exact values of data at a certain level, you most likely need to capture such data and store it in your data source.
Defining a join on fact tables using fact relations
Fact extensions can be defined by a fact relation instead of a table relation.
With a fact relation, the table join is possible on any table that contains the fact. This allows more flexibility in defining the relations, since the
MicroStrategy Engine is responsible for choosing the appropriate table to join, rather than you having to select tables manually.
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The following diagram shows the schema from the example in
Defining a join on fact tables using table relations, page 208
after two summary tables are added to it.
To extend the entry level of the Freight fact to Customer, you can create a fact relation using the Order Unit Sales fact.
The MicroStrategy Engine tries to join a table containing Freight to a table containing Order Unit Sales. The engine can make the following joins, depending on the join attributes specified:
•
Table 1 and Table 2 on Distribution Center, and Order
•
Table 1 and Table 4 on Distribution Center
•
Table 2 and Table 3 on Distribution Center
•
Table 3 and Table 4 on Distribution Center
The joins described above demonstrate how the join attributes can be either
Distribution Center and Order or just Distribution Center.
You can define the fact relation in the Level Extension Wizard which you can access from the Fact Editor. Open the Order Unit Sales fact and extend it to either Distribution Center and Order or just Distribution Center. Next, select the Select the relationship table dynamically option and specify the tables to use for the extension. This option is set in the step immediately after
To select the type of fact extension, page 210
in the procedure above. The tables and attributes you specify in the wizard determine the different types of joins that are created, as explained above.
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The SQL generated for a report containing Distribution Center, Customer, and Freight is shown below, if the only join attribute is Distribution Center.
select a1.DIST_CENTER, a2.CUSTOMER, sum(a1.Freight) from TABLE3 a1, TABLE4 a2 where a1.DIST_CENTER = a2.DIST_CENTER
group by a1.DIST_CENTER, a2.CUSTOMER
The SQL statement above is for an Access database. The SQL for your reports may vary depending on the type of DBMS you use.
As with table relations, you can specify the best fit as the join strategy so that the engine calculates the joins. In a best fit join, the set of join attributes must contain the entire key of the left-hand-side fact table (Table 3 in the example SQL above).
Forcing facts to relate to attributes: Using cross product joins
You can use a cross product join when a join does not exist and you need to force a fact to relate to an attribute by extending the fact. The cross product join is an extension that allows a single fact value to relate to all elements of an unrelated attribute. This method can produce incorrect data because data can be repeated and counted twice in some cases.
Cross products should only be used when no other way to extend the fact exists. When you specify a cross product join to relate a fact to an attribute, you are creating a Cartesian product of the lookup attribute.
Since this method can be inefficient, MicroStrategy does not recommend using the cross product join.
For example, in the following schema, Distribution Center does not relate to
Dollar Sales:
To report Dollar Sales by Distribution Center, a cross product join must be used.
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You can define this cross product join in the Level Extension Wizard in the
Fact Editor. Open the Dollar Sales fact and extend it to the Distribution
Center attribute. Next, select the Perform the extension through a cross
product
option. This option is set in the step immediately after
To select the type of fact extension, page 210
of the procedure above. For this example, you do not need to specify an allocation expression.
Notice that no join attributes are specified. The MicroStrategy Engine always cross-joins the lookup tables of the attributes in the extension.
The SQL generated for a report containing Customer, Distribution Center, and Dollar Sales is: select a1.DIST_CENTER, a2.CUSTOMER, sum(a2.DOLLAR_SALES) from TABLE1 a1, TABLE2 a2 group by a1.DIST_CENTER
The SQL statement above is for an Access database. The SQL for your reports may vary depending on the type of DBMS you use.
Lowering the level of fact data: Fact degradations
Degradation
, which lowers a fact level, is the logical opposite of aggregation.
To view fact data at a lower logical level than the fact is stored at, you must degrade the fact to a lower level. This scenario may occur because you stored a fact at a level that is used most commonly in reports. However, you must support those users who wish to view and analyze the same fact data at a lower logical level.
For example, the Human Resources Analysis Module includes a Planned
Compensation fact that is stored at the Department level, and has a fact degradation to the Employee level (the attributes, facts, and metrics used in this example can all be found in this Analytics Module). The fact extension does not use an allocation expression to degrade Planned Compensation to
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The Building Blocks of Business Data: Facts Project Design Guide the Employee level. This causes every employee to be listed with the same planned compensation value as the employee’s department, as shown below:
The analytical value of this fact degradation is not immediately recognizable.
However, now that Planned Compensation is available at the Employee level, you can create more meaningful analysis with other fact data that is stored at the Employee level. For example, the Compensation Cost fact is stored at the
Employee level. The metric Actual as % Planned Compensation has been created to calculate the actual compensation of an employee as a percentage of the planned compensation for the entire department of the employee. The metric definition is ([Compensation Cost]/[Planned
Compensation]), which performs a division of metrics defined from the
Compensation Cost and Planned Compensation facts, respectively. You can
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Project Design Guide The Building Blocks of Business Data: Facts 6 now view what percentage of your planned compensation per department has been spent per employee, as shown below:
Without using a degradation of Planned Compensation to Employee, you could not include Department and Employee on a report with these metrics and return accurate values.
The following procedure steps through how to create the fact degradation that has been created for the Planned Compensation fact of the Human
Resources Analysis Module. The procedure also describes general principles of creating fact degradations which you can use to create fact degradations for the facts in your project.
To define a fact degradation
1
In Developer, log in to the Human Resources Analysis Module.
2
Browse to the Facts / Compensation / Planning folder and double-click the Planned Compensation fact to edit it. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Fact Editor in edit mode so that you can make changes to the fact. The Fact Editor opens.
Note the following:
•
If you are only given the option of opening the Fact Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Fact Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
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•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
3
Click the Extensions tab.
4
Select Degradation to Employee and click Modify. The Level Extension
Wizard opens.
To create a new fact degradation you would click New. However, this example steps through how the Planned Compensation fact degradation Degradation to Employee was created.
5
Read the Welcome statement and click Next. The General Information page opens.
6
Enter a name and a description for your fact extension (already provided). Then select whether you want to:
• Lower the fact entry level
: define a fact degradation
• Extend the fact entry level
: define a fact extension on a table
relation, dynamic fact relation, or a cross product join (see
Defining a join on fact tables using table relations, page 208
Defining a join on fact tables using fact relations, page 212
• Disallow partially or completely the fact entry level
: define a fact extension that does not allow a fact to be reported at a certain level
(see
Disallowing the reporting of a fact at a certain level, page 220
)
For this example you are creating a fact degradation so select Lower the
fact entry level
, and click Next. The Extended Attributes page opens.
7
Select the attributes you want to degrade the fact to, allowing the fact to be reported at the new level. For this example Employee is already selected. Click Next. The Join Type page opens.
To extend the fact so that it can be reported at any level in a hierarchy, choose the lowest level attribute in that hierarchy.
8
Select what attribute(s) to perform the join. For this example, the
Department attribute is already selected. Click Next. The Join Attributes
Direction page opens.
9
You can choose to join using the attribute, or join using the attribute and its children. For this example, the join is performed on the Department attribute and its children. Click Next. The Allocation page opens.
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Enter an allocation expression that calculates the fact at the new level.
For this example, you do not need to include an allocation expression. See
Fact degradations with allocation expressions, page 219
for an example of using an allocation expression for a fact degradation.
11
Click Finish to create the fact degradation.
Fact degradations with allocation expressions
Not all fact degradations can simply be lowered to a new level. Ordinarily, you must add an allocation expression, which allows the distribution of values according to a calculation you specify, to change the definition of the fact in a level extension.
This is similar in concept to choosing an aggregation function (Sum,
Avg, and so on) when aggregating data to higher levels.
For example, if your fact is stored at the yearly level and you want to report the data at the monthly level, you can create a degradation on the fact to relate it to the monthly level. You select Month to be the attribute to which to degrade. You then specify that the allocation expression is fact/12.
By creating allocation expressions, you define how higher-level facts are degraded to lower-level attributes. Allocation expressions are defined by operations you set on attributes and facts in the Level Extension Wizard in the Fact Editor.
Fact degradations often produce data estimates rather than exact values for the fact data at lower logical levels. For example, consider the allocation expression fact/12 for a degradation from Year to Month. Using such an allocation expression would spread a year’s fact data evenly over the 12 months of that year. While it is possible that the fact data would be the same for every month of the year, this is often an unlikely scenario. Such fact degradations should be used only when fact data is not stored at a lower logical level and there is no way to directly relate the fact data to the lower logical level.
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Disallowing the reporting of a fact at a certain level
The Disallow partially or completely the fact entry level setting within the
Fact Editor is like a lock which prevents a fact from being reported at a specific level. The setting prevents unnecessary joins to lookup tables. The following examples describe instances in which disallowing a fact entry level can prove useful.
Disallowing a fact to be extended to a level lower than the fact’s entry level due to unnecessary complexity and the cost of analyzing fact data at such a level is a common use for this feature. If a fact is stored at a level that is counterproductive to a query, such as data that is stored at the Minute or
Second level, you can disallow the lower levels. For example, if you have three years’ worth of data, querying at the Minute or Second level consumes too many resources and returns extensive data. With a disallow in place, if you create a report and attempt to include the fact at the Minute or Second level, an error is returned, indicating that the report cannot be run at that level.
Consider a schema containing three dimensions: Geography, Time, and
Product. Suppose you create a fact called Sales at the Item level in the
Product dimension and a metric called Sales as the sum of the Sales fact.
When you create a report containing the Month attribute and the Sales metric, the Analytical Engine does a dynamic cross-join and evaluates the report. To explicitly disallow an extension of the Sales fact to the Time dimension, you would use the Disallow partially or completely the fact
entry level
setting and select the lowest attribute in the Time dimension
such as Day. This option is set in the step immediately after
To lower, extend, or disallow the fact entry level, page 209
of the procedure to create a fact extension above. After updating the schema and re-executing the report, the report fails because the disallow setting now prevents the cross-joins between the lookup tables and fact tables. This setting, however, does not affect normal joins.
In the previous example, for the Sales fact, assume you specify an extension to the Month attribute and also disallow extension to Year which is a parent of the extended attribute, Month. If you execute the report containing the Year attribute and Sales metric, the report runs successfully. In this case, the engine sorts the extension conditions specified in some order and calculates the report based on the sorted order of extensions. This is not an expected design condition, although the engine returns a valid SQL. It is advisable to avoid fact definitions that contain contradictory extension definitions.
The Disallow the fact entry level setting applies only to attributes that can be considered as extended attributes. For example, you create a report that
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Project Design Guide The Building Blocks of Business Data: Facts 6 contains the attributes Subcategory and Item and the Revenue metric, which is defined as sum of the Revenue fact. You now disallow an extension on the
Revenue fact for the Item attribute and update the schema. If you re-execute the report, you can still see Revenue by Item. This implies that the fact extension has not been disallowed. This is because Revenue exists at the same level as Item in the MicroStrategy Tutorial project. So you encounter only normal joins and no extensions. There must be a valid reason to disallow reporting a fact at a certain level. In this case, disallowing the
Revenue fact at the level it is stored at in the data warehouse does not make logical sense.
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Introduction
Business data represented by facts can offer little insight without the presence of business concepts and context, which take the form of attributes in MicroStrategy. Attributes provide the business model with a context in which to report on and analyze facts. While knowing your company’s total sales is useful, knowing where and when the sales took place provides the kind of analytical depth users require on a daily basis.
For example, you have a report with the Month, Year, and Region attributes on the template, as well as a Revenue metric based on the Revenue fact.
When executed, the report displays your company’s revenue at the region, month, and year levels. Because of the attributes on the report, a substantial amount of information is available, including which regions produced the least revenue and which years saw the highest growth in revenue. If you remove the attributes from the report, you can only find out how much revenue the company generated in total.
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Overview of attributes
Creating attributes is an important step in the initial project design effort, which comes after creating facts when using the Project Creation Assistant.
New user and application requirements make attribute creation and modification an important part of the entire project life cycle.
In the data warehouse, attributes are normally identified by a unique ID column in a lookup table. In MicroStrategy reports, attributes are identified by the column headers of the reports.
A report designer creates a report in part by determining these report column headers. Intelligence Server, using this report definition, instructs the engine how to build the SQL for that report. The expressions of attributes and facts in the report define the SELECT clause of the SQL command.
For example, consider the following:
Select Store_ID, Date, sum(Sales)
From Store_Fact
Group By Store_ID, Date
In the SQL above, sales information will be retrieved by store and date. The attributes and metrics in the report tell Intelligence Server where to look in
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Project Design Guide The Context of Your Business Data: Attributes 7 the data warehouse for the information and how to create the SQL that will retrieve it. Because of this process, report analyzers do not have to know SQL to extract information from a data warehouse.
The lowest level attribute you include in a report, such as Day, is the lowest level of detail reported. A high-level report, such as a report at the Year level, includes the Year attribute but lacks the detail of a similar report which includes the lower level attributes Month and Week. It is important to understand the data is still the same, it is just not aggregated.
A discussion about metrics, filters, and reports is beyond the scope of this guide and is covered in the MicroStrategy Advanced Reporting
Guide.
Attributes are defined by these properties:
• Attribute form
: contains an identifier or descriptor of an attribute.
Attributes can have multiple attribute forms. For example, for the
Customer attribute, Customer Email, Customer First Name, and
Customer Last Name are examples of attribute forms. See
Column data descriptions and identifiers: Attribute forms, page 244
• Attribute expression
: maps a MicroStrategy attribute form to one or more columns in the warehouse. See
Attribute form expressions, page 248
.
• Attribute relationship
: allows interaction of data at different conceptual
levels and shows how data is related within a project. See
Attribute relationships, page 262
.
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The following diagram illustrates how the attribute properties listed above are related:
While creating attributes is a major step in the initial creation of a project, it is often necessary to modify and create attributes throughout the life cycle of a project. The procedures to perform these tasks are discussed in the first section (
) of this chapter. The later sections discuss conceptual information on attributes, as well as highlight some advanced attribute design techniques and procedures.
Creating attributes
An attribute is primarily used to group and aggregate fact data to add business context to the fact data. The ability to report on and analyze data requires data to have a business context; therefore, creating attributes is a major step in any project design effort.
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This section provides steps to create attributes at different phases of the project design process, using different techniques and MicroStrategy interfaces:
•
Simultaneously creating multiple attributes, page 227
create multiple attributes as part of the initial project design effort or later in a project’s life cycle with the Attribute Creation Wizard.
•
Adding and modifying attributes, page 232
modify attributes for an existing project. This includes adding advanced features such as attribute forms to attributes that already exist or adding new attributes as your project evolves.
You can also create and modify attributes at any phase of the project design process using Architect. For information on creating and modifying attributes using Architect, see
Adding and modifying simple and advanced attributes using Architect, page 237
Simultaneously creating multiple attributes
During your initial project design effort or later in a project’s life cycle, you can create multiple attributes using the Attribute Creation Wizard.
You can also create multiple attributes using Architect, which is described in
Chapter 7, Adding and modifying simple and advanced attributes using Architect
To create attributes using the Attribute Creation Wizard
This procedure is part of an initial project creation effort using the Project
Creation Assistant, which launches the Attribute Creation Wizard to complete the attribute creation tasks. For steps to access the Project Creation
Wizard, see
To create a new project using the Project Creation Assistant, page 89
. You can also access the Attribute Creation Wizard at any time in the
Creating attributes
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Developer.
1
In the Project Creation Assistant, click Create attributes. The Attribute
Creation Wizard opens, as shown below.
2
Review the introduction page that is displayed.
Define attribute creation rules
These rules can make the process of choosing attribute columns and naming your attributes considerably easier, especially if you use consistent naming conventions and data types in your data warehouse.
The Attribute Creation Wizard uses these rules below to help automate the attribute creation process. Change these rules if the naming or data type conventions in your warehouse do not conform to these defaults.
3
Click Define Rules to set some basic attribute creation rules. The
Attribute Creation Rules page opens.
4
The Column data type area allows you to select the column data types to be available as possible attribute ID columns. Select the check boxes for the data types that should be included when the wizard searches the data warehouse for available attribute ID columns.
5
The Attribute name area allows you to determine how to create default attribute names. You can select the appropriate check boxes to set the following default behaviors for creating attribute names:
•
Replace underscores in the attribute name with spaces.
•
Remove the word “ID” from the name.
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•
Capitalize the first letter.
6
The Warehouse search area determines naming conventions to help locate your warehouse objects. The defaults are ID for identifier columns,
DESC for description columns, and LOOKUP for lookup tables.
7
Click OK to accept your rule changes and return to the Attribute Creation
Wizard.
Select the ID column
An ID column is a column or group of columns that uniquely identifies each element of an attribute.
8
Click Next. The ID Column Selection page opens.
When choosing the ID column for an attribute, make sure that all values in the column are unique and that it does not contain NULL values. You should never use a column that has NULL or repeated values as the ID column for an attribute. Doing so results in unexpected behavior and errors.
Only those columns with data types that match those chosen in the rules you defined above appear on the ID Selection page. The columns that match the identifier naming convention that you set in the warehouse search rule above are automatically highlighted.
9
From the Available columns pane, select the columns to use for your attribute IDs and click > to add them to your project. Click >> to add all the listed columns.
Note the following:
•
You can rename any attribute name to make it more user-friendly by right-clicking the attribute and selecting
Rename
.
•
To remove attribute ID columns from your project, select the attribute IDs in the Attributes pane and click < to move them to the Available columns pane.
•
The Attribute Creation Wizard cannot handle columns that hold the same information but have different column names
(that is, heterogeneous columns). For more information about mapping attributes to heterogeneous columns, see
Joining dissimilar column names: Heterogeneous mappings, page 254
.
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Create compound attributes
A compound attribute is defined as an attribute with more than one column specified as the ID column. This implies that more than one ID column is needed to uniquely identify the elements of that attribute (see
Attributes with multiple ID columns: Compound attributes, page 285
10
To create a compound attribute, complete the following steps: a Click Compound Attributes and then click Add. The New Compound
Attribute dialog box opens. b Type a name for the attribute. c Select the columns that are required to uniquely identify the compound attribute and click OK. You are returned to the Attribute
Creation Wizard.
Select the description column
Description columns provide the data which gives context and meaning to your attributes.
11
After adding all your attribute ID columns, click Next. The Description
Column Selection page opens.
12
Select whether to use the ID or a different column for the description of the attribute. The column that meets the description naming convention that you set in the warehouse search rule is automatically selected.
Note the following:
•
In general, you should use the default description column for each attribute. In some cases, however, it may make sense to use the ID column as the description column, such as Year.
•
Other attribute forms need to be created through the Attribute
Editor after you complete steps in the Project Creation
Adding attributes with the Attribute Editor, page 234
, for more information about attribute forms.
Select the lookup table
Lookup tables are the physical representation of attributes; they provide the information for an attribute through data stored in their ID and description columns.
13
Click Next when you are finished selecting description columns for attributes. The Lookup Table Selection page opens.
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14
Select the lookup table for each attribute.
The table that follows the lookup naming convention that you set in the warehouse search rule is automatically selected. In general, you should choose the default lookup table for each attribute.
15
Click Next:
•
If you have created compound attributes, the Compound Attribute
Definition page opens. Specify the lookup table and description column for the compound attributes and click Next. The Relationship
Definition page opens.
•
If you have not created a compound attribute, the Relationship
Definition page opens.
Define the relationship
For each attribute, you specify the children and the type of relationship: one-to-one, one-to-many, or many-to-many. When you design a logical data model for your project (see
Chapter 2, The Logical Data Model
), the relationships between attributes should become apparent. Related attributes such as City, State, or Region are often grouped in a common hierarchy, like Location. In a logical data model, when attributes are in the same hierarchy they must be related to each other, whereas attributes in different hierarchies cannot be related.
16
For each attribute, define child attributes: a In the Attributes pane, select an attribute and click Add. The Select
Children Attributes dialog box opens. b Select the child attributes from the list of available child attributes and click OK. You are returned to the Attribute Creation Wizard.
c In the Children of: attribute name pane, select the relationship type for the attribute to its child attribute. For more information on the
different attribute relationship types, see
Attribute relationships, page 262
17
When you have defined children for all the attributes that need them, click Next. The Finish page opens.
18
Review the summary information in the Finish page and click Finish to create the attributes.
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After you have completed the steps of the Attribute Creation Wizard, the attributes are created. This completes the initial creation of a project with the
Project Creation Assistant.
Adding and modifying attributes
Just as you can add more facts to your project once you have created it, you can also create and add attributes as they become necessary. As a company evolves, so does its reporting requirements; these requirements can lead to changes to the data warehouse as well as to the schema within its
MicroStrategy projects.
For example, a health care company, with offices only in the United States, decides to extend its operations into Europe and Asia. Before the shift overseas, the company does not include lookup tables with information about different countries in its data warehouse.
However, when the company opens its offices in Europe and Asia, it must add lookup tables that contain data about its new offices to its warehouse. It must then add these tables to its MicroStrategy project, and create the appropriate attributes so report users can analyze business data for their appropriate country.
You can use various techniques and interfaces to create and modify attributes for a project:
•
The Attribute Creation Wizard allows you to:
Create simple attributes
Create multiple attributes quickly
Add a large number of attributes during project creation
The Attribute Creation Wizard works well for building a large number of attributes initially, but you cannot use it to modify existing attributes or to define more advanced attributes. In general, you only use the Attribute
Creation Wizard as part of the initial project creation to create most of the attributes for the project. For steps to use the Attribute Creation Wizard, see
Simultaneously creating multiple attributes, page 227
Adding attributes with the Attribute Creation Wizard, page 233
.
•
The Attribute Editor allows you to:
Create simple and advanced attributes
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Edit existing attributes and configure additional schema-level settings
You can use the Attribute Editor to edit existing attributes and create additional attribute forms, map heterogeneous column names, define advanced expressions, configure additional settings, and so on. The
Attribute Editor allows you to modify one attribute at a time, which can be helpful when only a few facts in a project need to be modified. For
steps to use the Attribute Editor, see
Adding attributes with the Attribute
and
Modifying attributes, page 237
•
Architect allows you to:
Create simple attributes
Create multiple attributes quickly
Add a large number of attributes during project creation
Create simple and advanced attributes
Edit existing attributes and configure additional schema-level settings
With Architect, you can support all of the simple and advanced attribute features that are available in the Attribute Editor. Rather than focusing on one attribute at a time with the Attribute Editor, you can use Architect to create and modify multiple attributes for a project at once. For
information on how to use Architect, see
Adding and modifying simple and advanced attributes using Architect, page 237
.
Adding attributes with the Attribute Creation Wizard
Although the Attribute Creation Wizard is primarily used to create most of a project’s attributes during initial project creation, you may still find it useful if you need to create multiple attributes from remaining lookup columns in your warehouse.
Follow the steps below to use the Attribute Creation Wizard to create simple attributes in bulk.
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To create simple attributes in bulk using the Attribute Creation Wizard
1
In MicroStrategy Developer, log in to the project source that contains your project and expand your project.
You must use a login that has Architect privileges. See the List of
Privileges chapter in the Supplemental Reference for System
Administration for more information.
2
From the Folder List, select the project to which to add new attributes.
3
From the Schema menu, choose Attribute Creation Wizard. The
Attribute Creation Wizard opens.
4
To create attributes with the Attribute Creation Wizard, follow the steps
Simultaneously creating multiple attributes, page 227
Adding attributes with the Attribute Editor
The Attribute Editor is used to add advanced features such as attribute forms to attributes that already exist. You can also use it to add new attributes to your project.
To create an attribute using the Attribute Editor
1
In MicroStrategy Developer, log in to the project source that contains your project and expand your project.
2
From the File menu, select New, and then Attribute. The Attribute Editor opens, with the Create New Form Expression dialog box displayed on top of it.
3
From the Source table drop-down list, select a table which contains the columns of data for the attribute. Its columns are listed in the Available
Columns pane.
4
Create a form expression for the ID form of the new attribute being created.
•
To create a simple attribute form expression (
Attribute form expressions, page 248
), drag a column from the Available columns pane to the Form expression pane.
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•
To create a more advanced attribute form expression, use a combination of any of the following techniques:
– Enter constants in double quotes.
– Click f(x) in the Form expression toolbar to create a function using the Insert Function Wizard.
– Click any operator in the Form expression toolbar to insert it into the expression.
5
Click Validate to ensure that your expression is valid.
6
Under Mapping method, select Automatic or Manual:
• Automatic
mapping means that all of the tables in the project with the columns used in the attribute form expression are selected as possible source tables for the attribute form. You can then clear any tables mapped automatically or select other tables.
• Manual
mapping means that all of the tables in the project with the columns used in the attribute form expression are located but are not selected as possible source tables for the attribute form. You can then select which of those tables are used as source tables for the attribute form.
Note the following:
•
The mapping method defaults to Automatic for the first attribute or attribute form expression you create. The system maps the expression to each of the source tables. For subsequent attributes, the default is Manual.
•
An expression that uses only a constant value cannot use the automatic mapping method.
To use an attribute for meaningful analysis, you must verify that it includes a column from a fact table in its definition, or is related to an attribute that does. For example, to use the Month attribute for analysis, you must create a relationship to the Day attribute, which must include the DAY column from the fact table in its definition.
To create relationships between attributes, see
Attribute relationships, page 262
.
7
Click OK. The Create New Attribute Form dialog box opens, from which you can create attribute forms for the attribute (
Column data descriptions and identifiers: Attribute forms, page 244
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8
From the Source tables pane, select a table and click Set as Lookup to set the lookup table for the attribute. A lookup table acts as the main table which holds the information for an attribute. If you chose manual mapping, select the check boxes of the tables to map to the attribute form.
9
In the Form general information area, type a name and description in the associated fields for the attribute form.
10
In the Category used drop-down list, do one of the following:
•
Select a form category from the drop-down list. For a description of
Displaying forms: Attribute form properties, page 246
•
Click Modify to create a new form category.
Using a column with a non-numeric data type as an ID column of an attribute can result in SQL generation issues. Therefore, if you select a column with a non-numeric data type and set it as an ID column, a warning message appears by default when you click OK in the Create New Attribute Form dialog box.
11
In the Form format area, select a display type and a default sorting option from the associated drop-down lists.
Custom groups are sorted by the Default sort of the form that appears first in the Report display forms. For more information on custom groups, refer to the MicroStrategy Advanced Reporting
Guide.
12
If the attribute form should be capable of displaying information in multiple languages, select the Supports multiple languages check box.
For information on supporting the display of data in multiple languages, see
Supporting data internationalization for attribute elements, page 241
.
13
Click OK. The Attribute Editor opens.
14
From the File menu, select Save As. The Save dialog box opens.
15
Navigate to the folder in which to save the attribute. Enter a name for the attribute. Click Save.
16
From the Schema menu, select Update Schema to update the project schema. This ensures that your project is updated to recognize the new attribute definition.
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Modifying attributes
After creating an attribute, you can modify the attribute at any time using the
Attribute Editor. You cannot use the Attribute Creation Wizard to modify attributes.
To modify an existing attribute
1
In MicroStrategy Developer, open the folder that contains the attribute to modify.
2
Double-click the attribute to edit. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Attribute Editor in edit mode so that you can make changes to the attribute. The Attribute Editor opens.
Note the following:
•
If you are only given the option of opening the Attribute Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Attribute Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
You can then modify all the options available when creating and attribute
in the Attribute Editor, which is described in the previous procedure
To create an attribute using the Attribute Editor, page 234
.
You can learn how to create more advanced attributes in the various sections in this chapter.
Adding and modifying simple and advanced attributes using
Architect
Architect can be used to create and modify simple and advanced attributes in a visually integrated environment. Architect allows you to view the tables, attributes, attribute relationships, facts, user hierarchies, and other project objects together as you design your project.
Creating attributes
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With Architect, you can support all of the simple and advanced attribute features that are available in the Attribute Editor. Rather than focusing on one attribute at a time with the Attribute Editor, you can use Architect to create and modify multiple attributes for a project at one time. Review the chapters and sections listed below for information on Architect and steps to create and modify attributes using Architect:
•
Chapter 5, Creating a Project Using Architect
•
Creating and modifying projects, page 106
•
Creating and modifying attributes, page 151
Unique sets of attribute information: Attribute elements
Attribute elements
are the unique sets of information or values of an attribute. For example, in the following diagram, Customer is the attribute and New York NY, Baltimore BA, and Boston BN are elements of the attribute City:
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The following example displays the physical warehouse table that stores elements and data for the Customer attribute. Each attribute element is a row in an attribute lookup table in your data warehouse, as shown below:
The Customer attribute is a good example to understand the components of an attribute and the concept of an attribute element. With the Customer attribute, each attribute element is an individual customer. Each customer
(attribute element) has its own set of information such as last name, first name, email address, and so on which are defined by the attribute forms (see
Column data descriptions and identifiers: Attribute forms, page 244
As shown above, an attribute element is a unique set of information defined by the attribute forms of an attribute. Attribute elements are identified by their browse forms, which should be forms that provide a general description of the attribute element. For example, in the image above, the First Name and Last Name forms are used to identify the attribute elements. Just as you would not refer to a customer by his or her street address, you would not want to use the Address form to identify the Customer attribute elements.
For more information on selecting the attribute forms used to identify
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Using attributes to browse and report on data, page 288
Attribute elements can be identified in logical data models. As shown below, the attribute Division has multiple attribute elements, such as Men’s
Clothing, Shoes, and Sporting Goods:
In MicroStrategy reports, attribute elements are displayed depending on the location of the attribute they are associated with. For example, the report below has two attributes, Sales Organization and Year. Sales Organization is on the rows of the report along with its attribute elements such as USA
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Central. Year is on the columns of the report along with its attribute elements such as 2005.
The display of attributes and their attribute elements is also affected by the location of the metrics on the report. The report above uses the common practice of putting the metrics (Sales Orders Quantity (Base Units) and Cost
Sales Orders) on the columns of the report.
Supporting data internationalization for attribute elements
MicroStrategy supports the internationalization of your data into the languages required for your users. This allows attribute element data to be displayed in various languages that can reflect the user’s language preferences.
For example, consider the simple report below which displays profits for each month of the year.
This is the data that is shown to a user running the report with their regional settings defined as English. By supporting internationalized data, the same
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The Context of Your Business Data: Attributes Project Design Guide report can return the data shown below to a user with their regional settings defined as German.
The attribute element data is displayed in the German language. For example, the January, February, and March attribute elements are displayed as Januar, Februar, and März.
Data internationalization provides data from you data source translated in various languages for the attribute element data. To provide internationalized attribute names (such as Month of Year and Monat im Jahr in the reports above), descriptions, and other translated object information, you must use and configure metadata internationalization. For information on configuring metadata internationalization, see the Supplemental
Reference for System Administration.
Each attribute form can enable or disable the internationalization of its attribute element data. The procedure described below provides the steps to enable or disable internationalization for attribute forms.
You can also use Architect to enable or disable the internationalization of
attribute element data, as described in
Supporting data internationalization for attribute elements, page 172
.
Prerequisites
•
The translated data has been stored in your data source, as described in
Supporting data internationalization, page 56
.
•
The project has been enabled for data internationalization, as described in
Enabling data internationalization for a project, page 94
.
•
An attribute has been created.
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To enable or disable data internationalization for attribute forms
1
In Developer, log in to a project.
2
Navigate to the Schema Objects folder, open the Attributes folder, and then open a folder that contains attributes to enable or disable data internationalization for.
3
Right-click an attribute and select Edit. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Attribute Editor in edit mode so that you can make changes to the attribute. The Attribute Editor opens.
Note the following:
•
If you are only given the option of opening the Attribute Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Attribute Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
In the Attribute forms pane, select an attribute form and click Modify.
The Modify Attribute Form dialog box opens.
5
Select the Support multiple languages check box to enable data internationalization for the attribute form. You can clear the check box to disable internationalization for the attribute form.
The ID form of an attribute does not have this option as these forms are used strictly for identification purposes.
6
Click OK to return to the Attribute Editor.
7
Click Save and Close to save your changes and return to Developer.
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Column data descriptions and identifiers:
Attribute forms
Attribute forms are identifiers or descriptors of an attribute, as explained in
Logical data modeling conventions, page 31
Every attribute must have at least one form, and most have at least two:
•
The ID form (required)
•
A description form
Every attribute must have an ID form (identity form). ID forms serve to uniquely identify each attribute element from other elements for the same attribute. For example, the Customer attribute’s ID form is Customer_ID, which is a column of unique numeric values to identify each customer. To differentiate between two customers such as John Smith and Fred Black, each customer must have a different value for their identity column. In this case John Smith can have a value of 1 in the Customer_ID column and Fred
Black can have a value of 2 in the Customer_ID column.
Attributes also have description forms. The Customer attribute in the
MicroStrategy Tutorial has various forms, including the Customer Name and the Address forms. These types of forms give context and information about the Customer attribute.
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Some attributes can have additional descriptive forms that do not serve as the primary description form. For the Customer attribute, Email is included as an additional descriptive form, as shown in the following diagram:
In the data warehouse, a lookup table with three columns holds the following separate forms, described below:
•
Customer_ID: A unique, identifying number for each customer (ID
form)
•
Customer_Full_Name: The full name of each customer (Description
form)
•
EMAIL: The email address for the specific customer (Additional
description form)
In this example, the LU_CUSTOMER table records all of the attribute form data for the Customer attribute.
Attributes must contain at least one ID form, which uniquely identifies the attribute. The forms you create must have a reference to a lookup table and can include multiple expressions. Each table must have an ID form; the ID
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The Context of Your Business Data: Attributes Project Design Guide forms are used to join tables. When creating an attribute form, you can choose a lookup table in the Attribute Editor from a list of tables existing in the project. In the warehouse, two columns with different names can represent the same information about an attribute. In such cases, you must map the appropriate columns to the same attribute to retrieve accurate and complete results when using an attribute on a report. Heterogeneous column
Joining dissimilar column names: Heterogeneous mappings, page 254
.
For example, two tables exist, one with the forms Customer_ID, Name, and
SSN. The second lookup table contains Customer _ID and Email. The attribute will have the Customer_ID, Name, SSN, and Email forms and the tables will join together through the ID columns because that is the column they have in common.
Displaying forms: Attribute form properties
Attribute form properties are settings that affect the display of the forms.
You must select properties for each form when you create forms in the
Attribute Editor or Architect in MicroStrategy Developer. In the Attribute
Editor, these properties are available when creating a new attribute form, or by modifying an attribute form. In Architect, these properties can be
modified using the Properties pane, as described in
Modifying attributes with the Properties pane
Attribute form properties include the following:
• Categories:
Help group the types of forms. The standard category options are ID, Desc, and None. You can create new form categories in the Attribute Editor.
When you have attributes that require multiple description forms, it is a good practice to map the most commonly used or most important description form to the Desc form of the attribute. Each attribute can have only one Desc form; the other description forms must be mapped to
None forms. While there is no difference in how a Desc attribute form and None attribute form are used in MicroStrategy, mapping the most commonly used or most important description form can be helpful for project designers to quickly distinguish this attribute form from the other secondary forms.
• Format types:
Control how the form is displayed and how filters are defined. For example, specifying a format type of Big Decimal allows users to preserve precision when qualifying on a form with more than 15 digits. Big Decimal is discussed in detail in
.
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• Report Sort
: Defines how the attribute form is sorted by default when included in a report. From the drop-down list, you can choose from
Ascending, Descending, or None. For information on how attribute forms are sorted when multiple attribute forms of a single attribute define a default sort order, see
Default sorting of multiple attribute forms on reports
• Browse Sort
: Defines how the attribute form is sorted by default when viewed in the Data Explorer. From the drop-down list, you can choose from Ascending, Descending, or None. The Data Explorer is discussed in
Enabling hierarchy browsing in reports: Data Explorer, page 364
.
Additional examples on how you can use the Report Sort and
Browse Sort options to display attribute information are provided in
Using attributes to browse and report on data
• Geographical Role
: Defines how the attribute form can be used as geographical data with various MicroStrategy mapping features. When using Data Import, this type of geographical information can be automatically detected. For details on how Data Import is able to assign geographical roles to your data, see
Integrating geographical information with your imported data, page 74
.
• Shape File
: Defines the shapes used to display the attribute form on various MicroStrategy mapping features. For details on using shape files to define the display of attribute forms as part of the Image Layout widget, refer to the Dashboards and Widgets Creation Guide.
• Supports Multiple Languages
: Defines whether the attribute form’s information can be displayed in multiple languages using data internationalization. For information on defining attribute forms to allow data to be displayed in multiple languages, see
Supporting data internationalization for attribute elements, page 241
Default sorting of multiple attribute forms on reports
When creating attribute forms, you can define the default sort order for each attribute form on a report by selecting a Report Sort option as described in
Displaying forms: Attribute form properties
above.
If you define multiple attribute forms of an attribute with ascending or descending sort orders, the first attribute form with a default sort order is used to sort the attribute on the report. If the first attribute form with a default sort order is not included on a report, then the second attribute form with a default sort order is used for sorting, and so on.
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For example, the Customer attribute in the MicroStrategy Tutorial project has the five attribute forms shown below:
Of these five attribute forms, only Last Name has a default report sort order defined. Modify the Address form so that it has a descending report sort order. If you include Customer on a report with both Last Name and
Address, customers are sorted by their Last Name in ascending order. This is because Last Name was created first and therefore is considered for sorting before the Address form. If you remove the Last Name form from the report, customers are sorted by their address in descending order.
This is the default functionality for how attributes are sorted by their attribute forms on reports. It is important to note that you can change how attribute forms are sorted from within a report. In a report you can use advanced sorting to define how attribute forms, metrics, and other report objects are sorted. Sorting defined for a report takes precedence over default sorting defined for attribute forms. For more information on advanced sorting, refer to the MicroStrategy Advanced Reporting Guide.
Attribute form expressions
Attributes act like holders of information and provide context for fact data.
For example, the Customer attribute holds information about the customer such as Name and Address. These information units are called attribute forms. An
attribute form expression
defines what columns in the warehouse are used to represent the attribute form in SQL. Each attribute form must have at least one expression.
For example, the form expression for the Customer First Name attribute form is CUST_FIRST_NAME. The form expression for the Customer Last
Name attribute form is CUST_LAST_NAME. In this instance, the
CUST_FIRST_NAME and CUST_LAST_NAME columns in the warehouse provide information about first and last names respectively.
Although you can have multiple expressions in different tables, a form cannot have two different expressions in the same source table.
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You can create expressions using attribute columns, constants, and/or mathematical operators, for example, +, -, /, *. Only implicit attributes do not include a column in the expression, since they only use the constants you declare.
You can also create a form expression using Apply functions. These functions are discussed in the MicroStrategy Functions Reference.
The types of attribute form expressions are:
•
: Simple form expressions access data through columns in the data warehouse.
•
: Derived form expressions perform some
type of mathematical calculation on columns in the data warehouse to create an attribute form.
•
Joining dissimilar column names: Heterogeneous mappings, page 254
Heterogeneous mappings allow you to use columns with different names in the data warehouse as the same attribute form.
•
Implicit expressions, page 256
: Implicit form expressions do not relate
directly to data stored in the data warehouse. These form expressions create virtual data by combining or using columns to generate the data.
Simple expressions
A simple expression is based on a single warehouse column. The definition of the simple expression includes the tables in which the column is found.
For example, Category is an attribute in the MicroStrategy Tutorial. It has two forms, ID and Description, both of which are defined by simple expressions. The expression for the ID form is the CATEGORY_ID column and the expression for the description form is the CATEGORY_DESC column.
Both of these columns reside in the table LU_CATEGORY.
Example: creating an attribute form with a simple expression
A retailer begins a promotion that offers customers 25% off of their purchases if they fill out a feedback survey on the company website. The retailer intends to analyze the data gathered from the surveys to better market their products in the future.
The retailer’s customers provide a variety of information on the surveys, including their dates of birth. Once gathered, the date of birth data
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At this point, the project designer must add the column containing the customer dates of birth as an additional attribute form of the Customer attribute. This will enable report designers to display each customer’s date of birth alongside each customer’s name on reports.
Follow the procedure below to create Customer Birth Date as an attribute form in the Customer attribute.
You can also use Architect to create an attribute form with a simple
.
To create an attribute form with a simple expression
1
In MicroStrategy Developer, log in to the project source that contains the
MicroStrategy Tutorial project and then log in to MicroStrategy Tutorial.
2
Navigate to the Schema Objects folder, open the Attributes folder, and then the Customers folder.
3
Double-click the Customer attribute. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Attribute Editor in edit mode so that you can make changes to the attribute. The Attribute Editor opens.
Note the following:
•
If you are only given the option of opening the Attribute Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Attribute Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
Click New. The Create New Form Expression dialog box opens.
5
From the Source table drop-down list, select the LU_CUSTOMER table.
This is the table that contains customers’ dates of birth.
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6
Double-click the CUST_BIRTHDATE column to add it to the Form expression pane on the right. The mapping method is selected as
Automatic
by default.
7
Click OK. The Create New Attribute Form dialog box opens.
8
In the Form general information area, in the Name field, type Customer
Birth Date
.
9
From the Category used drop-down list, select DATE since Customer
Birth Date is neither the ID form of Customer nor the primary description form.
10
Click OK. The new Customer Birth Date attribute form is added to the
Attribute form pane in the Attribute Editor.
11
Because this is only an example, close the Attribute Editor without saving your changes.
Derived expressions
Derived expressions are created using a combination of warehouse columns, mathematical operators, functions, and constants. While simple expressions have their value determined by just one column in a warehouse table, derived expressions are defined using one or more columns as well as other operators and values. Any operation on a column (such as adding a constant, adding another column, or setting the expression to be an absolute value) creates a derived expression.
For example, you can create a
derived attribute
to calculate age or anniversaries. By calculating the difference between the columns Date of
Birth and Current Date, you can create an attribute to hold the age of a customer or an employee that has been derived from the two columns. By creating an attribute to calculate age in this manner, the attribute’s value is automatically updated as the age changes. If you created an attribute for age in which you included a constant number, the attribute would need to be updated every time a customer or an employee has a birthday.
Example: creating an attribute form with a derived expression
In your database, you store Customer names in two different columns,
CUST_FIRST_NAME and CUST_LAST_NAME. However, you want reports to display a customer’s first name and last name together as a single entry on a report. You can achieve this using a derived form expression Name, which
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The Context of Your Business Data: Attributes Project Design Guide consists of the two strings. Using the Customer attribute, the syntax of the derived expression for Name reads:
CUST_FIRST_NAME + “ “ + CUST_LAST_NAME
On a report, this information is displayed as Mary Jones under the Name column. As another example, you could create a derived expression for Name in the format of Last Name, First Name using the following syntax:
CUST_LAST_NAME + “, “ + CUST_FIRST_NAME
Using this expression, the information is displayed as Jones, Mary under the Name column.
Calculations and functions used in a derived expression can assist in deriving data from the database, but you must make sure you use expressions that meet the requirements of your database-specific SQL syntax. If you use syntax that is not supported by your database or other data source, the SQL query and resulting report can fail.
You can also use Architect to create an attribute form with a derived
Creating derived attribute form expressions, page 160
.
To create an attribute form with a derived expression
1
In MicroStrategy Developer, log in to the project source that contains the
MicroStrategy Tutorial project and then log in to MicroStrategy Tutorial.
2
Navigate to the Schema Objects folder, open the Attributes folder, and then the Customers folder.
3
Double-click the Customer attribute. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Attribute Editor in edit mode so that you can make changes to the attribute. The Attribute Editor opens.
Note the following:
•
If you are only given the option of opening the Attribute Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Attribute Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
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•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
Click New. The Create New Form Expression dialog box opens.
5
From the Source table drop-down list, select the LU_CUSTOMER table.
This is the table that contains customers’ first and last names.
6
Double-click the CUST_LAST_NAME column to add it to the Form expression pane on the right.
7
In the Form expression pane, place the cursor to the right of
[CUST_LAST_NAME] and type + “, “ +.
8
Double-click the CUST_FIRST_NAME column to add it to the Form expression pane on the right. Your expression should be defined as shown below.
9
Select Automatic as the mapping method.
10
Click OK. The Create New Attribute Form dialog box opens.
11
In the Form general information area, in the Name field, type Last
Name, First Name
.
12
From the Category used drop-down list, select None since Last Name,
First Name is neither the ID form of Customer nor the primary description form.
13
Click OK. The new attribute form is added to the Attribute form pane in the Attribute Editor.
14
Because this is only an example, close the Attribute Editor without saving your changes.
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Joining dissimilar column names: Heterogeneous mappings
Heterogeneous mapping allows Intelligence Server to perform joins on dissimilar column names. If you define more than one expression for a given form, heterogeneous mapping automatically occurs when tables and column names require it.
Because different source systems may store information in various contexts, your company may have multiple columns in different tables that all represent the same business concept. For example, in the MicroStrategy
Tutorial, the ID form of the attribute Day contains two expressions. The
Day_Date column occurs in the LU_DATE table and the Order_Date column occurs in the ORDER_DETAIL and ORDER_FACT tables.
In the above example, you can use heterogeneous mapping to map the Day attribute to all of the columns in the data warehouse that represent the same concept of Day. You can map Order_Date and Day_Date to the Day attribute. This ensures that both columns are used when information about the Day attribute is displayed on a report.
Each expression is linked to a set of source tables that contain the columns used in the expression. Of all the tables in which the columns exist, you can select as many or as few as you want to be used as part of the attribute’s definition.
In the Attribute Editor, you can view the chosen tables in the source
Tables area to the right of the Form Expressions area.
The data types of columns used in a heterogeneous mapping for a given attribute must be identical or similar enough for your particular
RDBMS to join them properly. For example, most databases cannot join a data type of Text to a data type of Number. However, depending on your database platform, you might be able to join columns with data types of Number and Integer.
You can also use Architect to create an attribute form with a heterogeneous column mapping, as described in
Joining dissimilar column names:
Heterogeneous mappings, page 162
.
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To create an attribute form with a heterogeneous column mapping
1
In MicroStrategy Developer, log in to the project source that contains the
MicroStrategy Tutorial project and then log in to MicroStrategy Tutorial.
2
Navigate to the Schema Objects folder, open the Attributes folder, and then the Time folder.
3
Double-click the Day attribute. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Attribute Editor in edit mode so that you can make changes to the attribute. The Attribute Editor opens.
Note the following:
•
If you are only given the option of opening the Attribute Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Attribute Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
Click New. The Create New Form Expression dialog box opens.
5
From the Source table drop-down list, select the LU_DAY table.
6
Double-click the DAY_DATE column to add it to the Form expression pane on the right. The mapping method is selected as Automatic by default.
7
Click OK. The Create New Attribute Form dialog box opens.
8
Click New. The Create New Form Expression dialog box opens.
9
From the Source table drop-down list, select the ORDER_DETAIL table.
10
Double-click the ORDER_DATE column to add it to the Form expression pane on the right. The mapping method is selected as Automatic by default.
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Click OK. The Create New Attribute Form dialog box opens.
Notice that there are now two expressions for the attribute form definition, both of which use different tables as the source of their information. You could continue this process to add as many heterogeneous columns as part of one attribute form as necessary.
12
In the Form general information area, in the Name field, type Date
Example
.
13
From the Category used drop-down list, select None since this is simply an example scenario.
14
Click OK. The new Date Example attribute form is added to the Attribute form pane in the Attribute Editor.
15
Because this is only an example, close the Attribute Editor without saving your changes.
Implicit expressions
While most attributes map directly to one or more physical columns in the warehouse, an
implicit attribute
is a virtual or constant attribute that does not physically exist in the warehouse. Such an attribute has an implicit expression, which is a constant value, although nothing is saved in an actual column. Implicit expressions are not defined by column names; they are defined by constants that you specify. Any constant is acceptable, for example, RushOrder=“Yes”. Some attribute definitions can be implied by the existence of a row in a certain table, rather than being defined in terms of columns. Implicit attributes are useful in analyzing and retrieving relevant information.
Implicit attributes are not commonly used, but are useful in special cases such as the scenario described below.
For example, the Rush Order attribute in MicroStrategy Tutorial is an example of an implicit attribute. This attribute can be used to display which orders are rush orders so you can better keep track of your shipments.
The Rush Order attribute is defined by the implicit expression “Y”, which stands for Yes. This implicit expression is based on an order’s existence in the RUSH_ORDER table, rather than being based on a column in a table. This
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ORDER_FACT table, the latter being the lookup table for the Order attribute.
The ORDER_FACT table includes more schema objects than are shown below, which are hidden as they are not important to this example.
In the RUSH_ORDER table shown above, the Order attribute and the Rush
Charge fact both display the table column that they use for their expressions.
You can identify Rush Order as an implicit attribute because it has no associated table column to define its implicit expression.
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The Tutorial project also includes a Rush Orders by Call Center report, which displays the Rush Order attribute on a report. For each order that is a rush order, a “Y” is displayed in the Rush Order column, as shown below.
In the report shown above, the Rush Charge is also displayed to show additional shipping costs that were charged to send the order as a rush order.
Modifying attribute data types: Column aliases
A column alias is a new data type that you can specify in place of the default data type for a given attribute form. Column aliases allow you to specify a more appropriate data type that can help avoid errors in your SQL.
They can also help you take more advantage of the data in your data warehouse. For attributes, a column alias performs the same function as it does for facts. By default, the data type for an attribute form is inherited from the data type of the column on which the form is defined. This inheritance is governed by MicroStrategy, which attempts to use a data type as similar as
possible to the data type in your database or other data source (see
for more information on how MicroStrategy selects a
matching data type). However, there are cases where you may need to change the data type. The following are some examples of such cases.
In your data warehouse you have a lookup table for an Accounts attribute where the ID is Account Number and the ID is stored in the database as
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DECIMAL(18, 0). Because this column stores high-precision values, you must modify the column alias for the attribute form and map it to a special data type, Big Decimal. By doing so, the precision can be preserved when performing filtering, drilling, or page-by on the Account attribute.
Another example could be a case in which your warehouse does not have a lookup table for year information, but you would like to create a Year attribute. Many database platforms have functions that can extract parts of a date from a Date data type. For example, SQL Server has a Year function that extracts just the year from a date. In such a case, you can create a Year attribute using the following form expression:
ApplySimple("Year(#0)",[Date_Id])
The ApplySimple expression above is syntactically correct for SQL
Server. However, depending on your database or data source type, you may need to use a different syntax.
The data type for this attribute is automatically set to a Date data type. This is because Date_ID is a Date data type. However, the result of the
ApplySimple calculation is an integer value that represents a given year, such as 2011.
When a temporary SQL table is created, if you do not change the data type of the column alias, the system uses a Date data type and tries to insert integer data into this column. While this does not create a problem in all database platforms, some databases will return an error. To avoid the possibility of an error due to conflicting data types, modify the column alias for the attribute form and change the default Date data type to an Integer data type.
In addition to specifying the data type to be used for an attribute form, the column alias also lets you specify the column alias name to be used in the
SQL generated by MicroStrategy. When you create a form expression using a custom expression or multiple columns (as discussed in
Attribute form expressions, page 248
), the column alias for the attribute form defaults to
CustCol (or CustCol_1, CustCol_2, and so on). The following piece of
SQL shows, in bold, where the column alias name is used:
SELECT Year(a12.Date_Id) CustCol_1, sum(a11.Tot_Dollar_Sales) WJXBFS1
FROM YR_CATEGORY_SLS a11 cross join TRANS_DATE_LW_LY a12
GROUP BY Year(a12.Date_Id)
While the column alias name does not affect the actual results or your report, you can change the column alias name to be more meaningful. The above
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You can use the Attribute Editor to create column aliases. You can also use
Architect to create column aliases, as described in
Creating and modifying attribute data types: Column aliases, page 164
Prerequisites
•
This procedure assumes you have already created an attribute with a valid attribute expression for which to create a new column alias.
To create a column alias for an attribute
1
In MicroStrategy Developer, log in to the project source that contains the attribute to create a new column alias for.
2
Right-click the attribute and select Edit. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Attribute Editor in edit mode so that you can make changes to the attribute. The Attribute Editor opens.
Note the following:
•
If you are only given the option of opening the Attribute Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Attribute Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
3
Select an attribute form and click Modify. The Modify Attribute Form dialog box opens.
4
Select the Column Alias tab.
5
In the Column alias area, click Modify. The Column Editor - Column
Selection dialog box opens.
6
Select New to create a new column alias. The Column Editor - Definition dialog box opens.
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You can modify the following properties for the column alias:
• Column name
: The name for the column alias which is used in any
SQL statements which include the attribute form column.
• Data type
: The data type for the attribute form. For a description of the different data types supported by MicroStrategy, see
.
•
Depending on the data type selected, you can specify the byte length, bit length, precision, scale, or time scale for your column alias.
8
Click OK to save your changes and return to the Column Editor - Column
Selection dialog box.
9
Click OK to save your changes and return to the Attribute Editor.
10
Select Save and Close to save your changes.
Attribute forms versus separate attributes
Attribute forms can be considered as additional descriptions for an attribute, whereas attributes themselves can be considered as report elements or group-by elements that have a one-to-many or a many-to-many relationship with other attributes. The data that you map to attributes can be represented as separate attributes or as an attribute form of an attribute.
In general, you should map data to an attribute form rather than a separate attribute if:
•
There is a one-to-one relationship between an attribute and the data.
•
You do not group by the data.
The decision to model data as an attribute form for a given attribute or as a separate attribute is usually determined during the logical data modeling phase of project design. For more information on whether to model data as an attribute form or as a separate attribute, see
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Attribute relationships
After you have created attributes for your project, you can define attribute relationships to define how the engine generates SQL, how tables and columns are joined and used, and which tables are related to other tables.
You link directly related attributes to each other by defining parent-child
relationships, as explained in
Attribute relationships, page 23
. Attribute elements, or the actual data values for an attribute, dictate the relationships that you define between attributes.
The parent-child relationships you create determine the system hierarchy within the project.
The implications of whether attributes are related become clear when you begin building reports. You can run a report with two attributes that are related without any problems. For example, it is easy to include Country and
City, on a report together. A report with two unrelated attributes, however, must include a metric based on a fact that is on or below the level of the two attributes, or else a Cartesian join occurs, which is generally undesirable. A
Cartesian join, or cross join, is very database intensive as every row in one table is joined to every row in the other table.
In MicroStrategy Developer, you can define relationships for the attributes in
your project. This step is covered in
Simultaneously creating multiple attributes, page 227
, as part of the initial project design effort and in
Viewing and editing the parents and children of attributes, page 264
, after a project has already been created.
Attributes can be either related or unrelated to one another:
• Related
: A parent-child relationship is defined between two or more related attributes. The relationship is defined through the attribute’s lookup table or a relationship table.
Four types of direct relationships can exist between related attributes, and these relationships are defined by the attribute elements that exist in the related attributes. Each type is described below:
One-to-one
: Each element in the parent attribute corresponds to one and only one element in the child attribute, and each child attribute corresponds to one and only one element in the parent attribute. A common example of a one-to-one relationship is citizen and taxpayer
ID. A citizen can have only one taxpayer ID and a taxpayer ID can be assigned to only one citizen.
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One-to-many
: Each element in the parent attribute corresponds to one or more elements in the child attribute, and each child attribute corresponds to one and only one element in the parent attribute.
These are the most common types of attribute relationships. Year has a one-to-many relationship to quarter. One year has many quarters, but a specific quarter can be in one year only. This assumes that quarters are defined with an accompanying year such as Q4 2006, Q1
2007, and so on.
Many-to-one
: Each element in the parent attribute corresponds to one and only one element in the child attribute, and each child attribute corresponds to one or more elements in the parent attribute.
Many-to-one relationships are the same type of relationship as a one-to-many, but it is defined from a different perspective. For example, year is described above as having a one-to-many relationship to quarter. This means that quarter has a many-to-one relationship to year.
Many-to-many
: Each element in the parent attribute can have multiple children and each child element in the child attribute can have multiple parents. In banking, customers and accounts are an example of a many-to-many relationship. One customer may have many accounts, and each account may be associated with many customers, such as in the case of a joint checking account.
Attributes can also be related to other attributes through a chain of attribute relationships. Attributes of this type are often in the same hierarchy. For example, a Geography hierarchy contains the attributes
Customer Region, Customer State, and Customer City:
In this scenario, Customer Region and Customer State are directly related to each other and Customer State and Customer City also have a direct relationship. While Customer City is not directly related to Customer
Region, these two attributes are related through Customer State. This allows you to include Customer Region and Customer City on a report and view the different customer cities for each customer region.
• Unrelated
: No parent-child relationship has been defined and the attributes are not related through a chain of attribute relationships. No relationship is present in the lookup tables or relationship tables for these attributes. Unrelated attributes can exist together in fact tables, giving context to the fact. For example, the Customer and Day attributes have no
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The Context of Your Business Data: Attributes Project Design Guide relationship to one another. A particular customer and a particular day only make sense together if a fact is associated with that combination. For example, a certain customer, Don Addison, spent $2,500 on January 5,
2003 on behalf of the health care company in which he works. In this case, care must be taken when using unrelated attributes on a single report. In general, however, these attributes are relatively straightforward to deal with from a project design perspective.
Viewing and editing the parents and children of attributes
The relationships that exist between attributes rely on the parent-child specifications that you assign to attributes. How attributes relate to one another and the types of relationships they share define the system hierarchy which is used to generate SQL. This SQL, in turn, determines the output of a report.
Parent-child relationships were designated when attributes were selected for the new project. However, you can continue to make changes to the relationships between attributes even after creating your project.
For example, the Distribution Center attribute is the parent of the Call Center attribute. A one-to-one relationship exists between Distribution Center and
Call Center. This means that only one call center exists in each distribution center. Country, in turn, is the parent of Distribution Center and multiple distribution centers exist in each country. So these two attributes have a one-to-many relationship.
Assigning parent-child relationships to attributes allows you to connect attributes to one another in user hierarchies, as discussed in
Hierarchies to Organize and Browse Attributes, page 347
report generates inaccurate SQL and results, viewing and changing parent-child relationships may be a necessary troubleshooting method.
Follow the procedure below to view and edit the parents and children of the
Distribution Center attribute.
You can also use Architect to define relationships between attributes.
Architect can provide a more intuitive and helpful workflow that allows you to view and modify multiple attributes as you define attribute relationships, as described in
Defining attribute relationships, page 174
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To view and edit the parents and children of an attribute
1
In MicroStrategy Developer, log in to the project source that contains the
MicroStrategy Tutorial project and then log in to MicroStrategy Tutorial.
2
Navigate to the Schema Objects folder, open the Attributes folder, and then the Geography folder.
3
Double-click the Distribution Center attribute. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Attribute Editor in edit mode so that you can make changes to the attribute. The Attribute Editor opens.
Note the following:
•
If you are only given the option of opening the Attribute Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Attribute Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
4
Click the Children tab. The Call Center attribute is listed, along with the relationship type it shares with Distribution Center, and the relationship table in which the relationship exists.
Consider a scenario in which multiple call centers now exist in the same distribution center so they no longer have a one-to-one relationship; in this case, you must change the relationship type between Call Center and
Distribution Center.
5
To change the relationship type, select One to Many from the
Relationship type
drop-down list.
6
You also want the relationship between the two attributes to be defined in the LU_Employee table instead of the LU_Call_Ctr table in which it is defined now. To change the relationship table, select the LU_Employee table from the Relationship table drop-down list.
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Click the Parents tab. The Country attribute is listed, along with the relationship type it shares with Distribution Center, and the relationship table in which the relationship exists.
From this Parents tab, you can view or modify the existing parent attribute relationships, as well as create or delete new parent attribute relationships.
8
Because this is only an example, close the Distribution Center attribute without saving your changes.
Supporting many-to-many and joint child relationships
Two forms of attribute relationships, many-to-many relationships and joint child relationships, can introduce additional complexity to the schema and warehouse design process. The following sections discuss the considerations you must make to ensure an effective warehouse design in light of the unique nature of these relationships.
While the topics are largely related to logical model design, a working knowledge of physical schemas is helpful when dealing with the challenges involved with these topics.
Before reading this section, you should know what logical data models and physical warehouse schemas are, and how to read and interpret them.
Logical data models and physical warehouse schemas are discussed in
Chapter 2, The Logical Data Model
Chapter 3, Warehouse Structure for
respectively. These chapters discuss how to plan and create a conceptual framework for your business intelligence data.
Many-to-many relationships
The presence of many-to-many relationships introduces complexity during the warehouse design process. With the presence of many-to-many relationships, you must make additional considerations to effectively plan your design.
Below are some real-life examples of many-to-many relationships which must be carefully handled in the data model and schema:
•
In a certain organization, each salesperson can work in more than one calling center. Likewise, each calling center has many salespeople.
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•
In a car manufacturing plant, many models of cars are produced, and each comes in several colors. That is, there are many colors for a single type of car, and many types of cars can be associated with the same color.
The following sections use the example of items and colors to demonstrate a many-to-many relationship and the options you have for dealing with them.
One item can come in many colors, such as red hats, blue hats, and green hats, and one color can be associated with many items, such as red dress, red hat, red shoes, and red socks.
Potential problems with many-to-many relationships usually come in the following forms, both of which can be avoided by correctly modeling the relationship:
•
Loss of analytical capability, page 267
•
Loss of analytical capability
With the color and item many-to-many relationship, there are usually two business questions for which users want answers:
1
In what colors are certain items available?
2
How much of a particular item/color combination was sold?
Answering the first question requires a table that contains a list of all possible item/color combinations. Recall that one-to-many relationships are usually in the child’s lookup table.
In many-to-many relationships this is not feasible. Rather, a distinct relationship table needs to be present in your warehouse. The following diagram shows the lookup and relationship tables for item and color:
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The Rel_Color_Item table provides a row for every possible item/color combination.
Answering the second question requires a fact table that has sales information as well as color and item information. The following diagram shows the same scenario as before, but in addition it shows a simple fact table containing sales data keyed by item, color, and date.
The fact table in the above diagram alone is not sufficient to answer the first question. Only item and color combinations that were actually sold, and therefore have sales recorded, can be retrieved from this table. If you have item and color combinations that are available but that have never been sold, this fact table cannot provide a complete list of item and color combinations to answer question one.
In summary, to prevent any loss of analytical flexibility when dealing with a many-to-many attribute relationship, the following requirements must be met:
•
A distinct relationship table to identify all the possible combinations of attribute elements between attributes
•
Both the attribute ID columns in the fact table
You can implement the above points in several different ways, which are
Working with many-to-many relationships, page 271
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Multiple counting
When dealing with many-to-many relationships, loss of analytical capability is only one challenge. Another equally significant issue is multiple counting.
Multiple counting occurs when all of the following takes place:
•
You attempt to aggregate data to the level of one of the attributes in the many-to-many relationship, or a higher level than one of the attributes in the many-to-many relationship.
•
The relationship exists in a distinct relationship table.
•
All of the attributes in the many-to-many relationship are not in the fact table.
Recall the example from above, but make the following change: remove color from the fact table.
Assume that there are three items, including hats, dresses, and socks. These items come in three colors, including red, blue, and green, with the exception
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The Context of Your Business Data: Attributes Project Design Guide of socks, which come in only green and blue. The following diagram shows this data in the lookup tables as well as some simple sales data:
The risk of multiple counting occurs when you run a query requesting the sales by color, effectively aggregating to the item attribute level in the many-to-many relationship. This query would require both the fact table, which has the sales information by item, and the relationship table, since color is not recorded in the fact table.
The difficulty lies in the fact that color is not in the fact table. There is no way to directly relate the sales of an item in the fact table to the color of that particular item. For example, instead of calculating the sales of red items, the query aggregates sales for all items that come in red according to the relationship table. The sum includes all hats and all dresses, including blue ones and green ones. This obviously leads to numbers that are higher than the true sales for red items.
For example, using the given data, the following questions cannot all be answered accurately:
•
What are the total sales for hats?
The answer is $35, which can be calculated directly from the fact table.
•
What are the total sales for red items?
You cannot determine an accurate answer. The answer you get is $85, which is the total for all hats and dresses, since socks do not come in red.
It is entirely possible that all the dresses sold are green; however, you cannot confirm this since color is not recorded in the fact table.
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•
What are the total sales for red dresses?
Again, you cannot determine an accurate answer. If all the dresses sold are indeed green, the correct answer is $0, but the answer you will get based on the data in the fact table is $50.
The following section describes several ways to prevent multiple counting when dealing with many-to-many relationships.
Working with many-to-many relationships
As you can see, seemingly simple questions can require you to take a number of steps to answer them when many-to-many relationships are involved.
You can use one of three techniques to provide physical support to answer the types of questions that cannot be answered accurately when using many-to-many relationships. The three techniques all have differing levels of flexibility, and flexibility is always a trade-off with complexity. In all cases, the two fundamental components remain in place in one form or another:
•
A relationship table to define the attribute relationship
•
Both the attribute’s ID columns in the fact table
MicroStrategy builds the rules that MicroStrategy SQL Engine uses to generate SQL when a report request is made. If you make both of the above physical implementations, the SQL Engine uses the related table when no metric is included on the report. When a metric is included, the fact table is used to answer the query.
All of the following methods require additional data in the fact table.
This means that you must capture the additional data in the source system. For example, you need to have data in the source system as to what the color is of each item sold. If this additional data was never captured in the source system, you cannot fully resolve the many-to-many relationship to calculate the amount of sales for items of a certain color.
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Method 1
This method is the most straightforward way to effectively manage many-to-many relationships.
Method 1 requires you to create a separate relationship table (in this case,
Rel_Color_Item) and add both attribute IDs to the fact table as shown in the following diagram.
Method 2
Method 2 eliminates the many-to-many relationship and the need for a distinct relationship table.
Here the many-to-many relationship is converted into a compound attribute relationship. You treat one attribute as a child of the other and have a compound key for the lower level attribute. Also, you add both attribute IDs, in this case Item_ID and Color_ID, to the fact table as shown in the following diagram.
While this method eliminates the need for a separate relationship table, you lose the ability to view items independent of color, or vice versa.
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Method 3
Method 3 is the most versatile solution and has the following characteristics:
•
Further simplifies the compound attribute relationship from Method 2 into a simple attribute relationship
•
Provides the ability to view item and color together or independently
•
Requires only one attribute column in the fact table for complete flexibility, rather than two
Here you must create a new attribute, lower in level than either Color or
Item. This attribute is essentially a concatenation of Color and Item, which gives it a one-to-many relationship between itself and each of its parent attributes. This is the SKU attribute, particularly common in retail data models or situations.
Finally, rather than including Color and Item in the fact table, you only need to include this new child attribute SKU, as shown in the following diagram.
This method is actually quite similar to Method 1. The major difference is that the distinct relationship table from Method 1 has an additional column,
SKU, which extends the relationship of each item and color combination into a single value. Consequently, you can use this single value in the fact table.
The major disadvantage of Method 3 lies in creating the new attribute if your business model does not already use a similar structure, as well as possibly adding complexity to the ETL process.
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Joint child relationships
Some attributes exist at the intersection of other indirectly related attributes.
Such attributes are called
joint children
.
Joint child relationships connect special attributes that are sometimes called cross-dimensional attributes, text facts, or qualities. They do not fit neatly into the modeling schemes you have learned about thus far. These relationships can be modeled and conceptualized like traditional attributes but, like facts, they exist at the intersection of multiple attribute levels.
Many source systems refer to these special attributes as flags.
Therefore, if flags are referenced in your source system documentation, these are likely candidates for joint child relationships.
Joint child relationships are really another type of many-to-many relationship where one attribute has a many-to-many relationship to two otherwise unrelated attributes. For example, consider the relationship between three attributes: Promotion, Item, and Quarter. In this case,
Promotion has a many-to-many relationship to both Item and Quarter, as shown in the following diagram.
An example of a promotion might be a “Red Sale” where all red items are on sale. A business might run this promotion around Valentine's Day and again at Christmas time.
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Supporting joint child relationships
One way to resolve a many-to-many relationship is to have a relationship table for the attributes involved in the many-to-many relationships. In this case, you might create two relationship tables, one to relate Promotion and
Item. The second relates Promotion and Quarter as shown in the following diagram.
These two tables are sufficient to answer questions such as:
•
What items have been in what promotions?
•
What quarters have had what promotions?
However, these tables are not sufficient to answer the following more detailed and insightful questions:
•
What items were in what promotions in a given quarter?
•
In what quarters was a certain item involved in a certain type of promotion?
To answer these questions, you must combine the two relationship tables, creating one table to relate all three attributes.
The relationship in the distinct relationship table must exist for a joint child relationship to be properly defined. However, it does not necessarily have to be in its own, distinct relationship table. Defining the relationship directly in the lookup table for the parent of the joint child—in this case, Promotion—would be fine. Alternatively, you can build the relationship directly into the fact table.
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In these examples, it is important to notice the relationship between the three attributes. The Promotion attribute is related to a particular
Item-Quarter pair, as opposed to it being related to Item and Quarter separately. This is the essence of a joint child relationship and is shown in the following diagram.
Notice that a joint child relationship can be one-to-many or many-to-many. The issues with many-to-many relationships, including loss of analytical capability and multiple counting, also apply to many-to-many joint child relationships.
If you have a joint child relationship in your data, it is important for you to define it in MicroStrategy so that you get the correct data for reports that use the parent attribute in a joint child attribute. This ensures that when you need to join the fact table to the parent attribute of a joint child relationship
(for example, to see sales by promotion) the join will always use both joint children rather than just one or the other.
Attributes that use the same lookup table:
Attribute roles
Attribute roles allow you to use the same data to define and support two separate attributes. Suppose you define two attributes that have the same data definition but play different roles in your business model. For example, in the following image, notice that the attributes Origin Airport and
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Destination Airport are defined using the same lookup table, LU_AIRPORT, and column, AIRPORT_ID.
Although it makes sense to see JFK as either an origin or destination airport on a report, it is understood that destination airport data differs from origin airport data. You need to support the logical concepts of an origin airport and a destination airport, but you do not want to create two separate lookup tables with identical data. Creating two separate lookup tables would create redundancy, and thus take up more storage space and be harder to maintain.
When multiple attributes are defined using the same lookup table and column, the attributes are essentially playing different
attribute roles.
How an attribute plays multiple roles depends on the specific attribute.
The Origin Airport and Destination Airport attributes share the same attribute forms, or various aspects about them, such as description, location, and so on. In the fact table, however, a separate column exists for each of
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The Context of Your Business Data: Attributes their roles; the fact columns are ORIGIN_AIRPORT_ID and
DESTINATION_AIRPORT_ID, as shown below.
Project Design Guide
If a report designer places both the Origin Airport and Destination Airport attributes on a report to obtain the number of flights that originated from
MIA and arrived at LGA, an empty result set is returned. This occurs because the SQL statement tries to obtain the description of an airport that is both
MIA and LGA at the same time (Airport_ID = "MIA" AND Airport_ID =
"LGA").
If you identify that one of your attributes needs to play multiple roles, you must create an attribute in the logical model for each of the roles, as
Specifying attribute roles, page 279
with attributes playing multiple roles returns correct data.
In the following diagram, State is another example of an attribute that can have two roles since it relates to both the Vendor and Store attributes. In one
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Project Design Guide The Context of Your Business Data: Attributes 7 case, it refers to the location of a vendor. In the other, it refers to the location of a store. The State attribute is therefore said to be playing two roles.
In an OLTP system, roles are most often implemented as a single table, as shown in the above diagram. In the data warehouse, a query involving both
Vendor State and Store State needs to use the State table twice in the same query. For example, a report is created to display vendors from Arkansas who sold to New York stores. The results may be blank if the data warehouse structure was set up incorrectly. The SQL statement tries to obtain the description of a state that is both Arkansas and New York simultaneously, generating the empty result set.
Specifying attribute roles
To see both roles on the same report, you must treat them as different attributes. That is, they must have different attribute names. To create unique attributes, you have the following options:
• Automatic attribute role recognition
, where you create multiple attributes that have the same lookup table and allow MicroStrategy to automatically detect the multiple roles. Automatic recognition is enabled by the VLDB property Engine Attribute Role Options at the database instance level. For more information on VLDB properties, refer to the
Supplemental Reference for System Administration.
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• Explicit table aliasing
, where you create multiple logical tables pointing to the same physical table and define those two logical tables as the lookup tables for the two attributes.
In a MicroStrategy project in which automatic attribute role recognition is enabled (meaning that the database instance-level
VLDB property, Engine Attribute Role Options, is enabled), you can have a maximum of 99 attributes defined on the same column of the same lookup table. If you create more than this number of attributes, you encounter an error, and are unable to update the project schema or restart Intelligence Server.
Table aliasing provides advanced users with more control. If you are upgrading or have a very complex schema, it may be the better alternative. If you are new to MicroStrategy, however, it is easier to use automatic attribute role recognition. MicroStrategy recommends that you take advantage of automatic role recognition if you do not know the details of the modeling logic or the database.
Automatic recognition does not work if the attributes are in the same hierarchy, meaning that a child attribute is shared. For example, in the State example provided above, the two State attributes do not have a common child attribute.
In summary, if you identify that any one of your attributes needs to play multiple roles, an attribute must be created in the logical model for each of the roles. Remember this rule to help you identify attribute roles: If you want to see the same attribute multiple times on one report, as Ship Month and
Order Month, for example, the attribute has multiple roles. In this example,
Month is the attribute that has multiple roles. You can use either automatic attribute role recognition or explicit table aliasing to create the attribute roles.
Using automatic attribute role recognition
In the data warehouse, a query involving both Vendor State and Store State needs to use the State table twice in the same query to get correct results. You can set up two attributes, Store State and Vendor State, both of which use the
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Project Design Guide The Context of Your Business Data: Attributes 7 same lookup table. The resulting SQL code contains a self-join with the
LU_State table. The logical model would look like the following:
Note that both roles for the State attribute are included in the logical model so that “State” can be considered from two different perspectives. Since the state in which a vendor resides and the state in which one of the stores is located are two different things, the logical model must reflect that.
Automatic recognition allows these two attributes, Vendor State and Store
State, to access the same lookup table, using different attribute names for the same expression.
Automatic role recognition works only when the attributes use exactly the same expression, which in most cases simply represents a column or columns in a lookup table.
Consider the following sample desired report:
In this case, the request is, “Show me total sales by Store State for all my vendors in Arkansas (Store State ID = 15).” The same lookup table,
LU_State, can be used for both attributes, Store State and Vendor State, if attribute roles are used. The two attributes refer to the same columns of that table.
To use automatic attribute role recognition, you must select the Engine
Attribute Role Options, found in the database instance-level VLDB
Properties under Query Optimization. See the MicroStrategy Developer
Help (formerly the MicroStrategy Desktop Help) or the Supplemental
Reference for System Administration for steps to set this VLDB property.
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Explicitly aliasing tables to specify attribute roles
Explicit table aliasing provides more robust functionality than automatic recognition, so advanced users are encouraged to take advantage of this solution.
An attribute such as State can play more than one role in the data warehouse; it can represent the Vendor State or the Store State. In this case, the State attribute is said to play two roles: it refers to both the location of a vendor as well as the location of a store.
When you use explicit table aliasing to designate attributes that have multiple roles, both roles for the State attribute are included in the logical model so that State can be considered from two different perspectives. The logical model would look like the following, just as it would if you used automatic recognition:
The difference between automatic recognition and explicit table aliasing is that when you use explicit table aliasing, you create separate lookup tables in the schema, but point them each to the same physical table.
If you use explicit table aliasing for the Store attribute, one table
(LU_STATE_STORE) contains the attribute Store State while the other
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(LU_STATE_VENDOR) contains Vendor State, as shown in the following diagram.
Consider the following sample desired report that should provide data about the total sales by Store State for all vendors in Arkansas (Store State ID = 15):
When explicit table aliasing is used, the two lookup tables LU_STATE_STORE and LU_STATE_VENDOR are used. Since they are just different names for the same physical table, the report accesses the same physical table, LU_STATE, for both state names, as shown by this sample SQL:
SELECT a12.State_Desc as State_Desc
SELECT a13.State_Desc as State_Desc
FROM LU_STATE a12
LU_STATE a13
When you create a table alias, the selected table is copied, allowing you to rename a copy of the same table. When you are ready to create new attributes—as in the example discussed above—you can map the appropriate table to each attribute. In the case above, you would select the
LU_STATE_STORE table for the Store State attribute and
LU_STATE_VENDOR for Vendor State.
Table aliases are one kind of logical table. For information about
.
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You can also use Architect to create attribute roles with explicit table aliasing, as described in
Specifying attribute roles: Attributes that use the same lookup, page 170
To create attribute roles with explicit table aliasing
This procedure provides steps to re-create the example of explicit table aliasing described in this section. The LU_STATE table is not included in the
MicroStrategy Tutorial project. However, you can use the same high-level procedure and concepts as guidelines to create attribute roles in your project setup.
1
In MicroStrategy Developer, log in to the project to create attribute roles with explicit table aliasing.
2
Navigate to the Schema Objects folder, and then select the Tables folder.
3
Right-click the LU_STATE table and select Create Table Alias. An
LU_STATE(1) table is created.
4
Right-click LU_STATE(1), select Rename, and rename the table as
LU_STATE_STORE
.
5
Right-click the LU_STATE table and select Create Table Alias. An
LU_STATE(1) table is created.
6
Right-click LU_STATE(1), select Rename, and rename the table as
LU_STATE_VENDOR
.
Create the attributes
7
Select the Attributes folder.
8
From the File menu, select New, and then Attribute. The Create New
Form Expression dialog box opens.
9
From the Source table drop-down list, select LU_STATE_STORE.
10
In the Available columns pane, double-click STATE_ID, which identifies the attribute role.
11
Select Manual mapping and click OK. The Create New Attribute Form dialog box opens.
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12
From the Source table drop-down list, select the same
LU_STATE_STORE table.
13
Click OK. The Attribute Editor opens.
14
Click New to create any other required attribute forms for the State Store attribute, such as a description attribute form. You must make sure to map any State Store attribute forms to columns from the
LU_STATE_STORE table.
15
Save the State Store attribute once you are finished mapping attribute forms to columns of the LU_STATE_STORE table.
16
Create a Vendor State attribute with the same sub-procedure (
Create the attributes, page 284
) used to create State Store above, except you must
use the LU_STATE_VENDOR table instead of the LU_STATE_STORE table.
Attributes with multiple ID columns: Compound attributes
A
compound attribute
is an attribute with multiple columns specified as the
ID column. This implies that more than one ID column is needed to uniquely identify the elements of that attribute. Generally, you create a compound attribute when your logical data model reflects that a compound key relationship is present. In the relational database, a
compound key
is a primary key that consists of more than one database column.
For example, a retail project has two attributes, Class and Item. Class is the parent of Item and has a one-to-many relationship with it. The values in the
Item_ID column do not uniquely identify an item. The item shirt has an
Item_ID of 1. However, there are different shirts, depending on the class, which includes men’s, women’s, and children’s. Therefore, to uniquely identify a man’s shirt, Item_ID and Class_ID must be grouped together, creating a compound attribute.
All of the ID forms of the compound attribute should be within the same lookup table.
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Example: Creating compound attributes
You must create a compound attributes when an attribute requires two or more columns to uniquely identify its elements. In the MicroStrategy
Tutorial project, Distribution Center is an example of a compound attribute.
To uniquely identify a distribution center, one must know two details about the distribution center: the ID of the distribution center and the country in which it exists. This data is represented by the Dist_Ctr_ID and
Country_ID columns respectively. The same Distribution Center identification numbers can exist for different distribution centers, but in the same country, each distribution center has a unique identification number.
Therefore, both the Country_ID and Dist_Ctr_ID columns must be grouped together for the Distribution Center attribute. This ensures that data about distribution centers is displayed correctly and completely on a report.
To support this functionality, a form group must be created. A
form group
is a grouping of attribute forms to create a compound attribute.
Follow the procedure below to create the Distribution Center compound attribute.
You can also use Architect to create compound attributes, as described in
Creating attributes with multiple ID columns: Compound attributes, page 166
.
To create the Distribution Center compound attribute
1
In MicroStrategy Developer, log in to the project source that contains the
MicroStrategy Tutorial project and then log into MicroStrategy Tutorial.
2
Navigate to the My Personal Objects folder, and open the My Objects folder.
3
From the File menu, select New, and then Attribute. The Attribute Editor opens, with the Create New Form Expression dialog box displayed on top of it.
4
From the Source table drop-down list, select the LU_DIST_CTR table.
This is the table in which the two ID columns of Distribution Center reside.
5
Double-click the COUNTRY_ID column to add it to the Form expression pane on the right.
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6
Select Automatic mapping and click OK. The Create New Attribute Form dialog box opens.
7
In the Form general information area, in the Name field, type Country
ID
.
8
Keep all other defaults, and click OK.
9
In the Attribute Editor, click New to create the other attribute ID form.
This attribute form maps to the distribution center ID column necessary to complete the definition of the Distribution Center attribute.
10
Double-click the DIST_CTR_ID column to add it to the Form expression pane on the right.
11
Select Automatic mapping and click OK. The Create New Attribute Form dialog box opens.
12
In the Form general information area, in the Name field, type
Distribution Center ID
.
13
In the Form category section, from the Category used drop-down list, select ID. Click OK.
You must designate this attribute form as an ID column so that it can be combined with the Country_ID form to create one unique identifier ID for the Distribution Center attribute.
Create a form group
14
A dialog box notifies you that another form (in this case, COUNTRY_ID) is already using the ID form category and that you must create a form group to combine the two ID columns. Click Yes. The Create New Attribute
Form dialog box opens.
15
In the Name field, type Distribution Center and click OK. The Attribute
Editor opens, with the form group you created in the Attribute forms pane.
16
Because this is only an example, close the Distribution Center attribute without saving your changes.
If you create a compound attribute, you must update the schema to see your changes in the project. Close all editors. Then, from the Schema menu, select Update Schema.
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Using attributes to browse and report on data
Once attributes are built, they are used in two primary ways, browsing and reporting. Users browse through attributes to locate an attribute to use on a report, and users place an attribute on a report to display details about the particular attribute and how it relates to fact data. Each attribute can be displayed in a variety of forms so you must specify the default display of each of the attributes in the project. You can do this on a report-by-report basis, but you still must specify the global, or project-wide, default for each attribute.
You must choose a default attribute display for browsing and another for reporting. Report display forms are the attribute forms that appear as columns in a completed report.
Browse forms
are the attribute forms that appear as a user browses through the element list of an attribute in the Data
Explorer in a project. Therefore, browse forms identify attribute elements.
This separation allows for greater attribute display flexibility depending on the application.
The browse forms of the attribute are also used to display the attribute elements in the prompt details auto text code of a Report Services document.
For information on creating Report Services documents, see the Report
Services Document Creation Guide.
When creating attributes, all forms are included as report display forms and browse forms by default. The only exception is if you create multiple attribute forms, the first form you create is not included as a report display or browse form.
An attribute’s report display forms determine which attribute forms are displayed by default when the report is executed. By selecting different forms for the attribute, you select a different set of values for display. For example, a report includes Region as an attribute. If ID is selected as the attribute form, the display could be a number such as four. If Description is selected as the attribute form, the display could be a name, such as Northwest. If a report lists the cities in which you have stores, then you might choose to display the Long Description form, such as Chicago, instead of the URL attribute form, that is,
www.chicago.com
.
You can also select which attribute forms are retrieved with the report results but not displayed on the grid. These browse forms are found in the Report
Objects pane. In Grid view, you can add the attribute forms in Report Objects to the report without re-executing the report. For example you can include a cities URL attribute form as a browse attribute form so that your users can choose to display the form on a report.
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To modify the attribute forms displayed, you can:
•
Right-click an attribute on a report or template and select the desired attribute forms
•
From the Data menu, select Attribute Display to open the Attribute
Display dialog box
For steps to display attribute forms on a report or template, see the online help and the section below.
Defining how attribute forms are displayed by default
You can generally display attribute forms in a number of ways. For example, the Distribution Center attribute in the MicroStrategy Tutorial consists of an
ID form group and a Description form. The ID form group is made up of two separate ID columns, Country_ID and Dist_Ctr_ID.
Displayed on a report, the Dist_Ctr_ID form shows the identification numbers of specific distribution centers in the data warehouse. The
Description form of Distribution Center, however, displays the actual name of the Distribution Center such as “San Diego.”
You can use the Attribute Editor to determine how the attribute forms are displayed on a report. In the case of the Distribution Center attribute, you can specify whether the identification number of each distribution center, the distribution center names, or both are displayed. You can also determine which attribute forms are displayed when browsing a project with the Data
Explorer.
Follow the example procedure below to set one of the Distribution Center attribute’s forms to be displayed in reports and while browsing the
MicroStrategy Tutorial project.
You can also use Architect to define how attribute forms are displayed in reports, as described in
Modifying how to use attributes to browse and report on data, page 168
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To display an attribute form in reports and in the Data Explorer
1
In the MicroStrategy Tutorial, navigate to the Schema Objects folder, open the Attributes folder, and then the Geography folder.
2
Double-click the Distribution Center attribute. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Attribute Editor in edit mode so that you can make changes to the attribute. The Attribute Editor opens.
Note the following:
•
If you are only given the option of opening the Attribute Editor in read only mode, this means another user is modifying the project’s schema. You cannot open the Attribute Editor in edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
3
Click the Display tab.
On the right, in the Report display forms pane, the description form of the Distribution Center is set as the only display form. This means that, when the Distribution Center attribute is used on a report, the actual names of the distribution centers are displayed. The ID 2 form in the
Available forms pane represents the distribution centers’ identification numbers.
4
You can set the ID 2 form to be displayed in the following ways:
•
To set the ID 2 form as a form that is displayed on a report by default:
Select ID 2 from the Available forms pane and click the top > button to add the form to the Report display forms pane on the right.
•
To set the ID 2 form so it is displayed in the Data Explorer when a user browses the Distribution Center attribute: Select ID 2 from the
Available forms pane and click the bottom > button to add the form to the Browse forms pane on the right.
The Data Explorer makes hierarchies available for users to facilitate
placing objects on reports. The Data Explorer is discussed in
Enabling hierarchy browsing in reports: Data Explorer, page 364
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5
You can also define the default sort order for attributes on reports and the
Data Explorer. For information on attribute form sorting options, see
Displaying forms: Attribute form properties, page 246
6
Because this is only an example, close the Attribute Editor without saving your changes.
You can also determine how attributes are displayed while users are editing and viewing reports. See the MicroStrategy Basic Reporting Guide for details.
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8.
O
PTIMIZING AND
M
AINTAINING
Y
OUR
P
ROJECT
Introduction
Once your MicroStrategy project is set up and populated with schema objects, you are ready to start thinking about ways to better maintain the project and optimize it for both the short and long term.
This chapter introduces you to maintenance and optimization concepts such as tuning the interaction between your data warehouse and your project, creating aggregate tables, and using partition mapping, and explains how to use these methods to enhance your project. You can find this information in the sections listed below:
•
Updating your MicroStrategy project schema, page 294
—As you continue to enhance the design and functionality of your project, you will need to make various schema changes. To see any enhancements and changes to your project schema, you must update your project schema.
•
Data warehouse and project interaction: Warehouse Catalog, page 296
—As your data warehouse changes to meet new data logging
requirements, your project must reflect these changes. This can include adding new tables to your project or removing tables that are no longer used. You can also tune the interaction between your data warehouse and
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•
Accessing multiple data sources in a project, page 318
MultiSource Option in Intelligence Server, you can connect a project to multiple relational data sources. This allows you to integrate all your information from various databases and other relational data sources into a single MicroStrategy project for reporting and analysis purpose.
•
Improving database insert performance: parameterized queries, page 331
— MicroStrategy’s support for parameterized queries can
improve performance in scenarios that require the insert of information into a database.
•
Using summary tables to store data: Aggregate tables, page 333
—Aggregate tables store data at higher levels than the data was originally collected in the data warehouse. These summary tables provide quicker access to frequently-used data, reduce input/output and other resource requirements, and minimize the amount of data that must be aggregated and sorted at run time.
•
Dividing tables to increase performance: Partition mapping, page 341
—Partition mapping involves the division of large logical tables into smaller physical tables. Partitions improve query performance by minimizing the number of tables and records within a table that must be read to satisfy queries issued against the warehouse.
Updating your MicroStrategy project schema
All of the schema objects—facts, attributes, hierarchies, transformations, and so on—in your project come together to form your project’s schema.
Although the concepts are related, the project schema is not the same as the physical warehouse schema. Rather, the project schema refers to an internal map that MicroStrategy uses to keep track of attribute relationships, fact levels, table sizes, and so on within the project.
Whenever you make any changes to a schema object you must indicate to
MicroStrategy that new schema object definitions have been included and that these definitions need to be loaded into memory.
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You can do any of the following to update your project schema:
•
Stop and restart MicroStrategy Intelligence Server, if in server-connected
(3-tier) mode.
•
Disconnect and reconnect to the project or the project source, if in direct
(2-tier) mode.
•
Manually update the schema.
Manually updating the schema allows you to determine which specific elements of the schema are updated.
To manually update the schema
1
In MicroStrategy Developer, from the Schema menu, select Update
Schema
.
2
In the Schema Update dialog box, select or clear the following check boxes:
• Update schema logical information
: Use this option if you added, modified, or deleted a schema object.
• Recalculate table keys and fact entry levels
: Use this option if you changed the key structure of a table or if you changed the level at which a fact is stored.
• Recalculate table logical sizes
: Use this option to use MicroStrategy
Developer’s algorithm to recalculate logical table sizes and override any modifications that you have made to logical table sizes.
Logical table sizes are a significant part of how the
MicroStrategy SQL Engine determines the tables to use in a query.
• Recalculate project client object cache size
: Use this option to update the object cache size for the project.
3
Click Update.
You can also update the schema with the last saved settings by clicking the Update Schema icon in the toolbar.
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Data warehouse and project interaction:
Warehouse Catalog
This section discusses how the Warehouse Catalog can control the interaction between the data warehouse and the database instance for a project. The Warehouse Catalog queries the data warehouse and lists the tables and columns that exist in it. From this list, you can select the tables to add to your project. Every project can have a unique set of warehouse tables.
You can add warehouse tables to your project with the Warehouse Catalog or
Architect. The Warehouse Catalog is well-equipped at maintaining the warehouse tables used for an existing project. For information on Architect, see
Chapter 5, Creating a Project Using Architect
.
This section also discusses customizing catalog SQL statements, the structure of the SQL catalogs, and the default SQL statements used for each database.
This section covers the following topics:
•
Before you begin using the Warehouse Catalog, page 297
•
Accessing the Warehouse Catalog, page 297
•
Adding and removing tables for a project, page 297
•
Managing warehouse and project tables, page 299
•
Modifying data warehouse connection and operation defaults, page 304
•
Customizing catalog SQL statements, page 311
•
Troubleshooting table and column messages, page 317
Note the following:
•
You can also add tables to a project using MicroStrategy Query
Builder. For more information on Query Builder, see the
MicroStrategy Advanced Reporting Guide.
•
You can connect to MDX Cube sources such as SAP BI, Hyperion
Essbase, and Microsoft Analysis Services instead of a relational database. In this case, the MDX Cube Catalog handles tasks similar to the Warehouse Catalog. For more information, refer to the MicroStrategy MDX Cube Reporting Guide.
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Before you begin using the Warehouse Catalog
Before you begin using the Warehouse Catalog, you need to be familiar with:
•
Your schema, so you know how the information in your data warehouse should be brought into MicroStrategy
•
How to create a project
Accessing the Warehouse Catalog
To access the Warehouse Catalog
1
On the Windows Start menu, point to Programs, then to MicroStrategy
Products
, and then select Developer.
2
Log in to the project source that contains your project in MicroStrategy
Developer, and expand the project source to select your project.
You must use a login that has Architect privileges. For more information about privileges see the List of Privileges chapter of the Supplemental Reference for System Administration.
3
If you are using read only mode for the project, from the Schema menu, clear the Read Only Mode option to switch to edit mode.
Only one user can be editing a project at a given time. Therefore, if someone else is modifying the project, you cannot use the Warehouse
Catalog. For more information on read only mode and edit mode, see
Using read only or edit mode for schema editors, page 99
.
4
From the Schema menu, select Warehouse Catalog. The Warehouse
Catalog opens after it retrieves the table information from the warehouse database.
Adding and removing tables for a project
As you become aware of the additional needs of report designers and users, it may become necessary to add additional tables from the data warehouse to your project. Also, as your project matures, you may need to remove tables
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Optimizing and Maintaining Your Project Project Design Guide from your project that are no longer used and are taking up space in the metadata.
You can access the Warehouse Catalog at any time to add additional tables from your data warehouse to your project and remove tables from your project.
For information on removing tables from a project that were removed from a
Removing tables from the Warehouse Catalog that have been removed from their data source, page 302
To add or remove tables after creating a project
1
Access the Warehouse Catalog for your project as described in
To access the Warehouse Catalog, page 297
. Log in to the project source that
contains your project in MicroStrategy Developer, and expand your project.
2
The left side of the Warehouse Catalog lists all available tables and the number of rows each table contains. The list on the right shows all the tables already being used in the project:
• To add tables
: From the left side, select the tables you want to add to the Warehouse Catalog, and click > to add the selected tables. Click >> to add all the listed tables.
• To remove tables
: From the right side, select the tables you want to remove from the Warehouse Catalog, and click < to remove the selected tables. Click << to remove all the listed tables.
•
If you have the MultiSource Option, you can add tables from multiple data sources into your project. For information on adding tables from multiple data sources into your project with the Warehouse Catalog, see
Accessing multiple data sources in a project, page 318
.
3
In the toolbar, click Save and Close to save your changes to the
Warehouse Catalog. The table definitions are written to the metadata.
This process can take some time to complete.
4
Update the project schema from the Schema menu, by selecting Update
Schema
.
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Managing warehouse and project tables
The Warehouse Catalog allows you to view tables that have been included in the project, as well as those tables that are available in the warehouse but have not been included in the project. To access the Warehouse Catalog for a project, see
Accessing the Warehouse Catalog, page 297
.
As you make changes to the tables in the warehouse, you need to periodically load the updates into the Warehouse Catalog. You can update it by selecting
Read the Warehouse Catalog
from the Actions menu.
The Warehouse Catalog has the following sections:
• Select current database instance
: From the drop-down list, select the database instance for the data source to view tables for. This option is available as part of the MultiSource Option, which allows you to access multiple data sources in a project, as described in
Accessing multiple data sources in a project, page 318
• Tables available in the database instance:
Displays tables that are located in the data source for the selected database instance, but have not been included in the project. You can add tables to the project by double-clicking the tables or by selecting the tables and then clicking >.
• Tables being used in the project:
Displays tables that have been selected to be part of the project. You can remove tables from the project by double-clicking the tables or by selecting the tables and then clicking
<
.
You can add or remove all the tables from one section to the other by clicking << and >> buttons.
Warehouse Catalog has the following menu options.
Menu
File
• Save
• Exit
Tools
• View Partitions
Description
Saves the current settings and status of the Warehouse Catalog.
Exits the Warehouse Catalog.
• Table Structure
Displays the list of tables referred to by the selected partition mapping table in the Table Partitions dialog box. This option is enabled when a partition mapping table is selected.
Displays the structure of a table selected in the Warehouse Catalog.
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Menu
• Calculate Table Row
Count
• Table Prefix
• Table Database
Instances
• Import Prefix
• Options
Description
Calculates the number of rows in the selected tables.
Allows you to add or remove a table prefix for the selected table.
This option allows you to support one of the following:
• MicroStrategy allows you to specify a secondary database instance for a table, which is used to support database gateways. For information on supporting database gateways, see
Specifying a secondary database to support database gateways, page 303
• If you have the MultiSource Option, you can add tables from multiple data sources into your project. For information on adding tables from multiple
data sources into your project with the Warehouse Catalog, see
Accessing multiple data sources in a project, page 318
.
Allows you to import the prefixes from the warehouse table name space.
Allows you to specify various settings for the Warehouse Catalog such as changing the database instance, changing or assigning default table prefixes and structures, automatic mapping, row calculation, and so on.
Actions
• Read the Warehouse
Catalog
Help
Allows you to update and reflect the changes done to tables in the warehouse.
Displays MicroStrategy help options
Some of these options are also available through toolbar buttons and through right-click menus for quick access.
Viewing table structure
To view the table structure of a table, right-click any table in the Warehouse
Catalog (see
Accessing the Warehouse Catalog, page 297
Structure
from the shortcut menu. You can also select Table Structure from the Tools menu. The table structure of the selected table is displayed in the dialog box.
The dialog box displays the columns available in the selected table and the data type of each column. You can also click Update Structure to reflect any recent changes done to that table (see
Updating table structure, page 301
When the data type of one or more columns is modified, you get a warning message of this change, which provides the following options:
•
Click OK to apply the change to this column in all the tables it appears.
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•
Click Cancel to undo all data type changes. This action results in no changes being applied to any tables or columns.
The warning message appears only if you have selected the Display a
warning if the columns data types are modified when updating the table structure
option in the Warehouse Catalog Options dialog box. This option is selected by default.
Updating table structure
Whenever the structure of the warehouse table changes you have to update the table structure in the Warehouse Catalog for the changes to reflect in the
MicroStrategy system. Some examples of these type of changes are when you add, delete, or rename a column in a table associated with a project.
To update the structure of a table
1
Access the Warehouse Catalog for your project (see
). The Warehouse Catalog opens.
2
In the Tables being used in the project list, right-click the table that has changed and select Update Structure.
If the data type of one or more columns is modified, you receive a message warning of this change. Verify the changes from the information dialog box that opens and click OK to apply the change in this column to all the tables in which it appears.
3
Click Save and Close to close the Warehouse Catalog dialog box.
•
If no object definitions have changed, the warehouse structure gets updated completely with the Update Structure command. For example, this would apply if you rename a column in the table and the column is not being used in any fact expression.
•
If any of the object definitions have changed, the table structure is only partially updated with the Update Structure command. Then, you have to manually update the schema objects that depend on the outdated structure.
For example, if you rename a column in a table, you have to manually update the facts that use this column. The procedure for manually updating the fact is as follows: a Right-click the fact and select Edit. The Fact Editor opens.
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Optimizing and Maintaining Your Project Project Design Guide b Select the fact expression and click Modify. The Modify Fact
Expression dialog box opens. c From the list of source tables select the source table from which the fact has been created. Edit the fact expression and click OK.
You are returned to the Fact Editor.
d Click Save and Close to save the changes and close the Fact
Editor.
e From the Schema menu, select Update Schema. The Schema
Update dialog box opens. f Click Update.
g Repeat the first two steps of this procedure to open the Warehouse
Catalog and update the table structure.
h Click Save and Close to save the changes and close the
Warehouse Catalog dialog box.
Viewing sample data
To view sample data from a table, right-click a table in the Warehouse
Accessing the Warehouse Catalog, page 297
) and choose Show
Sample Data
from the shortcut menu. You can also select Show Sample
Data
from the Tools menu. The first 100 rows of the table are returned as sample data in the Values dialog box.
To refresh the table data, click Reload table values.
Removing tables from the Warehouse Catalog that have been removed from their data source
When tables that are included in a project are removed from the data source that they were available in, you can use the Warehouse Catalog to remove these tables from the list of tables included in the project. This allows you to view an accurate list of tables that are included in the project from the selected data source.
The steps below show you how to perform this task using the Warehouse
Catalog. To remove these tables using MicroStrategy Architect, see
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Removing tables from a project that have been removed from a data source, page 130
If tables that were not included in a project are removed from the data source, these tables are automatically removed from the display of available tables in the Warehouse Catalog.
To remove the display of project tables that have been removed from the data source
1
In MicroStrategy Developer, log in to a project.
2
From the Schema menu, select Warehouse Catalog. The Warehouse
Catalog opens.
3
From the Warehouse Catalog toolbar, click Check for deleted catalog
tables
. The Deleted Catalog Tables dialog box opens.
4
Select the Delete check box for a table to remove it from the Tables being used in the project pane.
5
After you have selected all the tables to delete, click OK to return to the
Warehouse Catalog.
6
From the Action menu, select Read the Warehouse Catalog. All tables that were selected to be deleted in the Deleted Catalog Tables dialog box are removed from the Tables being used in the project pane.
7
Click Save and Close to save your changes and close the Warehouse
Catalog.
Specifying a secondary database to support database gateways
MicroStrategy allows you to specify a secondary database instance for a table, which is used to support database gateways. For example, in your environment you might have a gateway between two databases such as an
Oracle database and a DB2 database. One of them is the primary database and the other is the secondary database. The primary database receives all
SQL requests and passes them to the correct database. From the perspective of MicroStrategy products in this environment, you need to define two database instances, one for the primary database and another for the secondary database. The default database instance for the project is set to be
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This way, MicroStrategy products know how to generate SQL for each table.
If you use database gateway support, you cannot use the MultiSource Option feature to add tables from multiple data sources into your project. For information on adding tables from multiple data sources into your project
with the Warehouse Catalog, see
Accessing multiple data sources in a project, page 318
.
To specify a secondary database for a table
1
Access the Warehouse Catalog for your project (see
). The Warehouse Catalog opens.
2
Right-click a table being used in the project, (in the pane on the right side) and select Table Database Instances. The Available Database
Instances dialog box opens.
3
In the Primary Database Instance drop-down list, select the primary database instance for the table.
4
Select one or more Secondary Database Instances.
You cannot select the primary database instance as a secondary database instance.
5
Click OK to accept your changes and return to the Warehouse Catalog.
6
From the toolbar, select Save and Close to save your changes and close the Warehouse Catalog.
Modifying data warehouse connection and operation defaults
You can specify various settings for data warehouse connection and operation defaults using the Warehouse Catalog. Example settings include changing the database instance, changing or assigning default table prefixes and structures, automatic mapping, row calculation, and so on. The settings are available from the Warehouse Catalog, by choosing Options from the
Tools
menu (see
Accessing the Warehouse Catalog, page 297
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Project Design Guide Optimizing and Maintaining Your Project 8 access the Warehouse Catalog). The Warehouse Catalog Options dialog box opens, which allows you to perform the following tasks:
•
Data warehouse connection and read operations
•
Displaying table prefixes, row counts, and name spaces
•
Mapping schema objects and calculating logical sizes for tables
Data warehouse connection and read operations
You can modify the database instance and database login used to connect the data warehouse to a project, as well as change how the database catalog tables are read. You can make these type of modification from the Catalog category, which is divided into the following subcategories:
• Warehouse Connection:
Select the desired database instance to use for the project as well as the custom database login.
Database Instance
: You can select the primary database instance for the Warehouse Catalog from the drop-down list.
The primary database instance acts as the main source of data for a project and is used as the default database instance for tables added to the project. Non-database related VLDB property settings are also inherited from the primary database instance.
If the desired database instance does not appear in the Database
Instance box, or if it does but needs to be modified, you can select from the following:
– Click Edit to modify the selected database instance. The General tab of the Database Instances dialog box opens.
– Click New to create a new database instance. The Database
Instance Wizard opens.
Refer to the MicroStrategy System Administration Guide for more information on either of these dialog boxes.
Custom Database Login
: You can either select the database login or clear the login to use no database login.
For more information on the database login, see the
MicroStrategy Developer Help (formerly the MicroStrategy
Desktop Help).
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• Read Settings:
You can define how database catalog tables are retrieved:
Use standard ODBC calls to obtain the database catalog
: This option is available if you connect to a database type that supports
ODBC calls to retrieve the database catalog tables. If you select this option, standard ODBC calls are used to retrieve the database catalog tables. If you use a Microsoft Access database, ODBC calls are automatically used. If you select this option and the results are not retrieving the database catalog tables correctly, you can select the SQL statement option listed below to customize how the database catalog tables are retrieved.
Use one or more SQL statements that query directly the database catalog tables
: This option is available if you connect to a database type that supports ODBC calls to retrieve the database catalog tables.
Otherwise, this is option is not shown and only the Settings button is displayed. If you select this option, SQL statements are used to retrieve the database catalog tables.
You can customize the SQL to read the Warehouse Catalog for every platform (except Microsoft Access), by clicking Settings. This allows you to directly edit the catalog SQL statements that are used to retrieve the list of available tables from the Warehouse Catalog and the columns for the selected tables. The default catalog SQL retrieves a DISTINCT list of tables and columns from all users. You could restrict the information returned, for example, by specifying certain
conditions and table owners (see
Customizing catalog SQL statements, page 311
You can also choose from the following options to read the database catalog tables:
Read the table Primary and Foreign Keys
: Select this option to display, in MicroStrategy, which columns are defined as primary keys or foreign keys in the data source. Primary keys and foreign keys can help facilitate joining tables to create Query Builder reports, as described in the Advanced Reporting Guide.
Displaying primary key or foreign key information in MicroStrategy can also help users designing a project to determine which columns of data may be suitable to serve as the identification columns of attributes. For information on defining the primary key for tables included in a MicroStrategy project, see
Defining the primary key for a table, page 455
.
Count the number of rows for all tables when reading the database catalog:
Select this option if you want to control whether or not the Warehouse Catalog should get the number of rows each table
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Ignore current table name space when reading from the database catalog and update using new table name space:
This option allows you to switch between warehouses found in different database
name spaces. For more information, see
Ignoring table name spaces when migrating tables, page 310
of this appendix. By default this option is selected.
Display a warning if the column data types are modified when updating the table structure:
Select this option if you want to be warned when the data type for a column stored in the project is different from the one read from the data warehouse. The check for the data type change is only performed when updating a table’s structure. By default this option is selected.
Automatically update information for all Partition Mapping tables when reading the database catalog:
Select this option to read the latest information for the partition mapping tables (PMTs) currently present in the project. This setting should be cleared when the number of PMTs in the project is so large that reading their structure is causing performance problems when opening the Warehouse
Catalog. By default this option is selected.
Column Merging Options
: When you add a new table to your data warehouse, it may redefine the data type for a column included in the project. For example, your project includes a table named Table1 that has column C1 of data type char(1). Then a new table named Table2 is added to the project, but it has column C1 set to data type char(4).
This example is used to illustrate the options described below. When you update the table structure, the column data types are modified to maintain a consistent schema in one of three ways, depending on the option you select.
The options below do not handle the merge if the data type has changed to an incompatible data type. For example, a column is changed from data type char to data type integer. If the data type has changed to an incompatible data type, a warning is displayed and you are asked if you want to use the new data type.
– Use most recent data type: This option updates the column data type to use the most recent column definition. In the example
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– Use maximum denominator data type: This option updates the column data type to use the data type with the largest precision or scale. In the example above, the column data type for C1 would be changed to char(4), as defined in Table2. This is because char(4) has a higher precision than char(1) defined in Table1. If the data type has been changed to a different compatible data type, the data type with the largest precision or scale is used, as illustrated in the image below.
– Do not merge: This option renames the column in the newly added table, which allows the columns to have different data types.
From the example above, column C1 uses the char(1) data type for
Table1. Column C1 in Table2 is defined as a separate copy of C1 and uses the char(4) data type. This option can cause unwanted schema changes and should be used only when necessary.
• Read Mode
: The Warehouse Catalog can be automatically read upon opening the Warehouse Catalog, or restricted to only be read when a read is manually requested:
Automatic:
This option sets the Warehouse Catalog tables to be read as soon as the catalog browser is loaded.
Manual:
This option sets the Warehouse Catalog tables to be read only when the read catalog action is selected.
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Displaying table prefixes, row counts, and name spaces
You can choose to show or hide table prefixes, row counts, and name spaces, by using the View category. This category is divided into the following subcategories:
• Table Prefixes
: You can specify whether table prefixes are displayed in table names and how prefixes are automatically defined for tables that are added to the project. You have the following options:
Display table prefixes in the main dialog:
Select this option to display all prefixes in table names, including new tables added to the project. By default this option is selected.
Automatically define prefixes for all tables that are added to this project:
This setting enables/disables the following options:
– Set a prefix based on the warehouse table name space or
owner (import prefix)
: When this option is selected, the
Warehouse Catalog reads the name space for each table being added, creates a prefix having the same text as the name space, and associates it with the table being added.
– Set a default prefix: Select this to add a prefix to tables when they are added to a project. This option is only active when the database supports prefixes. You can select the default prefix from the
Default prefix box drop-down list or create a new table prefix by clicking Modify prefix list.
– Modify prefix list: You can create a new tables prefix or delete an existing prefix by selecting this option. The Table Prefixes dialog box opens. For more information on modifying the prefix list, see the online help.
• Table Row Counts
: You can show or hide the number of rows per table, using the check box:
Display the number of rows per table:
You can show or hide the values calculated for the number of rows for the tables. By default, this option is selected and the number of rows are shown.
• Table Name Spaces
: You can show or hide the name space for each table, using the check box:
Display the name space for each table (if applicable):
You can show or hide the owner or table name space where the table is located in the warehouse. By default, this option is selected and table name spaces are shown.
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Mapping schema objects and calculating logical sizes for tables
The Schema category is divided into the following subcategories:
• Automatic Mapping
: When you add new tables to the Warehouse
Catalog, you can determine whether existing schema objects in the project are mapped to these new tables automatically, using the following options:
Map schema objects to new tables automatically
: Existing objects in the schema automatically map to tables you add to the project.
Do not map schema objects to the new tables
: Objects in the schema are not automatically mapped to tables you add to the project.
These automatic mapping methods are only applied to existing schema objects when tables are added to the Warehouse Catalog. For example, the attribute Year with an attribute form mapped to YEAR_ID is included in a project. Then a new table which includes a YEAR_ID column is added to the Warehouse Catalog. With the Map schema objects to new tables automatically option selected, the Year attribute is automatically mapped when the new table is added.
If the table was added to the Warehouse Catalog first and then the attribute was created, the Warehouse Catalog automatic mapping settings do not determine whether the attribute and table are automatically mapped. Automatically mapping tables to schema objects when adding attributes or facts to a project is controlled by the Attribute Editor and Fact Editor, respectively.
• Table Logical Sizes
: You can select whether the Warehouse Catalog calculates logical sizes for new tables using one of the following options:
Calculate the logical table sizes automatically
: Logical sizes are automatically calculated for tables you add to the project.
Do not calculate table logical sizes
: Logical sizes are not calculated for the tables you add to the project.
Ignoring table name spaces when migrating tables
It is a common practice to establish a secondary warehouse with less information than the primary warehouse for development and testing.
Before going into production, you change the project to point to the primary warehouse.
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Most database management systems (Oracle, DB2, and others) support the concept of a
table name space
, which is a way of organizing database tables into different storage spaces. This method allows you to repeat the same table name in different table name spaces. For instance, you can have
LU_STORE in a table name space called dbo and another table LU_STORE in another table name space called admin. You now have two tables dbo.LU_STORE and admin.LU_STORE. The table name space provides an extra piece of information that uniquely identifies the table.
When you add tables to a project, the Warehouse Catalog saves information to the appropriate table name space. This can cause a problem when you migrate from a warehouse that resides in a certain table name space to another warehouse in a different table name space. The Warehouse Catalog interprets the table as already in the project and not found in the new warehouse. This is because the Warehouse Catalog is looking for a table named dbo.LU_STORE, and the table is actually stored as admin.LU_STORE in the new production warehouse.
To solve this problem, select the Ignore current table name space when
reading from the database catalog and update using new table name space
check box. You can find this option in the Warehouse Catalog Options dialog box under the Catalog - Read Settings options subcategory. If you select this option, the Warehouse Catalog ignores the current table name space when it reads the catalog information. Thus, the Warehouse Catalog recognizes the two tables as the same table and saves the new table name space information. This setting allows you to migrate much more easily between warehouses. If the check box is cleared, the Warehouse Catalog defaults to identifying the table by both table name space and table name.
Customizing catalog SQL statements
In all supported warehouse platforms other than Microsoft Access,
MicroStrategy uses SQL statements to query the relational database management system (RDBMS) catalog tables to obtain warehouse catalog information. This information includes catalog tables, columns, and their data types.
These catalog SQL statements vary from platform to platform and can be customized according to the characteristics of the specific warehouse.
Microsoft Access does not have catalog tables, so an ODBC call must be used to retrieve information about tables and columns in Access.
By default, a similar ODBC call is used for the Generic DBMS database
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The MicroStrategy Warehouse Catalog can be configured to read the catalog information in one- or two-pass SQL mode. In two-pass SQL mode, it first reads only the tables from the database. The structure of individual tables is read only when the table is selected. This is the recommended option for interactive warehouse catalog building because no unnecessary catalog information is read from the database, which increases processing speed.
One-pass SQL mode, on the other hand, reads all the tables and columns in one SQL statement. This option is recommended only if the catalog SQL is well customized to limit the amount of data returned by it.
The two retrieval options use different catalog SQL, but both can be customized in the Warehouse Catalog Options dialog box. In the following sections, the name Catalog Table SQL refers to the catalog SQL to retrieve the tables in the warehouse; that is, the first SQL used in a two-pass catalog retrieval.
The name Full Catalog SQL refers to the SQL used to read all the tables and columns in one pass.
To customize a catalog SQL, you must understand several important concepts and procedures:
•
The table name space, page 312
•
SQL placeholder strings and incomplete catalog SQL, page 313
•
Structure of Catalog Table SQL, page 313
•
Structure of Full Catalog SQL, page 314
•
Modifying catalog SQL, page 314
•
The table name space
In a typical RDBMS platform, a table name does not uniquely identify it in a particular database installation. A table name space is a partition of the database installation in which table names are unique. Depending on the type of RDBMS, this name space can be the name of the database, the owner of the table, or a combination of both database and owner. In both the
Catalog Table SQL and Full Catalog SQL, a name space gives each table a
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The table name space is optional. A customized catalog SQL can omit the name space if duplicate table names do not present a problem in the warehouse database.
SQL placeholder strings and incomplete catalog SQL
The default system catalog SQL can contain certain placeholder strings that can be resolved at run time or must be completed manually by the user.
These placeholders are:
•
#LOGIN_NAME#—This placeholder is automatically replaced at run time with the login name used to connect to the database. You can leave this template in the customized SQL if you want the catalog SQL to yield different results depending on the warehouse login used. Otherwise, this template is replaced with the name of the database user who owns the warehouse tables of interest.
•
#?Database_Name?#, #?Schema_Name?#—This catalog SQL placeholder is an incomplete SQL string that must be completed by the user before it can be executed. The string starts with #? and ends with ?#.
The command #?Database_Name?#, used with Teradata, must be replaced with the name of the database containing the database tables.
#?Schema_Name?#, used with DB2 AS/400 and MySQL, must be replaced with the name of the schema in which the database tables for the project reside.
Structure of Catalog Table SQL
Catalog Table SQL is expected to return two columns, one identifying the name space of the table and the other the name of the table. If a name space is not provided, only the table name column is required. Each row of the SQL result must uniquely identify a table. Duplicates are not allowed. The column that identifies the table name space uses the SQL column alias NAME_SPACE.
The column that identifies the table name has the alias TAB_NAME. The following example is the default Catalog Table SQL for Oracle 8.0:
SELECT DISTINCT OWNER NAME_SPACE, TABLE_NAME TAB_NAME
FROM ALL_TAB_COLUMNS
WHERE OWNER = '#LOGIN_NAME#'
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Structure of Full Catalog SQL
Full Catalog SQL is expected to return between five and seven columns, depending on the RDBMS platform and the customization.
The following aliases identify each column returned:
•
NAME_SPACE (optional): the table name space
•
TAB_NAME (required): name of the table
•
COL_NAME (required): name of the column
•
DATA_TYPE (required): a string or a number that identifies the major data type of the column
•
DATA_LEN (required): a number that describes the length or size of the column data
•
DATA_PREC (optional): a number that describes the precision of the column data
•
DATA_SCALE (optional): a number that describes the scale of a floating point column data
Full Catalog SQL must return its rows ordered first by NAME_SPACE, if available, and then by TAB_NAME.
The following example is the default Full Catalog SQL for Microsoft SQL
Server 7.0:
SELECT U.name NAME_SPACE, T.name TAB_NAME, C.name
COL_NAME, C.type DATA_TYPE, C.length DATA_LEN, C.prec
DATA_PREC, C.scale DATA_SCALE
FROM sysobjects T, syscolumns C, sysusers
WHERE T.id = C.id and T.type in ('U', 'V')
AND T.uid = U.uid
ORDER BY 1, 2
Modifying catalog SQL
You can customize and modify the catalog SQL that is run against your database for each project. The catalog SQL can be modified in the Warehouse
Catalog options for your project.
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To modify the catalog SQL for your project
1
Access the Warehouse Catalog for your project (see
). The Warehouse Catalog opens.
2
From the Tools menu, select Options. The Warehouse Catalog Options dialog box opens.
3
Expand the Catalog Category, and select Read Settings. The Catalog -
Read Settings options are displayed.
4
Click the Settings button, the catalog SQL options are displayed as shown below.
The catalog SQL settings are unavailable if your project is connected to a Microsoft Access database.
The top pane controls the Catalog Table SQL and the bottom pane controls the Full Catalog SQL.
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Default catalog SQL
When customizing the catalog SQL that is executed on your database, it is recommended you consult the default catalog SQL that MicroStrategy uses to support different database platforms. You can generate the default catalog
SQL in MicroStrategy for the database platform your project connects to.
To generate and view the default catalog SQL
1
Access the catalog SQL options for your project (see
). A dialog box for the catalog SQL options is displayed.
•
The top pane controls the Catalog Table SQL, which retrieves a list of available tables in the Warehouse Catalog.
•
The bottom pane controls the Full Catalog SQL, which retrieves column information for the selected tables.
Before performing the next step, cut and paste the SQL statements in the two panes into any text editor. This allows you to save any modifications you have made previously to the catalog SQL statements, and then compare them to the default statements you are about to generate.
2
Generate and view the default catalog SQL for your database platform.
Any text in the panes is overwritten with the default catalog SQL statements:
•
To generate and view the default Catalog Table SQL for your database platform, click the upper-most Use Default button.
•
To generate and view the default Full Catalog SQL for your database platform, click the bottom-most Use Default button.
You can use the default catalog SQL statements or compare and combine them with your own customized catalog SQL statements.
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Troubleshooting table and column messages
You may encounter the following messages while using the Warehouse
Catalog:
•
•
•
Tables missing
This happens when one or more tables already in the project are removed from the data warehouse. Two cases can be seen:
•
When the Warehouse Catalog is starting and retrieving the table information from the data warehouse and it detects that one or more tables already in the project are missing, it displays an error message which gives you the following options:
Leave the table in the project
: This leaves everything as is in the project metadata. However the definition in the project may be inconsistent with the real physical structure in the warehouse. This can result in SQL errors when running reports that need data from a
“missing” table.
Remove the table from the project.
In this case, the Warehouse
Catalog does not check for any dependencies until you save the changes. If there are any dependencies, they are presented to you, and you have the option to proceed or cancel the operation.
•
When the Warehouse Catalog tries to update the structure of a table that is missing in the warehouse, a message is shown which explains that the table structure update cannot proceed because the table was not found in the warehouse. In this case, no changes occur and the original table structure remains intact.
Column’s data type changed
When the table structure is updated for one or more tables in which the column data types have been changed, you get a warning message showing the table name, column name, original data type, and new data type. You can click Cancel at any time to undo all data type changes. This results in no changes being applied to the tables and columns.
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Columns missing
Missing columns are detected when Update Structure is performed. If this happens, the Warehouse Catalog checks for the following:
• Column is not mapped to any schema object:
If this is the case, then no error message is shown.
• Column is mapped to a schema object:
If this is the case, then a message is displayed that gives details on objects, which are mapped to the missing column and the update structure operation is canceled. You are asked to remove the mapping before continuing with the update structure and original table structure is restored.
Accessing multiple data sources in a project
MicroStrategy provides an extension to Intelligence Server referred to as
MultiSource Option. With the
MultiSource Option
feature, you can connect a project to multiple relational data sources. This lets you integrate all of your information from various databases and other relational data sources into a single MicroStrategy project for reporting and analysis purposes. All data sources included by using the MultiSource Option are integrated as part of the same relational schema for a project.
Accessing multiple relational data sources in a single project can provide many benefits and reporting solutions. There is the obvious benefit of being able to integrate information from various data sources into a single project.
Along with accessing data in data sources provided from a centralized server, you can also access personal relational data sources.
For example, a sales manager wants to include forecast data available in a spreadsheet stored on a sales representative’s local machine. By connecting to the spreadsheet as a relational data source, this forecast data can be viewed along with actual sales data from the centralized database.
MultiSource Option also allows you to use Freeform SQL, Query Builder, and
MDX cube reports, that access secondary data sources, as filters on standard reports. For information on Freeform SQL and Query Builder reports, see the Advanced Reporting Guide. For information on MDX cube reports, see the MDX Cube Reporting Guide.
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If you have the MultiSource Option, you can access multiple data sources in a project as described below:
•
Connecting data sources to a project, page 319
•
Adding data into a project, page 322
•
You can create logical views on data sources other than the data source for the primary database instance. This technique along with steps to create logical views are described in
Creating logical views, page 456
•
You can support fact tables that are partitioned across multiple
databases. Using metadata partitioning is described in
Metadata partition mapping, page 342
•
You can include MDX cube data in regular reports, which lets you include
MDX cube data along with data from your relational project as well as
MDX cube data from other MDX cubes. For examples of this capability and steps to create these types of reports, refer to the MDX Cube
Reporting Guide.
Connecting data sources to a project
You can connect a project to a data source through a database instance. A database instance specifies warehouse connection information, such as the data source name, login ID and password, and other data source specific information. For information on creating a database instance, see the
Installation and Configuration Guide.
Once database instances have been created for your data sources, you can connect them to your project. However, keep in mind that if you include multiple data sources in a project, the data sources should all fit into the same logical data model and warehouse structure planned for your project.
For information on planning a logical data model and a physical warehouse
Chapter 2, The Logical Data Model
Structure for Your Logical Data Model
.
The procedure below describes how to include multiple data sources in a project.
Prerequisites
•
A project has been created.
•
Database instances have been created for the data sources to include in a project.
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•
You must have the MultiSource Option to connect multiple data sources to a project.
To include multiple data sources in a project
1
In Developer, log in to a project.
2
Right-click the project and select Project Configuration. The Project
Configuration Editor opens.
3
From the Categories list, expand Database Instances, and then select
SQL Data Warehouses
.
4
In the Database Instance pane, select the check box next to the database instances for the data sources to include in a project.
Selecting a check box for a database instance also makes its data source available for use with Query Builder and Freeform SQL. The availability of multiple data sources through Query Builder or Freeform SQL does not require the MultiSource Option. However, only one data source can be used at a time in a Query Builder or Freeform SQL report. For information on Query Builder and Freeform SQL, see the Advanced
Reporting Guide.
5
In the drop-down list near the top, select a database instance to act as the primary database instance.
The primary database instance acts as the main source of data for a project and is used as the default database instance for tables added to the project. Non-database related VLDB property settings are also inherited from the primary database instance.
To determine the order of data source access
You can define the order in which data sources are used to provide data as part of the MultiSource Option, when the same data can be retrieved from multiple data sources.
6
From the Categories list, expand Report definition, and then select
SQL generation
.
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In the Database Instance Ordering area, you can define the order in which data sources are used to provide data as part of the MultiSource
Option, as described below: a To define a priority in which each data source is used to access data as part of the MultiSource Option, click Modify. The Select Objects dialog box opens. b Move any applicable database instances for the project to the Selected objects pane. c You can then use the up and down arrows to change the priority of each database instance, with the database instance at the top of the list having the highest priority. Highest priority means that the database instance is used first if it is one of the database instances that can be used to retrieve the requested data. In addition to using this order for a project, you can enable or disable the use of this ordering for individual reports, as described in the Developer Help (formerly the MicroStrategy Desktop Help).
d You have the following options to determine the order in which data sources are selected for the project:
• Use MultiSource Option default ordering
(default): If data is available in multiple data sources through MultiSource Option, the primary database instance is used if it has the necessary data. If the data is only available in other secondary data sources, one of the secondary data sources that includes the data is used to retrieve the necessary data using some basic internal logic. Any data source priority that you defined using Database Instance
Ordering is ignored.
By selecting this option, this MultiSource Option default ordering is used for all reports in a project by default. You can enable or disable the use of this ordering for individual reports, as described in the Developer Help (formerly the MicroStrategy Desktop
Help).
• Use project level database instance ordering
: If data is available in multiple data sources through MultiSource Option, the data source used to retrieve the data is based on the priority that you defined using Database Instance Ordering. If data is only available in a data source that is not included in the priority list, then an applicable data source is chosen using the standard
MultiSource Option logic.
By selecting this option, the data source priority list that you defined for the project is used for all reports in a project by default.
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You can enable or disable the use of this ordering for individual reports, as described in the Developer Help (formerly the
MicroStrategy Desktop Help).
To save your changes and complete the configuration
8
Click OK to save your changes and close the Project Configuration Editor.
9
MicroStrategy includes additional techniques that can help to improve the performance of MultiSource Option in certain scenarios, including:
•
If the data source you use with MultiSource Option supports parameterized queries, you can enable the use of parameterized queries to improve the performance of MultiSource Option. For information on enabling the use of parameterized queries, see
Improving database insert performance: parameterized queries, page 331
•
Using MultiSource Option means that multiple passes of SQL are executed to return the result from more than one data source. You can execute these multiple passes of SQL at the same time by using parallel SQL execution. For details on how parallel SQL execution works and how it can be enabled using the Parallel SQL Execution
VLDB property, refer to the Supplemental Reference for System
Administration.
The data sources you included in the project can now be accessed from the
Warehouse Catalog and Architect to import tables into the project, as described in
Adding data into a project
Once data sources are connected to a project, you can add data from these data sources into the project. This can be done by importing tables from your data sources into the project.
Tables can be imported into a project using the Warehouse Catalog or
Architect. In the Warehouse Catalog, you can use the Select current
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database instance
drop-down list shown below to switch between the data sources you are importing tables for.
In Architect, you can use the Warehouse Tables pane shown below to switch between the data sources you are importing tables for.
If the tables that you import from various sources all use different table names, the tables are imported exactly as they are when only a single data source is used. You can also import tables with the same name from different data sources, and is described in
Supporting duplicate tables in multiple data sources
below.
Supporting duplicate tables in multiple data sources
You can support the integration of duplicate tables in multiple data sources through the use of MultiSource Option. The MicroStrategy SQL Engine can then obtain any required attribute information from the data source that stores that information. This process can return this information to reports
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Optimizing and Maintaining Your Project Project Design Guide and documents without any extra considerations or tasks for a report or document designer.
Including duplicate copies of tables from different data sources allows
MicroStrategy to execute within a single data source for certain types of queries.
For example, you have two data sources. One data source stores historical data for your company. The other data source stores forecast data for the same business sectors. Each data source includes duplicate copies of tables that store attribute information, which describe the context of data. The data sources differ in the availability of historical data versus forecast data, which is integrated into your MicroStrategy project through the use of facts and metrics.
In this scenario, including each copy of the tables that include attribute information from both data sources allows some queries to be processed within a single data source. By including these duplicate copies, users that only need to view historical data can have their query resolved within a single data source. Similarly, users that only need to view forecast data can have their query resolved completely within the other data source. This reduces the time and system resources required for these types of queries since working within a single data source is more efficient than querying across multiple data sources.
Including both historical and forecast data on the same report from these different data sources is also possible in this scenario through the use of MultiSource Option. However, since the historical and forecast data are only available in separate data sources, this query must include both data sources.
To import multiple copies of the same table from different data sources into a project, the requirements listed below must be met:
•
The table name and column names must be exactly the same.
•
One of the copies of the table must act as the primary table used in the project. All of the columns in this table must also be present in the other copies of the table from other data sources. The other copies of the table that are used as secondary tables can include additional columns of information. However, these additional columns are not included in the project when the table is added.
•
When you import multiple copies of a table from multiple data sources, import the table that is to act as the primary table first. Once you import
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If you do not import the primary table first, you may have to remove some tables and then add them back into the project after the primary table is imported. This workflow may be required to update existing projects that did not previously use MultiSource Option.
•
The data types of matching columns must be compatible. Compatibility of column data types is described below:
A Decimal data type with a scale of zero is compatible with the Integer data type.
A Numeric data type with a scale of zero is compatible with the Integer data type.
A Decimal data type is compatible with a Numeric data type.
Double, Float, and Real data types are all compatible with each other.
A Date data type is compatible with a Timestamp data type.
A Time data type is compatible with a Timestamp data type.
A Char data type is compatible with a VarChar data type.
Any other data types are only compatible with an identical data type.
Be aware that a Date data type is not compatible with a Time data type, and NVarChar and NChar data types are not compatible with
VarChar and Char data types.
The procedures below describe how to import multiple copies of the same table into MicroStrategy using the Warehouse Catalog or Architect:
•
Importing tables from multiple data sources in a project using the
•
Importing tables from multiple data sources in a project using Architect, page 327
Importing tables from multiple data sources in a project using the Warehouse
Catalog
Prerequisite
•
You must have MultiSource Option to connect multiple data sources to a project.
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To import tables from multiple data sources in a project using the Warehouse
Catalog
1
In Developer, log in to a project.
2
From the Schema menu, select Warehouse Catalog. The Warehouse
Catalog opens.
3
From the Select current database instance drop-down list, select the database instance for one of the data sources the table resides in. The first data source you use to import a table should be the one you plan to use as the primary data source for the table.
Importing a table from the primary database instance for a project or a non-primary database instance has an effect on how the table is updated when the primary database instance for a project is changed, as described in
Importing tables as part of the project’s primary database instance, page 329
.
4
In the Tables available in the database instance pane, select the table to add to the project and click the > button. The first copy of the table is added to the project and is displayed in the Tables being Used in the
Project pane.
To add copies of a table from other database instances
5
From the Select current database instance drop-down list, select the database instance for a different data source that also includes the table.
6
In the Tables available in the database instance pane, select the table to add to the project and click the > button.
If all of the required conditions to import multiple copies of the table
Supporting duplicate tables in multiple data sources, page 323
) are met, a Warehouse Catalog Browser dialog box opens. To include a copy of the table in the project, select Indicate that
TABLE_NAME is also available from the current DB instance
, and click OK. The copy of the table is added to the project and is displayed in the Tables being Used in the Project pane.
Review any messages displayed when attempting to import a copy of a table from a different data source.
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To add additional tables and configure the tables included in the project
7
To add tables from additional data sources, repeat the steps in
To add copies of a table from other database instances
above.
8
In the Tables being used in the project pane, right-click the table and select Table Database Instances. The Available Database Instances dialog box opens.
9
From the Primary Database Instance drop-down list, select a database instance for the data source that stores the primary table for the project.
All of the columns in this primary table must also be present in the other copies of the table from other data sources. Any additional columns available in other copies of the table that are used as secondary tables are not included in the MicroStrategy project.
10
The Secondary Database Instances pane lists the other data sources that the table is available from for the project. You can clear the check box next to a data source to remove that copy of the table from the project.
11
Click OK. You are returned to the Warehouse Catalog.
12
Click Save and Close to save your changes and close the Warehouse
Catalog.
Importing tables from multiple data sources in a project using Architect
Prerequisites
•
You must have the MultiSource Option to connect multiple data sources to a project.
To import tables from multiple data sources in a project using Architect
1
In Developer, log in to a project.
2
From the Schema menu, select Architect. MicroStrategy Architect opens.
3
From the Project Tables View, in the Warehouse Tables pane, expand the database instance for one of the data sources the table resides in. The first data source you use to import a table should be the one you plan to use as the primary data source for the table.
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Importing a table from the primary database instance for a project or a non-primary database instance has an effect on how the table is updated when the primary database instance for a project is changed, as described in
Importing tables as part of the project’s primary database instance, page 329
.
4
From the Warehouse Tables pane, right-click the table to add to the project and select Add Table to Project. The first copy of the table is added to the project and is displayed in the Project Tables View of
Architect.
To add copies of a table from other database instances
5
From the Warehouse Tables pane, expand the database instance for a different data source that also includes the table.
6
From the Warehouse Tables pane, right-click the table to add to the project and select Add Table to Project.
If all of the required conditions to import multiple copies of the table
Supporting duplicate tables in multiple data sources, page 323
) are met, an Options dialog box opens. To include a copy of the table in the project, select Indicate that TABLE_NAME is also available
from the current DB instance
, and click OK. The copy of the table is added to the project and is displayed in the Project Tables View of
Architect.
Review any messages displayed when attempting to import a copy of a table from a different data source.
To add additional tables and configure the tables included in the project
7
To add tables from additional data sources, repeat the steps in
To add copies of a table from other database instances
above.
8
From the Project Tables View, select the table. Information on the table is displayed in the Properties pane.
9
From the Properties pane, select the Primary DB Instance option, and then click ... (Browse). The Available Database Instances dialog box opens.
10
From the Primary Database Instance drop-down list, select a database instance for the data source that stores the primary table for the project.
All of the columns in this primary table must also be present in the other copies of the table from other data sources. Any additional columns
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11
The Secondary Database Instances pane lists the other data sources that the table is available from for the project. You can clear the check box next to a data source to remove that copy of the table from the project.
12
Click OK. You are returned to Architect.
13
Click Save and Close to save your changes and close the Warehouse
Catalog.
Importing tables as part of the project’s primary database instance
Importing a table from the primary database instance for a project or a non-primary database instance has an effect on how the table is updated when the primary database instance for a project is changed:
•
If you import a table from the primary database instance for a project, the table's primary data source is updated when the primary database instance for the project is changed. This supports scenarios such as moving from a testing environment to a production environment where different database instances are used for each environment.
For example, tables are imported into a project from the primary database instance of a project. The primary database instance is for testing purposes. When the system is switched into production, a new production database instance is defined as the new primary database instance for the project. During the switch of primary database instances, all tables that were imported with the original (testing) primary database instance are modified to use the new (production) primary database
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Optimizing and Maintaining Your Project Project Design Guide instance. This example scenario is shown below, which illustrates that the tables remain connected to the new primary database instance.
•
If you import a table from a non-primary database instance for a project, the database instance used for the initial import remains as the primary data source for the table. This supports scenarios in which a table is only provided in secondary data sources that are not the primary data source for the project.
You can modify a table to always use the primary database instance as its primary data source. Steps on how to switch the database instance for a table are provided below.
To switch primary database instances for a table
1
In MicroStrategy Developer, log in to a project.
2
From the Schema menu, select Warehouse Catalog. The Warehouse
Catalog opens.
3
In the Tables being used in the project area, right-click a table and select Table Database Instances. The Available Database Instances dialog box opens.
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4
From the Primary Database Instance drop-down list, select the database instance to act as the primary database instance for the table:
•
The database instance that is used as the primary database instance for a project is listed twice in the drop-down list. An example of this is shown below:
If you select the database instance that is listed as the project’s primary database instance, the table's primary data source is updated when the primary database instance for the project is changed.
If you select the database instance that is listed without the project primary database instance distinction, this database instance remains as the primary data source for the table even if a new project primary database instance is selected.
•
The other database instances listed that are not the project’s primary database instance can also be selected. If selected, the database instance remains as the primary data source for the table.
5
Click OK. If any warnings are displayed, read the information and take any required action.
6
In the Warehouse Catalog, click Save and Close to save your changes.
Improving database insert performance: parameterized queries
MicroStrategy’s support for parameterized queries can improve performance in scenarios that require the insertion of information into a database. The scenarios that can benefit from the use of parameterized queries include:
•
Reports that combine data from multiple data sources using
MicroStrategy MultiSource Option. For information on MultiSource
Option, see
Accessing multiple data sources in a project, page 318
.
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•
MicroStrategy data marts that are stored in a database other than the database used for the main data warehouse. For information on creating and using data marts, refer to the Advanced Reporting Guide.
•
Metrics that use functions that are evaluated by the Analytical Engine.
For information on functions, refer to the Functions Reference.
•
Custom groups that use banding qualifications that are evaluated as normal calculations. For information on custom groups, refer to the
Advanced Reporting Guide.
Parameterized queries are SQL queries that can use placeholders for data.
Using placeholders allows these queries to be re-used. A common application of this re-usability is to combine multiple inserts of data into a database as a single query. The following is an example of a parameterized query:
INSERT INTO DMTABLE (Customer_ID, Customer_Name) VALUES
(?, ?)
Combining multiple INSERT statements into a single query can improve the performance of inserting data into the database. The steps below show you how to enable the use of parameterized queries in MicroStrategy.
If you enable parameterized queries for a Netezza database that includes data that uses the WCHAR data type, this can cause some characters to be returned incorrectly as question mark (?) characters.
If you encounter this type of data inconsistency, you can configure the
Use Column Type Hint for Parameterized Query VLDB property to return the WCHAR data accurately. For information on configuring this VLDB property, see the Supplemental Reference for System
Administration.
Prerequisites
•
Parameterized queries are only supported by certain databases. Refer to your third-party database documentation to ensure that your database can support parameterized queries.
•
A database instance has been created. This database instance must connect to the database to enable support for parameterized queries.
To enable the use of parameterized queries
1
In MicroStrategy Developer, log in to a project source with a user account that has administrative privileges.
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2
From the Folder List, expand Administration, then expand
Configuration Managers
, and then select Database Instances.
Database instances for the project source are displayed.
3
Right-click a database instance and select Edit. The Database Instances
Editor opens.
4
To the right of the Database connection area, click Modify. The Database
Connections dialog box opens.
5
On the Advanced tab, select the Use parameterized queries check box.
6
If you are enabling parameterized queries for one of the databases listed below, you must also include the following parameters:
•
To enable parameterized queries for Oracle 10g, Oracle 10gR2, Oracle
11g, Oracle 9i, Sybase Adaptive Server 12.x, or Sybase ASE 15.x, type the following parameter in the Additional connection string
parameters
field:
EnableDescribeParam=1
•
To enable parameterized queries for Teradata 12.0 or Teradata
V2R6.2, type the following parameter in the Additional connection
string parameters
field:
EnableExtendedStmtInfo=Yes
7
Click OK to accept your changes and close the Database Connections dialog box.
8
Click OK to close the Database Instances Editor.
Using summary tables to store data: Aggregate tables
Aggregate tables
are summary tables that store data at higher levels than it was stored when the data was initially captured and saved. Aggregate tables provide quicker access to frequently requested information, while retaining the traditional power of ROLAP to directly query the database to answer any questions. This section describes how and why aggregate tables are used.
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MicroStrategy creates aggregates only on fact tables since lookup tables and relationship tables are usually significantly smaller. To understand aggregate tables, you should be familiar with fact tables in the context of data modeling
and data warehousing. For more information on these topics, see
Chapter 3, Warehouse Structure for Your Logical
Chapter 6, The Building Blocks of Business Data: Facts
When to use aggregate tables
MicroStrategy uses optimized SQL to query the relational database directly to answer users’ questions. Users can ask any question that is supported by the data in their warehouse and then analyze the results until they find a precise answer.
The disadvantage to this relational OLAP (ROLAP) methodology is that accessing huge fact tables can be potentially time-consuming.
Multidimensional OLAP (
MOLAP
) is sometimes considered by some to be the answer to this problem. However, MOLAP is not scalable for large projects because of the difficulty of maintaining every possible combination of aggregates as the number of attributes and the amount of data increases.
MicroStrategy’s solution is the use of aggregate tables to provide quicker access to frequently-accessed data while still retaining the power to answer any user query.
Aggregate tables are advantageous because they:
•
Reduce input/output, CPU, RAM, and swapping requirements
•
Eliminate the need to perform dynamic calculations
•
Decrease the number of physical disk reads and the number of records that must be read to satisfy a query
•
Minimize the amount of data that must be aggregated and sorted at run time
•
Move time-intensive calculations with complicated logic or significant computations into a batch routine from dynamic SQL executed at report run time
In summary, the MicroStrategy SQL Engine, in combination with aggregate tables and caching, can produce results at about the same speed as MOLAP.
This combined solution allows questions to be answered on the fly and is also scalable for large databases.
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Aggregation versus pre-aggregation
Whenever the display level of data on a report must differ from the level at which the data is initially captured,
aggregation
, that is, the rolling up of data, must occur. By default, aggregation occurs dynamically with a SQL statement at report run-time.
For example, sales data is stored by day in a fact table. A report requesting month-level data is executed. The daily values from the fact table are selected, sorted, and added to produce the monthly totals, as shown below.
Aggregation can also be completed before reports are executed; the results of the aggregation are stored in an aggregate table. This process is called
pre-aggregation
. You can build these pre-aggregated—or aggregate—tables as part of the ETL process. If sales data is frequently requested at the month level, as in the previous example, an aggregate table with the sales data rolled up to the month level is useful.
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Pre-aggregation eliminates the reading, sorting, and calculation of data from many database rows in a large, lower-level fact table at run time, as shown in the following example.
If the daily sales fact table is the lowest-level fact table and contains
atomic
-level data, it is referred to as a
base table
. In these terms, an aggregate table is any fact table whose data is derived by aggregating data from an existing base table.
Degree of aggregation
While MOLAP can provide fast performance when it answers a question, it requires a completely aggregated schema to answer most questions. That is, every possible combination of aggregate associations must be generated when the multidimensional cube is built. This ensures that all possible questions can be answered. This scenario becomes very difficult to maintain as the number of attributes and the amount of data increase, and therefore is not very scalable.
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In a ROLAP environment, the degree of aggregation can be as dense or as sparse as is appropriate for your users. A densely aggregated warehouse has a large number of aggregate tables while a sparsely aggregated warehouse has fewer. Sparse aggregation refers to the fact that a given project only requires as many aggregate fact tables as is useful to its users.
ROLAP, therefore, provides much greater flexibility than MOLAP. Only the aggregate combinations that you determine are beneficial must be created.
That is, if the aggregate table is useful in answering frequently-asked queries, its presence provides a response as fast as a MOLAP system can provide.
However, if a certain aggregate combination is rarely or never used, the space in the RDBMS does not need to be consumed and the resources to build that table during the batch process do not need to be used.
Not every attribute level or hierarchy intersection is suitable for pre-aggregation. Build aggregate tables only if they can benefit users, since the creation and maintenance of aggregate tables requires additional work by the database administrator. Also, do not waste database space for tables that will not be used.
Consider the following factors when deciding whether to create aggregate tables:
•
The frequency of queries at that level—
Determining the frequency of queries at a specific level, page 337
•
The relationship between the parent and child—
Considering any related parent-child relationships, page 338
•
The compression ratio—
Determining the frequency of queries at a specific level
Build aggregate tables only if they can be useful to your users. If aggregate tables are never accessed, they consume disk space and impose unnecessary burdens on the extraction, translation, and loading process, as well as the database backup routines.
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However, usefulness is not always easy to quantify. For example, consider the following hierarchy:
A summary of data at the department level seems to be a good candidate for an aggregate table. However, if users frequently want to exclude inactive items, the query must use item-level data and summarize the department data dynamically. Therefore, the department aggregate tables would not be used in this situation.
Once your warehouse is in production, trace the usage of any aggregate tables to determine how frequently they are used in a day-to-day business environment. If any table is not used, eliminate it from the warehouse.
MicroStrategy Enterprise Manager allows you to easily track table usage. For more information on Enterprise Manager, see the MicroStrategy System
Administration Guide.
Considering any related parent-child relationships
When an aggregate table is created, the child records are usually summarized into the parent record, based on the key combinations in a relationship table.
In any hierarchical relationship, when the parent-child relationship is altered, all tables that hold that relationship or data relevant to it must be updated. Whether these relationships are dynamic or static change how they are aggregated into tables.
Dynamic relationships
When the relationship between parent and child elements change, the relationship is called
dynamic
. These changes often occur because of organizational restructuring; geographical realignment; or the addition, reclassification, or discontinuation of items or services. For example, a store can decide to reclassify the department to which items belong.
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Aggregate tables that contain dynamic relationships must be recalculated every time a change is made. If the tables are large, this process can take time, consume resources, and complicate the batch process. Frequent changes can mean aggregate tables are not optimal for this situation.
Consider the frequency of the changes, the table size, and the impact on the batch process, and then balance the disadvantages against the advantages of having an aggregate table.
Also, rolling up an entire hierarchy can avoid many problems with relationship changes. For example, a table contains one value for the sum of all stores. It is not affected by a reorganization within the geography hierarchy.
Static relationships
When elements rarely or never change relationships, they are a part of
static relationships
. In these cases, maintaining aggregate tables is very easy. For example, time hierarchies are seldom dynamic—days do not migrate into different weeks, and fiscal weeks do not move into different months.
Compression ratio
The process of data aggregation applies an
aggregate function
, such as sum or average, to a set of child records to produce a single parent record. The average number of child records combined to calculate one parent record is called the
compression ratio
. One measure of effectiveness of an aggregate table can be estimated from this number, since it represents the decrease in records that must be read to respond to a query at that level.
Recall that some of the reasons to build aggregate tables include the reduction of disk I/O and the number of records that must be dynamically sorted and aggregated. Therefore, pre-aggregating data is effective only if the compression ratio is significant. For example, if the compression ratio is 3:2, the aggregate table requires 2/3 of the base table’s storage space but yields only a 1/3 reduction in the number of records. In contrast, if the compression ratio is 4:1, the aggregate table reduces the number of records by 3/4 and uses only 1/4 of the storage space.
When the number of elements differs significantly between two attributes in the same hierarchy, the compression ratio suggests that an aggregate table can provide more efficient queries. Also, for smaller base tables, the resource demands placed on the database server by dynamic aggregations decrease and therefore so does the effectiveness of pre-aggregation. To determine
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For more information on ratios, refer to
Cardinalities and ratios, page 32
Creating aggregate tables
You can integrate aggregate tables in your project using the Warehouse
Catalog in MicroStrategy Developer, as outlined in the following procedure.
To use an aggregate table in an existing project
1
Using the Warehouse Catalog, add the table to the project. For steps to
add tables using the Warehouse Catalog, see
Adding and removing tables for a project, page 297
.
2
Use the new table in the desired fact expressions and attribute form expressions.
If your aggregate table structure is consistent with your base fact table structure, Architect automatically adds it to the definitions of your existing attributes and facts. In other words, Architect is aggregate-aware. How does
Architect know to use the aggregate table rather than the base fact table, when either could provide the answer to a query? The answer is logical table size.
The size of tables in a project: Logical table size
MicroStrategy Developer assigns a size to every table in the project when you first add them to the project. These size assignments are stored in the metadata and are calculated based on the table columns and their corresponding attributes. Because Developer uses the conceptual or logical attribute definitions when assigning sizes, this measurement is known as the
logical table size
.
When you run a report, the Analytical Engine chooses the smallest of all tables, based on logical table size, that contains enough data to answer the query. The initial logical table size is based on the number of attribute columns and the various levels at which they exist in their respective
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Defining logical table sizes, page 452
Dividing tables to increase performance:
Partition mapping
Partition mapping
involves the division of large logical tables into smaller physical tables; this division is based on a definable data level, such as month or department. Partitions improve query performance by minimizing the number of tables and records within a table that must be read to satisfy queries issued against the warehouse. By distributing usage across multiple tables, partitions improve the speed and efficiency of database queries.
Time is the most common category for partitioning databases. Partitioning by time limits growth of the database tables and increases stability.
Server versus application partitioning
Partitioning can be managed by either the database server or the
MicroStrategy application. Either way, tables are partitioned at the database level. The terms “application” and “server” refer to what manages the partitioned tables, not where the tables are split.
Server-level partitioning
The database server, rather than MicroStrategy, manages the partitioned tables in RDBMS
server-level partitioning
. The original fact table is not physically broken into smaller tables. Instead, the database server logically partitions the table according to parameters specified by the database administrator. You do not need to take any action in MicroStrategy to support the partitioning.
Since only the logical table is displayed to the end user, the partitioning is transparent to MicroStrategy. In contrast, in application-level partitioning the relational database is unaware of the partitioned tables.
Refer to your database documentation for details on server partitioning for your particular platform.
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Application-level partitioning
In
application-level partitioning
the application, rather than the RDBMS server, manages the partition tables. A
partition base table
(PBT) is a warehouse table that contains one part of a larger set of data. Partition tables are usually divided along logical lines, such as time or geography.
MicroStrategy supports two types of partitioning:
•
Metadata partition mapping, page 342
information in the project metadata
•
Warehouse partition mapping, page 344
—uses a specialized warehouse table to determine which table to access
Metadata partition mapping
Metadata partition mapping
is the mapping of partitions where the mapping of partitions is performed and maintained in the project metadata by the application, in this case, MicroStrategy. MicroStrategy manages the mapping between the logical table and the physical tables. This approach makes it easier for you to specify a flexible partitioning schema.
In metadata partition mapping, you specify one or more partitioning attributes in the Metadata Partition Mapping Editor. Next you define what attribute elements within those attributes should point to which PBT. You create all of the rules for choosing the appropriate PBT here and the rules are stored in the MicroStrategy metadata.
If you have the MultiSource Option (see
Accessing multiple data sources in a project, page 318
) you can also support fact tables that are partitioned across
multiple databases.
For steps to create a metadata partition mapping, refer to the MicroStrategy
Developer Help (formerly the MicroStrategy Desktop Help).
Homogenous and heterogeneous partitions
Metadata partitions can be homogenous or heterogeneous. With heterogeneous partitioning, the PBTs can have different amounts of data stored in them at different levels. For example, one table can contain six months of sales data, while another stores an entire year. The PBT level, or key, refers to how the data is stored. For example, sales data for the current year can be stored at the daily level, while historical sales data is saved by
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Project Design Guide Optimizing and Maintaining Your Project 8 month only. Heterogeneous partitions can therefore require additional long-term maintenance and organization because the data contained in them is stored at various levels throughout the partition.
MicroStrategy stores one PBT level for each partition. If all the PBTs within a partition are not stored at the same level, the highest PBT level is used as the
PBT level of the partition. For instance, if all the sales data in the previous example is stored in one partition, you cannot access current sales at the day level. This is because the PBT level for the partition is month, which is higher than day. If you save current data in a partition at the daily level and the historical data in another partition at the month level, you are able to fully access the data.
In contrast, homogenous partitions must have the same amount of data stored at the same PBT level. The logical structure of the PBTs must be the same, that is, they must have the same facts and attributes defined. To continue with the previous examples, each table must store one year of data at the month level. Homogeneous partitions work well for frequently-accessed data such as information about the previous year.
When you define the particular PBT to which an attribute is linked in
MicroStrategy, you do not need to specify whether or not the PBT is homogeneous or heterogeneous. MicroStrategy makes the distinction automatically depending, in part, on how the data is stored in the PBT.
Data slices
After PBTs are created, you define a data slice. The
data slice
acts as a filter that describes what portions of data are placed in the partition table. Based on this data slice, the MicroStrategy engine knows which table to get data from when generating the SQL.
A data slice holds the parameters that a partition is based upon, for example,
Month=January. Instead of retrieving data for all months, the server knows to access a particular table that contains the data for January only. By creating a data slice with the partition, you can retrieve specific data quickly without time-consuming joins and searches.
It is important to create a reasonable and valid data slice because
MicroStrategy cannot verify its accuracy or relevance. The data slice must make sense for the data. A poorly crafted data slice can lead to errors from generating incorrect SQL and retrieving the wrong data.
Data slicing displays and can be modified only for the metadata partitioning.
Each partition mapping table must include at least one data slice. In a
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Optimizing and Maintaining Your Project Project Design Guide heterogeneous mapping, data slices can exist at different levels and can be composed of different keys.
Attribute qualifications
To create data slices, you use attribute qualifications.
Attribute qualifications
are types of filters that are applied to attribute forms. These qualifications allow you to limit the type and amount of data that is returned for a report. For example, if you create a report that contains the attribute
Country but you want to return only the data for France, you can create a qualification on the attribute Country and select France as the element that appears on the report.
For steps to create a data slice, refer to the MicroStrategy Developer Help
(formerly the MicroStrategy Desktop Help).
Warehouse partition mapping
Warehouse partition mapping
is the mapping of partitions, where the mapping is performed by and maintained in the data warehouse. You can define a warehouse partition by using the MicroStrategy Warehouse Catalog to add a table with a special structure. This table contains the map for the partition, and is stored in the warehouse. Warehouse partitions divide tables physically along any number of attributes, although this is not visible to the user.
Warehouse partitions must be homogenous, unlike metadata partitions, so that the same amount of data is stored at the same PBT level and the same facts and attributes are defined. Homogenous partitioning divides data of equal levels, like January and February. A sample fact table and warehouse
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Project Design Guide Optimizing and Maintaining Your Project 8 partitioning table are shown below for months. Note how the data exists at equal levels, for example, different months of the same year.
The original fact table, which contains all of the data, is not brought into the project. Rather, the database administrator creates multiple smaller physical tables in the data warehouse. Each table contains a subset of the data in the original fact table. The database administrator is responsible for keeping the partitions consistent and up-to-date. He or she must also create and maintain a
partition mapping table
(PMT), which is used to identify and keep track of the partitioned base tables as part of a logical whole.
After the PMT is created, when you run a report in Developer or Web that requires information from one of the PBTs, the Query Engine first runs a pre-query to the PMT to determine which PBT to access to bring the data back for the report. The pre-query requests the PBT names associated with the attribute IDs from the filtering criteria. When it finds the name of the
PBT, it calls the SQL Engine to write the appropriate SQL for the warehouse.
When using warehouse partition mapping, it is usually not necessary to bring in the individual PBT tables into the project. Doing so can cause errors if such tables are mistakenly mapped directly to schema objects. You should only include the PMT table in the project. With this strategy you can map all related schema objects to the PMT, which then accesses the correct PBT in the warehouse.
Note the following:
•
There are no data slices in a warehouse partition.
•
MicroStrategy supports warehouse partitions on both upgraded and newly created projects. These are added using the Warehouse
Catalog Browser. For steps to add warehouse partitions, refer to the MicroStrategy Developer Help (formerly the MicroStrategy
Desktop Help).
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Metadata versus warehouse partition mapping
Metadata partition mapping does not require any additional tables in the warehouse. Metadata partition mapping is generally recommended over warehouse partition mapping in MicroStrategy. However, if you already have warehouse partition tables set up and are migrating to a newer version of
MicroStrategy, you can continue to use the warehouse partitions. If you are creating partitions for the first time, however, it is recommended you implement metadata partition mapping.
Metadata partition mapping is recommended because you create the rules in
MicroStrategy that the Query Engine uses to generate the SQL to run reports.
Because you create the partitions directly in the metadata, it is easier to maintain.
Metadata partition mapping also allows both heterogeneous and homogenous partitions, unlike warehouse partition mapping. With heterogeneous partitions, the PBTs can have different amounts of data stored in them at different levels. Only homogenous partitions can be used in warehouse partition mapping. For steps to map partitions, refer to the
MicroStrategy Developer Help (formerly the MicroStrategy Desktop Help).
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9.
C
REATING
H
IERARCHIES TO
O
RGANIZE AND
B
ROWSE
A
TTRIBUTES
9
Introduction
Hierarchies are groupings of attributes that can be displayed, either ordered or unordered, to reflect the relationships that exist between the attributes in a project.
Chapter 2, The Logical Data Model
, you learned how to use hierarchies to group related attributes in practical business areas. For example, you can include a Time hierarchy in your model that consists of Day, Week, Month, and Year attributes.
This chapter discusses hierarchies as they exist in the MicroStrategy environment and provides information on the two different types of hierarchies in MicroStrategy. These types of hierarchies include the system hierarchy and the user hierarchy. The system hierarchy is automatically created when you create a project and is maintained by the relationships that
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Creating Hierarchies to Organize and Browse Attributes Project Design Guide exist among the project’s schema objects. The user hierarchy is a hierarchy which you create specifically for your report designers.
This chapter explores how to create and configure user hierarchies in
MicroStrategy and provides additional information about hierarchy functionality in MicroStrategy Developer.
Creating user hierarchies
In MicroStrategy Developer, you create user hierarchies using the Hierarchy
Editor or Architect. For an introduction to user hierarchies and system hierarchies, see
Types of hierarchies, page 351
Follow the procedure below to create a user hierarchy with the Hierarchy
Editor. For information on how to use Architect, see
Creating user hierarchies using Architect, page 350
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To create a new user hierarchy
1
In MicroStrategy Developer, log into the project source that contains your project and open the project.
2
In the Folder List, navigate to and open the Schema Objects folder.
3
Open the Hierarchies folder, and then the Data Explorer folder.
4
From the File menu, select New, and then Hierarchy. The Hierarchy
Editor opens, followed immediately by the Select Attributes dialog box.
5
In the Available objects pane, select the attributes to use in the hierarchy and click the arrow to add them to the Selected objects pane. Click OK to close the Select Attributes dialog box. The attributes you selected appear in the Hierarchy Viewer.
6
The arrows that connect certain attributes denote a relationship between the connected attributes. You can use these relationships as the browsing or drilling relationships for your hierarchy, or you can create your own.
To create a browsing or drilling relationship, select in the middle of an attribute that is to be enabled to browse to and/or drill down to another attribute. Drag from the middle of the attribute to the related attribute. A browsing and/or drilling relationship is created between the two attributes.
7
To use the hierarchy as a drill hierarchy, select the Use as a drill
hierarchy
check box at the bottom of the Hierarchy Editor. If you clear this check box, the hierarchy is only used for browsing.
A drill hierarchy can be used for browsing as well as drilling. Drill hierarchies are discussed in
Drilling using hierarchies, page 365
.
8
Each attribute in a user hierarchy has properties that affect how that attribute is displayed and accessed in a hierarchy. You can right-click an attribute and configure the properties listed below:
• Define Browse Attributes
: Defines the attributes to which users can browse to and/or drill to from the selected attribute. These relationships can also be defined by dragging-and-dropping from one attribute to another as described earlier in this procedure.
• Define Attribute Filters
: Specifies whether the data retrieved and displayed should be complete or filtered by any specific criteria. A
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Creating Hierarchies to Organize and Browse Attributes Project Design Guide filter on a hierarchy acts like a filter in a report. Only data satisfying the filter criteria is displayed (see
Filtering attributes in a hierarchy, page 359
• Set As Entry Point
: Specifies whether the user can begin browsing in
this hierarchy using this attribute (see
• Element Display
: Determines the elements a user can see. The element display may be Locked, Unlocked, or Limited (see
Controlling the display of attribute elements, page 355
9
Click Save and Close. The Save As dialog box opens.
10
Type a name for the hierarchy. Then navigate to the location in which you want to save the hierarchy.
You can save user hierarchies in any folder. However, to make the user hierarchy available for element browsing in the Data Explorer, you must place it in the Data Explorer sub-folder within the Hierarchies folder.
This is discussed in
Enabling hierarchy browsing in reports: Data
11
From the Schema menu, select Update Schema.
Creating user hierarchies using Architect
Architect can be used to create and modify user hierarchies in a visually integrated environment. Architect allows you to view the tables, attributes, attribute relationships, facts, user hierarchies, and other project objects together as you design your project.
With Architect, you can support all of the features that are available in the
Hierarchy Editor. Rather than focusing on one hierarchy at a time with the
Hierarchy Editor, you can use Architect to create and modify multiple hierarchies for a project at one time. Review the chapters and sections listed below for information on Architect and steps to create and modify user hierarchies using Architect:
•
Chapter 5, Creating a Project Using Architect
•
Creating and modifying projects, page 106
•
Creating and modifying user hierarchies, page 181
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Types of hierarchies
The two types of hierarchies that exist in MicroStrategy include:
• System hierarchy
: The system hierarchy is created according to the relationships defined between the attributes in your project. You do not need to create the system hierarchy; it is automatically created in
Developer when you create a project. Although the system hierarchy specifies an ordered set of all attributes in the project, it does not define ordering or grouping among attributes. The ordering and grouping of attributes, among other configurations, is defined in user hierarchies.
• User hierarchy
: User hierarchies are groups of attributes and their relationships to each other, arranged in ways that make sense to a business organization. They are user-defined and do not need to follow the logical data model. As the structure of your business intelligence system evolves, you can modify the design of a user hierarchy to include additional attributes or limit user access to certain attributes. This type of hierarchy is created to provide flexibility in element browsing and report drilling. Steps to create user hierarchies are discussed in:
Creating user hierarchies, page 348
, which describes creating user
hierarchies with the Hierarchy Editor.
Creating and modifying user hierarchies, page 181
creating user hierarchies using Architect.
System hierarchy: Project schema definition
The
system hierarchy
is the default hierarchy that MicroStrategy sets up for you each time you create a project. It contains all of the attributes in the project and is actually part of the schema definition. When you first create a project, the only hierarchy it contains is the system hierarchy.
The system hierarchy holds information on the relationships between attributes in the project. The system hierarchy cannot be edited but is updated every time you add or remove attribute children or parents in the
Attribute Editor, or when you define attribute children in the Project
Creation Assistant.
The system hierarchy is useful in determining relationships between all objects in the project. Attributes from the system hierarchy do not need to be part of an explicitly-defined user hierarchy. Any attributes that are not assigned to a user hierarchy remain available to the system as report objects,
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You can view the system hierarchy in the Data Explorer or in the Hierarchy
Viewer, but not the Hierarchy Editor. You can access the Hierarchy Viewer from Graphical View in the Schema menu. The Hierarchy Viewer is discussed in detail in
Using the Hierarchy Viewer, page 368
.
User hierarchies: Logical business relationships
User hierarchies
are sets of attributes and their relationships, arranged in specific sequences for a logical business organization. You create user hierarchies to define the browse and drill relationships between attributes.
For example, you can create a Time hierarchy that contains the Year,
Quarter, Month, and Day attributes. When you browse the attributes in the
Data Explorer, you can double-click Year to get to Quarter and double-click
Quarter to get to Month, and so on.
Whereas browsing occurs through the Data Explorer, in drilling the user actually chooses to move to higher or lower levels on a report or move across to levels within different hierarchies. For example, if the user drills on the
Quarter attribute in a report, he or she can drill down to Month, up to Year, or across to an attribute within another hierarchy.
You can create user hierarchies in the Hierarchy Editor using one or more attributes from the system hierarchy.
A user hierarchy is the only type of hierarchy you can define, and you can create any number of user hierarchies for each project. You should define user hierarchies that correspond to specific areas of your company business model and data warehouse schema.
Hierarchy organization
The best design for a user hierarchy is to organize or group attributes into logical business areas. This allows users to more easily locate attributes in a project and navigate from one attribute to another. For example, you can place related attributes into hierarchies by their level.
The example below demonstrates the Location and Customer hierarchies.
Within the Location hierarchy, State, City, and Store are organized according
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When creating user hierarchies, keep in mind that hierarchies do not have to be separate from one another or necessarily follow the dimensional structure of your logical data model.
Hierarchy structure
While both a system hierarchy and user hierarchy allow you to navigate attributes in your project, only the user hierarchy allows you to logically define and order groups of attributes.
The rest of this chapter discusses user hierarchies and how to create and configure them in your project.
When you group attributes together into user hierarchies, you are developing a working design of the display and browse functions of the attributes. In the example below, there are two instances of the Region hierarchy. One hierarchy demonstrates Region having multiple States and the States having multiple Stores.
This hierarchy allows you to create drilling and browsing options to the lower levels to view Region, State, and Store on a report. However, if you only
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Creating Hierarchies to Organize and Browse Attributes Project Design Guide include Store in the Region hierarchy, as in the second example, then the only options for drilling or browsing are the Region and Store levels.
Viewing hierarchies: Hierarchy Viewer
The Hierarchy Viewer graphically represents user hierarchies and the system hierarchy. In the system hierarchy, the connections between the attributes represent the parent-child relationships. In user hierarchies, the connections show the browse paths between the attributes. The Aerial perspective provides an overview of hierarchies; its decreased scale allows you to navigate through the entire project.
The Hierarchy Viewer is accessed from the Graphical View option in the
Schema
menu. The Hierarchy Viewer is discussed in further detail in
Using the Hierarchy Viewer, page 368
.
Configuring hierarchy display options
Each attribute in a user hierarchy has properties that affect how that attribute is displayed and accessed in a hierarchy. You can use the Hierarchy
Editor to configure each of these properties, as shown in the following procedures:
•
Element Display: Determines the elements a user can see. The element
display may be locked, unlocked, or limited (see
Controlling the display of attribute elements, page 355
).
•
Attribute Filters: Specifies whether the data retrieved and displayed should be complete or filtered by any specific criteria. A filter on a
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Project Design Guide Creating Hierarchies to Organize and Browse Attributes 9 hierarchy acts like a filter in a report. Only data satisfying the filter criteria is displayed (see
Filtering attributes in a hierarchy, page 359
).
•
Entry Point/Not an Entry Point: Specifies whether the user can begin browsing in this hierarchy using this attribute (see
).
•
Browse Attributes: Shows the attributes to which users can browse from a given attribute. Represented by lines that connect one attribute to others
(see
The following sections explain these properties and how to use the Hierarchy
Editor to configure each.
Controlling the display of attribute elements
The sections listed below describe various techniques to control the display of attribute elements:
•
Locked/Unlocked attribute elements, page 355
•
Limited attribute elements, page 357
Locked/Unlocked attribute elements
Locking a hierarchy prevents a user from viewing all elements of the specific attribute and any lower level attributes in the hierarchy. A hierarchy is referred to as
locked
when at least one attribute within that hierarchy has the
Element Display option set to Locked. Anything higher in the hierarchy is still visible.
You can lock the hierarchy to restrict the user from viewing elements and lower level attributes for security reasons or to better manage lengthy hierarchies. By restricting the view of attribute elements and lower level attributes in the Data Explorer, you can prevent the expansion of long attribute element lists that can consume system resources. When you set the element display to locked, a padlock icon appears next to the attribute name.
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For example, the attribute Order is locked in the Data Explorer sample shown below.
While the user can view the attribute elements of Customer Region and
Customer City, he or she cannot view information about each customer’s order. The Order attribute may be locked in order to prevent unauthorized users from accessing sensitive information about customer orders.
Prerequisites
•
A hierarchy has been created.
To lock or unlock an attribute in a hierarchy
1
In MicroStrategy Developer, open a hierarchy using either the Hierarchy
Editor or Architect, as described below:
•
Locate a hierarchy in the Folder List, right-click the hierarchy, and select Edit. The Hierarchy Editor opens.
•
From the Schema menu, select Architect. MicroStrategy Architect opens.
From the Hierarchy View, in the Hierarchies drop-down list, select a hierarchy.
If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the schema editor in edit mode so that you can make changes to the hierarchy.
Note the following:
•
If you are only given the option of using read only mode, this means another user is modifying the project’s schema. You
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•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
2
Lock or unlock an attribute using the options listed below:
•
To lock an attribute, right-click an attribute, point to Element
Display
, and then select Locked. A padlock icon appears next to the locked attribute, and users can no longer view elements of this attribute.
•
To unlock a locked attribute, right-click an attribute, point to Element
Display
, and then select Unlocked. The padlock icon is removed from the attribute, and users can now view the elements of this attribute.
3
In the Hierarchy Editor or Architect, click Save and Close to save your changes and return to Developer.
4
From the Schema menu, select Update Schema.
You can also lock and unlock attributes when you edit them in the Display tab of the Attribute Editor. However, this locks and unlocks the attributes within the system hierarchy, not any user hierarchies that contain the attributes. For example, if the attribute Year is locked in the Attribute Editor, no elements for Year display in the Data Explorer when Year is expanded.
Limited attribute elements
Another way to restrict users from viewing attribute elements in the Data
Explorer is to limit the number of elements that appear at one time. This method is useful when there are extensive attribute elements in a hierarchy.
Instead of loading all attribute elements at once, you can set the limit to five or ten at a time. Also, retrieving a large number of elements at once can negatively impact system performance. The user can then click the arrows to see the next set of elements for that attribute.
For example, the Chocolate subcategory, shown below, contains many items.
Rather than displaying all of them at once and overwhelming the user, a limit
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Creating Hierarchies to Organize and Browse Attributes Project Design Guide of five items has been set. The following graphic displays this view in the
Data Explorer.
Prerequisites
•
A hierarchy has been created.
To limit the display of attributes in a hierarchy
1
In MicroStrategy Developer, open a hierarchy using either the Hierarchy
Editor or Architect, as described below:
•
Locate a hierarchy in the Folder List, right-click the hierarchy, and select Edit. The Hierarchy Editor opens.
•
From the Schema menu, select Architect. MicroStrategy Architect opens.
From the Hierarchy View, in the Hierarchies drop-down list, select a hierarchy.
If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the schema editor in edit mode so that you can make changes to the hierarchy.
Note the following:
•
If you are only given the option of using read only mode, this means another user is modifying the project’s schema. You cannot use edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
2
Right-click the attribute to limit, point to Element Display, and then select Limit. The Limit dialog box opens.
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3
Type the number of elements to display at one time and click OK.
4
In the Hierarchy Editor or Architect, click Save and Close to save your changes and return to Developer.
5
From the Schema menu, select Update Schema.
Filtering attributes in a hierarchy
Before reading this section, refer to the Filters chapter in the MicroStrategy
Advanced Reporting Guide to understand what filters are and how to create them in MicroStrategy.
You can add filters to a hierarchy to control how data is retrieved and displayed. With a filter you can choose exactly which attribute elements to display in a hierarchy. For example, you can filter a hierarchy so that data for only one quarter is displayed, or data for only a few days of one quarter.
Filters make data retrieval faster by only allowing specific data to be displayed.
You cannot use a prompt-based filter to filter a hierarchy.
Each attribute in the hierarchy can have multiple filters applied to it. When filtering attributes in a hierarchy, you are limiting the elements of the data returned when you browse the Data Explorer. Creating a limited hierarchy reduces the number of elements displayed at one time. Filters, however, limit the elements a user is allowed to see and therefore, perform a type of security.
Filters increase efficiency when retrieving data because you can limit user access to parts of a hierarchy when you apply filters to attributes. The filters allow the Data Explorer to display only the criteria you select, and the user is unable to see additional data in the hierarchy.
For example, you want to view only those customers who are younger than
30 years old. First, create a filter on Customer Age less than 30. In the
Hierarchy Editor, add the filter to the Customer attribute. Update the project schema, and view the Customer hierarchy in the Data Explorer. Only those customers younger than 30 years old are displayed.
When adding filters to an attribute in a hierarchy, you need to make sure that each filter is relevant to the attribute’s information.
MicroStrategy does not validate that the associated filter makes sense on that attribute.
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Prerequisites
•
A filter has been created.
•
A hierarchy has been created.
To apply a filter to an attribute in a hierarchy
1
In MicroStrategy Developer, open a hierarchy using either the Hierarchy
Editor or Architect, as described below:
•
Locate a hierarchy in the Folder List, right-click the hierarchy, and select Edit. The Hierarchy Editor opens.
•
From the Schema menu, select Architect. MicroStrategy Architect opens.
From the Hierarchy View, in the Hierarchies drop-down list, select a hierarchy.
If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the schema editor in edit mode so that you can make changes to the hierarchy.
Note the following:
•
If you are only given the option of using read only mode, this means another user is modifying the project’s schema. You cannot use edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
2
Right-click the attribute to filter and select Define Attribute Filters.
3
If a tip about filtering opens, click OK. The Select Objects dialog box opens.
4
In the Available objects pane, select the filters to apply and click > to add them to the Selected objects pane.
5
Click OK to close the Select Objects dialog box. The attribute to which you applied the filter appears in the hierarchy with a filter icon.
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6
In the Hierarchy Editor or Architect, click Save and Close to save your changes and return to Developer.
Entry point
An
entry point
is a shortcut to an attribute in the Data Explorer. Creating an entry point grants users faster access to the attribute without having to browse through multiple attributes to reach different levels in a hierarchy.
This is especially useful when accessing frequently-used attributes.
When you create a user hierarchy, the hierarchy, the attributes, and their elements appear in the Data Explorer. When you set an attribute to be an entry point, you are creating a shorter route to access that attribute. For example, a typical hierarchy is Time. When you click on Time, elements for each Year, such as 2007, 2006, and 2005, open. When you click on 2006, an element for each Quarter, such as Q1, Q2, Q3, and Q4, opens. If you are seeking Week 24, you need to open several levels of attributes to reach the correct data level, which is Week. If you set the attribute Week as an entry point, the attribute Week appears in the Data Explorer at the same level as
Year. If an attribute is not set to be an entry point, it appears in its normal position within the hierarchy structure.
If you set a locked attribute as an entry point, it still appears in the hierarchy but with a padlock icon. You can see the locked attribute, but are unable to access elements or attributes below that level.
Prerequisites
•
A hierarchy has been created.
To create entry points in a hierarchy
1
In MicroStrategy Developer, open a hierarchy using either the Hierarchy
Editor or Architect, as described below:
•
Locate a hierarchy in the Folder List, right-click the hierarchy, and select Edit. The Hierarchy Editor opens.
•
From the Schema menu, select Architect. MicroStrategy Architect opens.
From the Hierarchy View, in the Hierarchies drop-down list, select a hierarchy.
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If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the schema editor in edit mode so that you can make changes to the hierarchy.
Note the following:
•
If you are only given the option of using read only mode, this means another user is modifying the project’s schema. You cannot use edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
2
Right-click the attribute to set as an entry point, and select Set As Entry
Point
. The attribute is marked with a green check mark to denote that it is an entry point.
To remove an entry point from an attribute, right-click an attribute and select Remove Entry Point.
3
In the Hierarchy Editor or Architect, click Save and Close to save your changes and return to Developer.
4
From the Schema menu, select Update Schema.
Hierarchy browsing
Once you choose which attributes to place in a hierarchy, you can define the relationships between them. These relationships determine how users can browse the attributes from the Hierarchies folder.
For example, if Catalog, Category, Subcategory, and Item are the attributes that comprise the user hierarchy Catalog Items, the hierarchy resembles the example below, showing the parent/child relationships between the
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A user hierarchy does not need to have these direct relationships defined. It can simply be a collection of attributes.
Attributes in a hierarchy can have both browsing and drilling relationships between them.
Browse attributes
are attributes you specify a user can directly browse to from a given attribute in the user hierarchy. When you apply browse attributes to attributes in a hierarchy, you are specifying what levels of detail are visible when browsing the Data Explorer. Including hierarchies in the Data Explorer makes the hierarchies available for reports and to users in the project. For more information on including hierarchies in
Enabling hierarchy browsing in reports: Data
For each attribute in a hierarchy, you can assign one or more browse attributes to it. Using the example above, some of these attributes have been assigned a browse attribute. Specifically:
Hierarchy Attribute
Catalog
Category
Subcategory
Item
Browse Attribute(s)
Category, Subcategory
Subcategory
Catalog, Item
The addition of these browse attributes allows users to see the Subcategory elements directly from the Catalog attribute, without having to first browse
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Creating Hierarchies to Organize and Browse Attributes Project Design Guide down through the Category attributes to get to Subcategory. The ability to browse more directly through the hierarchy can be represented as shown below.
In the Data Explorer, the hierarchy described above resembles the example below.
Users can now view the subcategories in the catalog without first having to browse through the categories.
Enabling hierarchy browsing in reports: Data Explorer
You can make hierarchies available for browsing and including in reports by storing the hierarchies in the Data Explorer. Moving hierarchies to and from this folder also allows you to keep some hierarchies visible to users while
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Data Explorer
is a tool in the Object Browser that holds the system hierarchy and the user hierarchies. When you create a new project, the system hierarchy for that project is automatically placed in the
Data Explorer.
You can save user hierarchies in any folder. However, to make a user hierarchy available for browsing in the Data Explorer you must place it in the
Data Explorer folder—a subfolder of the Hierarchies folder, which is located in the Schema Objects folder.
Drilling using hierarchies
Drilling is a function in MicroStrategy reports that allows users to browse different levels of attributes along specified paths. Depending on the level of the attributes included in the drilling specification, reports can allow users to drill down, up, and across to different levels of detail.
When a user selects a drilling path in a report, the report refreshes to display the selected level of detail. For example, on a report with the Year attribute and Revenue metric, the user can drill down on the Year attribute to a lower level attribute such as the Month attribute. A new report is automatically executed; on the new report, Revenue data is reported at the Month level.
You can make user hierarchies available for drilling. This option enables you to determine, at a project level, the attributes to which users can drill from other attributes. In the example of the Year and Month attributes, drilling is enabled in the Time hierarchy, which contains the two attributes. This allows a user to drill down from Year to Month and, if they need to, drill back up from Month to Year.
To enable a user hierarchy as a drill path, you must enable the user hierarchy to be used as a drill hierarchy in the Hierarchy Editor. If a user hierarchy is not enabled, the default drill path is defined by the System Hierarchy.
Therefore, you can think of browsing paths in a user hierarchy as potential drilling paths. For example, in the following hierarchy, Subcategory is a browse attribute of Catalog, which means that you can access the elements of
Subcategory without having to necessarily access the elements of Catalog in
Data Explorer. If you enable drilling in this hierarchy, you can drill from
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Catalog down to Subcategory—and any other browse attributes of
Catalog—on a report.
A drill hierarchy can be used for browsing as well as drilling. However, the way in which you browse attributes may not be the same way in which you drill on attributes in reports. If your drilling and browsing paths between attributes are different, you should create separate drilling and browsing hierarchies.
Prerequisite
•
A hierarchy has been created.
To define a user hierarchy as a drill hierarchy
1
In MicroStrategy Developer, open a hierarchy using either the Hierarchy
Editor or Architect, as described below.
•
Locate a hierarchy in the Folder List, right-click the hierarchy, and select Edit. The Hierarchy Editor opens.
•
From the Schema menu, select Architect. MicroStrategy Architect opens.
From the Hierarchy View, in the Hierarchies drop-down list, select a hierarchy.
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If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the schema editor in edit mode so that you can make changes to the hierarchy.
Note the following:
•
If you are only given the option of using read only mode, this means another user is modifying the project’s schema. You cannot use edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
2
To define a user hierarchy as a drill hierarchy:
•
With the Hierarchy Editor, select the Use as a drill hierarchy check box located at the bottom of the Hierarchy Editor.
•
With Architect, right-click within the Hierarchy View and select Use
As a drill hierarchy
.
3
In the Hierarchy Editor or Architect, click Save and Close to save your changes and return to Developer.
4
From the Schema menu, select Update Schema.
After a user hierarchy is enabled for drilling, the hierarchy contributes to the drilling path of any attributes in it. Additional information on drilling is available in the MicroStrategy Advanced Reporting Guide.
Using the Hierarchy Viewer and Table Viewer
Through the Hierarchy Viewer, MicroStrategy Architect gives you the ability to view the system hierarchy as well as all of your user hierarchies in a single place. The Table Viewer is another tool within MicroStrategy Architect that provides you with a bird’s eye view of some of the information within your project. It is used to view all of the tables in your project graphically.
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Using the Hierarchy Viewer
The Hierarchy Viewer allows you to select the hierarchy you want to examine, and also allows you direct access to the attributes that comprise it.
You can use the Hierarchy Viewer to view either the system hierarchy or any of your user hierarchies.
•
When you view the system hierarchy, you can see the actual relationships between attributes, as defined by the system when the project was created.
•
When you view a user hierarchy, you do not see true attribute relationships, but rather the structure of the user hierarchy as defined by a project designer, to facilitate user browsing and report development.
•
The Aerial perspective provides an overview of the hierarchies in your project. Its decreased scale allows you to navigate through the entire project.
The Hierarchy Viewer gives you flexibility over how much of a given hierarchy you choose to view at once. You can see all of the entry points into a hierarchy at once, or you may select only one at a time. For details on entry points, see
.
The Hierarchy Viewer also gives you direct access to any of the attributes in the hierarchy you choose to view. When you access a hierarchy’s attributes directly, you can define them as entry points. See
for more details on creating entry points.
To view the system hierarchy in the Hierarchy Viewer
1
In MicroStrategy Developer, from the Schema menu, point to Graphical
View
.
2
Select Hierarchies.
To view a user hierarchy in the Hierarchy Viewer
1
In the Hierarchy Viewer, from the Hierarchy menu, select the hierarchy to view.
2
Attributes that have a green check mark next to them are entry points.
for more details on creating entry points.
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To edit an attribute from the Hierarchy Viewer
1
In the Hierarchy Viewer, right-click the attribute to edit.
2
Select Edit.
To access Aerial perspective mode in the Hierarchy Viewer
1
In the Hierarchy Viewer, from the View menu, select Aerial perspective.
An aerial view of the hierarchy you are currently viewing is displayed. The green squares indicate attributes that are entry points.
2
The hierarchy in the Hierarchy Viewer shifts according to where you navigate in the aerial view. Click a section of the aerial view display to shift your view of a hierarchy to that particular section.
Using the Table Viewer
The Table Viewer allows you to view all of the tables in your project as well as the joins and/or relationships between those tables and the names of the individual columns in each table.
The tables that are displayed here are logical tables. They represent and indicate how Architect sees the tables that were brought into the project when it was created.
If you make changes to the actual tables in the data warehouse, you
will need to update the logical table structure. See
The size of tables in a project: Logical table size, page 340
for information on updating logical table structures.
You can also view all of this information using Architect, which is described in
Chapter 5, Creating a Project Using Architect
To view your project’s tables in the Table Viewer
1
In MicroStrategy Developer, from the Schema menu, point to Graphical
View
.
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2
Select Tables.
Project Design Guide
To view more or less information about each table in the project
1
Open the Table Viewer, as described above.
2
In the Table Viewer, select Options.
3
From the Options menu, select or clear the options for any of the following, depending on what you want to see in the Table Viewer:
• Show joins
• Use circular joins
• Show relationships
• Show relationship types
• Show columns
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10
10.
C
REATING
T
RANSFORMATIONS
TO
D
EFINE
T
IME
-B
ASED AND
O
THER
C
OMPARISONS
Introduction
Suppose you want to compare how much revenue your company grew last year to how much it grew this year. This type of analysis, called a TY/LY comparison (This Year versus Last Year), is a commonly used form of time-series analysis and is relevant to many different industries, including retail, banking, and telecommunications.
Transformations—schema objects you can create using attributes in your project—are one of the many MicroStrategy techniques used to perform time-series analysis.
To calculate a variance or a growth percentage such as last year’s revenue versus this year’s revenue, it is very convenient to use a transformation.
Transformations are often the most generic approach and can be reused and applied to other time-series analyses. To use a transformation, a report designer creates a metric and applies the transformation to it.
Transformation-style analysis can also be supported using the Lag and Lead functions provided with MicroStrategy. These functions can be used to define metrics that compare values from different time periods without the use of transformations. For information on using these functions to support transformation-style analysis, see the Functions Reference.
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This chapter discusses the different types of transformations and how to create them. It is assumed that you have some understanding of what metrics are, as transformation metrics are discussed in this chapter. For information on metrics and using transformations in metrics and reports, see the Metrics chapter of the MicroStrategy Advanced Reporting Guide.
Creating transformations
A
transformation
is a schema object that typically maps a specified time period to another time period, applying an offset value, such as current month minus one month.
Usually defined by a project designer, transformations are used in the definition of a metric to alter the behavior of that metric. Such a metric is referred to as a
transformation metric
. For example, time-related transformations are commonly used in metrics to compare values at different times, such as this year versus last year or current date versus month-to-date. Any transformation can be included as part of the definition of a metric and multiple transformations can be applied to the same metric.
Transformation metrics are beyond the scope of this guide; for information about transformation metrics, refer to the MicroStrategy Advanced
Reporting Guide.
Recall the example used in the introduction, the TY/LY comparison. To calculate this year’s revenue, you can use the Revenue metric in conjunction with a filter for this year. Similarly, to calculate last year's revenue, you can use the Revenue metric in conjunction with a filter for last year. However, a more flexible alternative is to use a previously created Last Year transformation in the definition of a new metric, last year’s revenue. With a single filter, on 2003 for example, the two metrics Revenue and Last Year
Revenue give you results for 2003 and 2002, respectively.
Since a transformation represents a rule, it can describe the effect of that rule for different levels. For instance, the Last Year transformation intuitively describes how a specific year relates to the year before. It can in addition express how each month of a year corresponds to a month of the prior year.
In the same way, the transformation can describe how each day of a year maps to a day of the year before. This information defines the transformation and abstracts all cases into a generic concept. That is, you can use a single metric with a last year transformation regardless of the time attribute contained on the report.
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While transformations are most often used for discovering and analyzing time-based trends in your data, not all transformations have to be time-based. An example of a non-time-based transformation is This Catalog/
Last Catalog, which might use Catalog_ID-1 to perform the transformation.
Expression-based versus table-based transformations
The definition of the association between an original value and a transformed one can be represented in an expression that uses columns of the warehouse, constants, arithmetic operators, and mathematical functions.
This is known as an expression-based transformation. However, it is sometimes desirable to precalculate these values and store them in a table designed for the transformation. This method is sometimes referred to as a table-based transformation.
The advantage of a table-based transformation is the possible use of indexing to speed query times. Another advantage is that table-based transformations provide additional flexibility beyond what formula expressions can produce.
The drawback of this kind of transformation is that it requires the creation and management of an additional table in the warehouse. However, once the table is created, it usually significantly decreases the query time. Returning to the TY/LY example, you have the option of using a simple formula such as
Year_ID - 1 in the definition of the transformation or precalculating the data and storing it in a column in a table.
A table-based transformation is required when a many-to-many transformation is performed. An example is a year-to-date calculation.
A significant advantage to the dynamic calculation of an expression-based transformation is that the database administrator does not have to create and maintain a transformation table. The drawback is that the system must perform the calculation every time.
A single transformation can use a combination of table-based and expression-based transformations. For example, you can create a last year transformation based on Year and Month. The ID of the Year attribute is in the format YYYY, so the transformation can use the expression Year_ID -
1. The ID for the Month attribute is in the format ‘MonthName,’ so you cannot easily use a mathematical expression. You must use a table instead.
The following sections walk you through creating both a table-based transformation and an expression-based one.
Creating transformations
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Building a table-based transformation
The following example shows how to create a last year transformation based on a lookup table in MicroStrategy Tutorial, which pairs each year with the previous year. This transformation is used in the report displayed below, which compares revenue for this year and last year.
Creating the transformation metric and the report are discussed in the
Transformation metrics section in the Metrics chapter of the
MicroStrategy Advanced Reporting Guide.
To create a last year transformation based on a table
1
Log in to the project source that contains your project in MicroStrategy
Developer and expand your project.
2
From the File menu, point to New, and select Transformation. The
Transformation Editor opens with the Select a Member Attribute dialog box displayed.
3
Double-click Time to open the folder, then double-click Year. The Year -
Define a new member attribute expression dialog box opens.
4
Select the LU_Year table from the Table drop-down list. The table's columns appear in the Available columns list. Notice that this table contains a previous year column, which maps this year to last year.
5
Double-click the PREV_YEAR_ID column to place it in the expression box.
6
Click OK.
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7
Click Save and Close on the toolbar. Name the transformation Last
Year (Table)
.
You have now created the transformation. A report designer can now use the transformation in a revenue metric to calculate last year’s revenue, then create a report using that transformation metric to obtain last year’s revenue.
Building an expression-based transformation
This example shows how to create a last year transformation using an expression rather than a table. The Year_ID is in the format YYYY, so the previous year is simply Year_ID minus one. For example, one subtracted from the year 2005 results in the previous year, 2004.
This transformation is added to the report shown in the table-based transformation example above. The resulting report is displayed below.
Note the following:
•
Creating the transformation metric and the report are discussed in the Transformation metrics section in the Metrics chapter of the
MicroStrategy Advanced Reporting Guide.
•
The performance of reports that use expression-based transformations can be improved in certain scenarios using the
Transformation Optimization VLDB property. For information on this VLDB property and how it can improve report performance, see the Supplemental Reference for System Administration.
Creating transformations
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To create a last year transformation based on an expression
1
In MicroStrategy Developer, from the File menu, point to New, and select
Transformation
. The Transformation Editor opens with the Select a
Member Attribute dialog box displayed.
2
Double-click Time to open the folder, then double-click Year. The Year -
Define a new member attribute expression dialog box opens.
3
Select the LU_Year table from the Table drop-down list. The table's columns appear in the Available columns list.
4
Double-click the YEAR_ID column to place it in the expression box.
5
Type -1 in the expression box. The transformation will subtract one from the Year ID to calculate last year’s ID.
6
Click Validate. The message “Valid expression” appears with a green check mark.
7
Click OK.
8
Click Save and Close on the toolbar. Name the transformation Last
Year (Expression)
.
You have now created the last year transformation. A report designer can now use the transformation in a revenue metric to calculate last year’s revenue, then add it to the report created in the previous example.
Transformation components
All transformations have the following components:
•
Member attributes: This component contains the attributes to which the transformation applies, that is, the different levels to which the rule applies.
For example, in the Last Year transformation in the MicroStrategy
Tutorial, the member attributes are Year, Quarter, Month, and Day.
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•
Member tables: These tables store the data for the member attributes.
For an expression-based transformation, each member expression is based on a specific table, generally the lookup table corresponding to the attribute being transformed.
For a table-based transformation, this is the transformation table defining the relationship. For example, in the Last Year transformation, the member tables are LU_YEAR, LU_QUARTER,
LU_MONTH, and LU_DAY, for the member attributes Year, Quarter,
Month, and Day, respectively.
•
Member expressions: Each member attribute has a corresponding expression.
For an expression-based transformation, this is a mathematical expression. In the most generic case, this expression uses constants, arithmetic operators, mathematical functions, and columns from the warehouse, typically the attribute ID column.
For example, you can create a Last Year transformation using
Year_ID-1 as the expression. However, many cases can exist where the data is not conducive to such calculation. For instance, if you store
Month as 200001 (January 2000), you cannot subtract one and receive December 1999 as the result.
For a table-based transformation, this is simply a column from a specific warehouse table specifically populated with data supporting the transformation. The rule is then not encapsulated in an expression but directly in the data of the column. Since the data defines the rule, this approach provides considerable flexibility in the transformation definition. It is particularly effective when no straightforward formula can express the rule. In fact, in the case of a many-to-many transformation, a separate table is required.
For example, in the Last Year transformation, the member expressions are LY_DAY_DATE, LY_MONTH_ID,
LY_QUARTER_ID, and PREV_YEAR_ID. These are all columns from the lookup tables set in the Member tables field.
•
Mapping type: This component determines how the transformation is created based on the nature of the data. The mapping can be one of the following:
One-to-one: A typical one-to-one relationship is “last year to this year.” One day or month this year maps exactly to one day or month from last year.
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Many-to-many: A typical many-to-many relationship is year-to-date.
For one date, many other dates are included in the year-to-date calculation.
Many-to-many transformations can lead to double-counting scenarios. For example, consider YearToDate defined as a many-to-many transformation and Revenue (YTD) as a transformation metric. Suppose this metric is used on a report that does not include the Day attribute, which is the member attribute on the template. In the report, a range of dates is specified in the filter. In this instance, the Revenue (YTD) metric will double count.
Transformation metrics and joint child attributes
Review the discussion of joint child attributes and relationships in
Joint child relationships, page 274
before proceeding in this section.
In a report, a transformation metric displays the current attribute with transformed data, that is, the values for the transformation. For example, a report contains Quarter and the transformation metric Last Year’s Revenue.
Each quarter is displayed, with the previous year’s revenue, as shown below:
When a joint child attribute, which is an attribute that exists at the intersection of other indirectly related attributes, is added, this affects how the attribute elements and transformation values are displayed.
For example, the joint child attribute Promotion is added to the previous report. The report displays the quarter, the promotion associated with a given quarter, and the revenue data from the date-promotion combination, minus one year. To demonstrate the effect this has on the display of data on
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2010, without any transformations:
The highlighted data in the report shown above indicates that in the fourth quarter of 2009, there was $52,529 of revenue related to the Memphis
Special Sale promotion. You can also see in this report that the Memphis
Special Sale promotion was not held in the fourth quarter of 2010. The report below shows how this data is displayed when a transformation of the data is used:
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In the report shown above, notice that the Memphis Special Sale is displayed with the fourth quarter of 2010, and the fourth quarter of 2009 has no listing of this promotion. This can seem contradictory as compared to the previous report that did not include a transformation on the data. However, this is due to the fact that the Last Year’s Revenue metric is displaying data for the previous year for each attribute element. Since the joint child attribute is dependent on time, the attribute elements also must change to reflect the transformation of data. This results in a report that displays the Memphis
Special Sale data for the fourth quarter of 2010, because this was the revenue that was generated for this quarter during the previous year.
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11
11.
D
ESIGNING A
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INANCIAL
R
EPORTING
Introduction
Financial reporting and analysis is important to every business. This can include standard reporting such as profit and loss reporting that provides analysis of a company’s profits compared to its losses. An example profit and loss report is shown below:
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While financial reporting is an important aspect of any company, it can be difficult to support and achieve through a standard relational database. This is partly due to the additional context and hierarchical structure that is required for the data.
In MicroStrategy, data such as Revenue and Gross Revenue is normally mapped to metrics. However, the additional context and organization required for this data is better suited to attributes in a MicroStrategy system.
Since MicroStrategy provides the flexibility to integrate data from your relational database as either metrics or attributes, this allows you to support financial reporting and analysis requirements.
Prerequisites
The best practices listed in this chapter can help you design a financial reporting project in MicroStrategy. To create this type of financial reporting project in MicroStrategy, you should also have knowledge of the following:
•
Understanding of how to store and maintain data in a database. The best practices provided to support a financial reporting project in
MicroStrategy require a specific physical warehouse design.
•
Expertise in the following MicroStrategy techniques and features:
Project design
Metric and report creation
•
Knowledge of your financial data, including:
Logical data model for your financial data, such as whether your financial data requires additional context including account, vendor, customer, and time. The best practices provided include a high-level discussion of creating context for your financial data, but it is assumed that you have a defined logical data model for your financial data.
The calculations required to create your financial data. Some examples of financial calculations are provided, but it is assumed that you know the calculations required to return the financial data relevant to your financial reporting requirements.
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Physical warehouse requirements
Since financial reporting often requires data that would normally be integrated as metrics in MicroStrategy to act more like attributes in
MicroStrategy, the physical warehouse needs to be able to support this difference.
Creating tables to provide hierarchical organization
As shown in the example financial report below, the metric data is presented in a hierarchical organization similar to that of attributes in MicroStrategy:
To create this structure, you store each element in lookup tables, and then define the hierarchical relationship between the elements using a relationship table.
Creating the lookup tables
The lookup tables define the hierarchical structure of your financial data for display on financial reports.
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To illustrate these techniques, below are examples of the Level 1, Level 2, and
Level 3 tables that would need to be created for the example financial report provided in
Level1_ID
3
4
1
2
5
Level1_DESC
Revenue
Gross Profit
Operating Income
EBITDA
Net Income
Level2_ID
-3
-4
-5
9
-1
-2
7
8
5
6
3
4
1
2
Level2_DESC
Taxes null null null null null
Cost of Revenues
Gross Profit Margin
Operating Expense
Deprecation Expense
Operating Margin
EBIT
Interest Income
Interest Expense
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Level3_ID
1
2
Level3_DESC
Other Income
Interest Coverage Ratio
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Project Design Guide
Level3_ID
-9
-10
-11
-12
-13
-14
-7
-8
-5
-6
-3
-4
-1
-2
Designing a Project for Financial Reporting 11
Level3_DESC
null null null null null null null null null null null null null null
To create the lookup tables
1
Gather the following information:
•
What is the deepest level of elements for financial reporting? In other words, what is the maximum number of levels that are used to represent any given element in your financial report?
In the example financial report, the elements Other Income and
Interest Coverage Ratio are both at the deepest level for the financial report, which uses three levels.
The number of levels needed is the number of lookup tables that need to be created to represent this organization in your physical warehouse.
•
How many elements are included in each level?
In the example financial report, Level 1 has five items, Level 2 has nine items, and Level 3 has two items.
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The number of items in each level is used to determine how many elements are needed in each lookup table.
With this information, you can create a lookup table for each level, starting with Level 1, the highest level.
2
To create the first lookup table, create an ID column to uniquely identify the item, and a description column that provides the name of the element when displayed on financial reports. It is recommended that you use a simple sequential list of positive numbers for the ID values. This lookup table defines the elements for the Level 1 attribute. The table contains all of the elements that are displayed at the highest level. An example of a
Level 1 table is provided in
Creating the lookup tables, page 383
3
Create a lookup table for each additional level of elements that is required. In addition to sharing all of the requirements for the first lookup table, each successive table must also meet the following requirements:
•
In addition to storing each element at the associated level, null or dummy elements must also be included. These elements are required to ensure that the hierarchical structure can be supported in
MicroStrategy reports. When creating dummy elements, use the following guidelines:
Include the regular elements as the first elements in the table, using the same standard of sequential ID values and description information.
Include dummy rows equal to the total number of elements in the next highest level lookup table, including both regular elements and dummy elements.
For example, if you are creating the Level 2 table, you need to create a number of dummy elements equal to the total number of elements in the Level 1 table. If you are creating the Level 3 table, you need to create a number of dummy elements equal to the total number of elements in the Level 2 table.
For each dummy element, it is recommended that you use sequential, negative values for the ID value. For example, the first dummy element of a table should have -1 as its ID value, the second dummy element should use -2, and so on.
Leave the description column blank for any dummy elements, so that it has a null or empty value.
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An example of a Level 2 and Level 3 tables is provided in
Creating the lookup tables, page 383
. In these example tables, notice that the Level 2 table has five dummy elements. This is because the Level 1 table has five total elements. The Level 3 table has 14 dummy elements. This is because the Level 2 table has 14 total elements when counting both the regular and dummy elements. The dummy elements for each table also use sequential, negative values for the ID values of the dummy elements.
Creating a relationship table
Once you have the lookup tables created, you must create a relationship table to define the hierarchical organization of the level attributes.
To create a relationship table
1
To determine how to assign matching elements between the lookup tables, you need to combine all of the regular elements from all tables together, mimicking the hierarchical organization required for the final report. For example, using the three lookup tables described above, the regular elements and their ID values would be combined as follows:
3
3
2
2
3
3
2
2
1
1
Level1_ID Level1_DESC Level2_ID Level2_DESC
Revenue
Gross Profit
Operating
Income
1
2
3
4
5
5
6
Cost of Revenues
Gross Profit Margin
Operating Expense
Deprecation Expense
Operating Margin
EBIT
Level3_ID Level3_DESC
1 Other Income
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4
4
4
4
4
5
Level1_ID Level1_DESC Level2_ID Level2_DESC
EBITDA
Net Income
7
8
8
9
Interest Income
Interest Expense
Taxes
Level3_ID Level3_DESC
2 Interest Coverage Ratio
This mimics the organization of the final required report, and provides a relationship between all of the regular elements for the three level lookup tables.
2
Assign the dummy elements to fill out the structure. It is recommended to assign these elements in reverse order. For example, the first element,
Revenue, is assigned the lowest dummy element value for the Level 2 table. Using this strategy, the table is defined as follows:
Level1_ID Level1_DESC Level2_ID Level2_DESC
3
3
4
2
3
3
2
2
1
1
2
4
4
4
4
5
Revenue
Gross Profit
Operating
Income
EBITDA
Net Income
-5
1
-4
2
3
4
-3
5
5
6
-2
7
8
8
9
-1
Cost of Revenues
Gross Profit Margin
Operating Expense
Deprecation Expense -9
-8
Operating Margin -7
EBIT
Interest Income
Interest Expense
Taxes
Level3_ID Level3_DESC
-4
-3
2
-2
-1
1
-6
-5
-14
-13
-12
-11
-10
Other Income
Interest Coverage Ratio
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3
Since the relationship table is only needed to create a relationship between the level attributes, you can exclude all description information from the table. This leaves you with the following relationship table, which defines the hierarchical organization of the level attributes in the example above:
9
-1
8
8
-2
7
5
6
-3
5
3
4
-4
2
-5
1
Level1_ID Level2_ID Level3_ID
4
5
4
4
4
4
3
3
3
3
2
2
2
2
1
1
-2
-1
-3
2
-5
-4
1
-6
-8
-7
-10
-9
-14
-13
-12
-11
Notice the ID column for the lowest level attribute in this table, in the example above, Level3_ID. There is a unique value in this ID column for each entry, which means that this column can be used as the primary key of the table. Additionally, this single ID column can be used to uniquely identify each financial line item. This value is used later to identify each financial line item.
This table data is used to create attributes to represent your financial line items in financial reports. Information on creating these attributes is included in
Creating attributes for financial line items, page 392
.
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Storing financial data
After you create the lookup tables and relationship table to create the hierarchical structure of your financial data, you must store and categorize the financial data itself. This data includes all of the various financial accounts that you plan to include in your financial report. For example, recall the following example financial report:
Each separate line item on the report is an account, and this data needs to be stored so that it can be retrieved by MicroStrategy for financial reporting.
There are various ways in which you can store data in a database. To facilitate financial reporting in MicroStrategy, it is recommended to store all of the data for all financial accounts in a single column, at the lowest logical level. This results in a table with multiple columns that will be modeled as attributes in MicroStrategy, and a single column that stores all financial data.
For example, the table would have a structure similar to the following:
Day
1/1/2012
1/1/2012
1/1/2012
...
Other Attributes Financial Account Data
Account, Vendor, Customer, etc. -14 (Revenue) 300
Account, Vendor, Customer, etc. -13 (Cost of Revenue) 500
Account, Vendor, Customer, etc. -12 (Gross Profit)
...
...
125
...
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Day
1/2/2012
1/2/2012
1/2/2012
...
Other Attributes Financial Account Data
Account, Vendor, Customer, etc. -14 (Revenue) 480
Account, Vendor, Customer, etc. -13 (Cost of Revenue) 320
Account, Vendor, Customer, etc. -12 (Gross Profit)
...
...
95
...
When storing your data in a table with this structure:
•
Include a column for each category that gives context to the financial data. For example, you might need to know the account, vendor, or customer for a given piece of financial data, as well as the day when the transaction occurred. This should include all aspects of the financial data at the lowest logical level of data. This concept is discussed more in detail in
Fact table levels: The context of your data, page 44
The concepts related to fully categorizing your data are discussed in
Chapter 2, The Logical Data Model
. Be aware that each category required needs to be fully defined in your database. This means you must create lookup tables to provide the necessary, descriptive information to describe these categories. For additional details on lookup table design, see
Lookup tables: Attribute storage, page 39
•
Include a column that identifies which financial line item the data is associated with. These identification numbers must match the ID column of the lowest level lookup table you created to define the hierarchical
organization of the final financial report (see
Creating tables to provide hierarchical organization, page 383
). In the example scenario, the
Revenue line item is associated with the value -14 in the lowest level lookup table, which is LU_LEVEL3. So any entry of Revenue data must use the value of -14 in this Financial Account column to identify it as
Revenue data.
•
Include a column that stores all of the financial data for each separate financial line item. This data is entered for each line item for every record that is applicable.
Do not include data for any financial line items that are percentages. This includes financial line items such as margins and ratios. For example, data such as Gross Profit Margin should not be included in this table. This data is created later in MicroStrategy with the use of metrics.
The resulting table has more rows than columns. This is because, rather than storing the financial data in their own separate columns, each entry is stored in a single column.
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Designing the financial reporting project
With your financial data stored in a database as described in
Physical warehouse requirements, page 383
, you are ready to integrate the data into
MicroStrategy. This is accomplished by adding your tables into
MicroStrategy and creating attributes, facts, and the other schema objects required to support the financial information.
Creating attributes for financial line items
Once you have created the lookup tables and relationship table in your
Creating tables to provide hierarchical organization, page 383
) you can include them in your MicroStrategy project for financial
reporting. With the tables included in a MicroStrategy project, you can then assign the values to attributes. You can use MicroStrategy Architect to perform the tasks outlined below. Details on how to use Architect are provided in
Chapter 5, Creating a Project Using Architect
.
For your financial reporting project, each lookup table is represented in
MicroStrategy as a single attribute representing that level of elements, with the attribute ID form representing the ID values in the table, and the attribute description form representing the description values in the table.
The image below shows the three lookup tables and the one relationship table, from the examples above, included in a project.
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To create attributes for financial line items
1
Create attributes for each level, and map the attribute forms to the corresponding columns of data in the level lookup tables.
2
Create the relationships between the level attributes you created, to represent the hierarchical structure of the financial information. The relationships can be simple one-to-many relationships, with the highest level attribute being directly related to the second highest level attribute, the second highest level attribute being directly related to the third highest related attribute, and so on. An example of this is shown below for attributes Level1, Level2, and Level3.
3
Create a hierarchy that represents these attributes as a single, logical group. This hierarchy is used later when creating metrics for the project, as described in
Returning financial data with metrics, page 397
background information on hierarchies, see
Hierarchies to Organize and Browse Attributes
to create a new hierarchy for these attributes, see
Creating user hierarchies, page 348
.
Displaying financial line item attributes in exported results
As part of defining the attributes for your financial line items, you can ensure that these attributes are displayed correctly when your financial reports are exported to Microsoft Excel or as a PDF using MicroStrategy Web.
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When a report that includes these financial line item attributes is exported to
Excel or as a PDF using MicroStrategy Web, all rows of data are exported.
Since these financial line item attributes include empty elements to support the hierarchical structure of the financial data, this causes some of the rows of the exported report to also be empty.
To prevent this, you can identify which of the financial line item attributes include empty elements. These elements are then ignored when reports including those attributes are exported using MicroStrategy Web.
To display financial line item attributes in exported results
1
Determine the ID values for the attribute objects: a In MicroStrategy Developer, navigate to the attribute, right-click the attribute, and select Properties. The Properties dialog box opens. b Make a note of the alphanumeric string of characters listed as the ID value. You must determine this ID value for each attribute that
includes empty elements. In the example described in
Creating attributes for financial line items, page 392
Level3.
2
Right-click the project that stores your financial data and select Project
Configuration
. The Project Configuration Editor opens.
3
On the left, expand Project definition, and then select Advanced.
4
In the Project-Level VLDB settings area, click Configure. The VLDB
Properties Editor opens.
5
From the Tools menu, ensure that Show Advanced Settings is selected.
6
In the VLDB Settings pane, expand Export Engine, then select the GUID
of attributes in profit and loss hierarchy (separated by ‘:’) that has dummy rows to be removed
VLDB property.
7
Clear the Use default inherited value check box.
8
In the text field, type each ID value for all financial line item attributes that include empty elements. Use a colon (:) to separate each attribute ID value.
9
Click Save and Close to save your changes and close the VLDB
Properties Editor.
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When you use MicroStrategy Web to export a report that includes financial line item attributes, the empty elements will not be shown in the exported data.
Creating schema objects for financial data
Storing financial data, page 390
show you how to include your financial data in your database, in a MicroStrategy fact table.
With this data stored in a fact table, you are ready to create schema objects to integrate the financial data into a MicroStrategy project.
Your fact table should include a single column that stores the financial data for all of the financial accounts.
To create schema objects for financial data
1
Using MicroStrategy Architect, you must create a fact to integrate the data into the project. This can be a simple fact that points directly to the column in your database that stores the financial data. For steps to create
Creating and modifying facts, page 138
2
Each category used to define the level of data in this fact table must be included in MicroStrategy as an attribute. In addition to the fact table, you may have additional lookup tables that provide the descriptive data for these categories. Within the fact table, the ID column of each category should be included. The image below shows an example of what the fact table might look like in MicroStrategy, with a single fact column (Data) of financial data, and additional attributes to categorize the financial data.
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Notice that a Level3 attribute is included in the fact table above. This is the lowest level attribute included as part of creating the financial line items, as described in
Creating attributes for financial line items, page 392
. It is important to include this column of data and associated attribute in this fact column because it identifies which financial line item is associated with each value in the Data column. Without this column, you cannot accurately identify the values for the Data fact that integrates this data into MicroStrategy.
3
For additional details on how to create the other attributes required to categorize your financial data, see
Chapter 7, The Context of Your
.
Creating reporting objects for financial data
Now that you have a basic project designed to integrate your financial data into MicroStrategy, you can create the remaining objects required to report on your financial data. This includes building blocks such as filters and metrics, along with the reports themselves.
Determining financial line items using filters
Storing financial data, page 390
, all financial data is represented with a single fact column. In MicroStrategy, a filter is required to determine how data is related to each financial line item displayed in the report.
This can be achieved by creating a filter for each financial line item. Each filter is defined as an attribute qualification, with the following definition:
LevelN (ID) Exactly Value
In the definition provided above:
•
LevelN is the lowest level attribute used to define the hierarchical
organization of the financial line items, as described in
Creating tables to provide hierarchical organization, page 383
•
Value is the value that corresponds to the financial line item, as defined
in the relationship table created in
Creating tables to provide hierarchical organization, page 383
.
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For example, in
Creating tables to provide hierarchical organization, page 383
, level attributes and a relationship table were created to build the
organization of the report shown below.
Revenue is displayed in the report along with other financial line items. In the relationship table for this example, the value for the ID column of the lowest level attribute (Level3) that is associated with the Revenue financial line item is -14. So the filter definition for this line item is as follows:
Level3 (ID) Exactly -14
You can use the relationship table you created in
Creating tables to provide hierarchical organization, page 383
to determine the value for each financial line item, and then use this information to create a filter for each financial line item. For details to create filters, see the Basic Reporting Guide and the Advanced Reporting Guide.
Returning financial data with metrics
Metrics allow you to further integrate your data into MicroStrategy and display this data on reports. To support this financial reporting project, you need to create some specific metrics.
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Retrieving financial data from the database
Since you modeled your data to store all financial data in a single fact column, you need to create a metric that returns all of the data that is stored in the database. The definition for this metric is as follows:
Sum(Fact) {~+, <[Level]+}
In the definition provided above:
•
Fact is the single fact you created for your financial data, as described in
Creating schema objects for financial data, page 395
provided, the fact is named Data.
•
Level is the hierarchy created for the attributes used to define the hierarchical organization of the financial line items, as described in
Creating tables to provide hierarchical organization, page 383
example provided, the hierarchy is named Level.
The level of this metric is defined as {~+, <[Level]+}, which indicates that this metric is not aggregated over the Level attributes that define the hierarchical structure of the financial line items. This ensures that subtotals are calculated correctly.
For example, the definition for this metric can be as follows:
Sum(Data) {~+, <[Level]+}
You can use any name for this metric; for this example the metric is named
StoredData.
Metrics for financial line items with percentage values
All financial line items that are percentages, margins, ratios, and so on are not stored in the database, as described in
Storing financial data, page 390
These instead need to be created as metrics in MicroStrategy.
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For example, in the report shown below, the financial line items that are not stored in the database include Gross Profit Margin, Operating Margin, and
Interest Coverage Ratio.
Determine the general calculation for each of these metrics. In the example, these metrics would have the following general calculations:
•
Gross Profit Margin can be defined as Gross Profit / Revenue.
•
Operating Margin can be defined as Operating Income / Revenue.
•
Interest Coverage Ratio can be defined as Operating Income / Interest
Expense.
With these general definitions, you need to define the metrics so that they represent these values. Since each of these metrics relies on other financial line items, you need to determine how to return the values for each financial line item. This can be done with a definition with the following syntax:
Sum(Fact) {~+, !Level+} <[FinancialLineItemFilter]; @2;
->
In the definition provided above:
•
Fact is the single fact you created for your financial data, as described in
Creating schema objects for financial data, page 395
provided, the fact is named Data.
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•
Level is the hierarchy created for the attributes used to define the hierarchical organization of the financial line items, as described in
Creating tables to provide hierarchical organization, page 383
example provided, the hierarchy is named Level.
The level of this metric is defined as {~+, !Level+}, which indicates that this metric does not include a group-by on the Level attributes that define the hierarchical structure of the financial line items. This ensures that the division calculation can be performed accurately.
•
FinancialLineItemFilter is the filter used to determine the data for the specific financial line item. Creating these filters is described in
Determining financial line items using filters, page 396
.
•
The metric definition parameters @2; and - define how the filter is applied to the metric.
In this example, this definition can be used to create the two values required for Gross Profit Margin:
•
Gross Profit = Sum(Data) {~+, !Level+} <[GrossProfit]; @2;
->
•
Revenue = Sum(Data) {~+, !Level+} <[Revenue]; @2; ->
To create the metric for Gross Profit Margin, you include these two definitions of financial line items in one metric definition for Gross Profit
Margin:
Sum(Data) {~+, !Level+} <[GrossProfit]; @2; -> /
Sum(Data) {~+, !Level+} <[Revenue]; @2; ->
All metrics for financial line items can be created with this same format; for this example, you would create the following metric definitions:
•
Operating Margin = Sum(Data) {~+, !Level+}
<[OperatingIncome]; @2; -> / Sum(Data) {~+, !Level+}
<[Revenue]; @2; ->
•
Interest Coverage Ratio = Sum(Data) {~+, !Level+}
<[OperatingIncome]; @2; -> / Sum(Data) {~+, !Level+}
<[InterestExpense]; @2; ->
Notice that the only difference between these metrics is the filters used to identify the required financial line items.
You must define each of these metrics as a smart metric. When creating the metric in the Metric Editor, on the Subtotals/Aggregation tab, select the
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Allow Smart Metric
check box. This ensures that the division is calculated correctly. For best practices in using smart metrics, refer to the Advanced
Reporting Guide.
Financial data metric
When displaying reports of your financial data, the values are all displayed using a single metric. This metric combines the values stored in your database with the values of the metrics that calculate the percentage financial line items into a single metric.
To create the financial data metric
1
To create the metric that combines all of the data for your financial line items into a single metric, you need to create the following metrics: a A metric that retrieves all of the financial data that is stored in the database. Creating this metric is described in
Retrieving financial data from the database, page 398
b A metric that calculates the financial line items that are not stored in the database, and instead are created within MicroStrategy. Creating these metrics is described in
Metrics for financial line items with percentage values, page 398
.
c A metric that helps to determine whether the data for a financial line item is retrieved from the database, or from a metric in MicroStrategy.
The syntax for this metric is:
Min([LevelN]@ID) {<Level+}
In the definition provided above:
LevelN is the lowest level attribute created to help define the hierarchical organization of the financial line items, as described in
Creating tables to provide hierarchical organization, page 383
. In the example provided, the attribute is named Level3.
Level is the hierarchy created for the attributes used to define the hierarchical organization of the financial line items, as described in
Creating tables to provide hierarchical organization, page 383
. In the example provided, the hierarchy is named Level.
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Based on the example, the definition for this metric is as follows:
Min([Level3]@ID) {<Level+}
In addition to this metric definition, you must define the subtotal function for this metric to be Minimum. When creating the metric in the Metric Editor, on the Subtotals/Aggregation tab, in the Total subtotal function drop-down list, select Minimum.
You can use any name for this metric; for this example, the metric is named Levels.
2
With these metrics, you are ready to build the single metric that combines all of your financial line item data into one metric. The syntax for this metric is:
Case(([Levels] = Value1), [PercentageMetric1], ... ,
([Levels] = ValueN), [PercentageMetricN],
[StoredData])
In the definition provided above:
•
Levels is the metric you created above to determine whether the data for a financial line item is retrieved from the database, or from a metric in MicroStrategy.
•
([Levels] = Value1), [PercentageMetric1], ... ,
([Levels] = ValueN), [PercentageMetricN] is the part of the metric definition that retrieves the values for all financial line items that are created using metrics in MicroStrategy, as described in
Metrics for financial line items with percentage values, page 398
Value1 through ValueN refer to the values that correspond to the financial line item. You can use the relationship table you created in
Creating tables to provide hierarchical organization, page 383
determine the value for each financial line item.
PercentageMetric1 through PercentageMetricN refer to the metrics you created for the financial line items created as metrics to support percentages.
•
StoredData is the metric that retrieves all of the financial data that is stored in the database. Creating this metric is described in
Retrieving financial data from the database, page 398
In this example, the metric needs to determine whether to use one of four different metrics as the source of data for the financial line item. This includes the metrics for Gross Profit Margin, Operating Margin, Interest
Coverage Ratio, and the StoredData metric that retrieves all the financial
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Case(([Levels] = -11), [GrossProfitMargin], ([Levels]
= -7), [OperatinMargin], ([Levels] = 2),
[InterestCoverageRatio], [StoredData])
This metric selects data from the correct source for each financial line item. You can use any name for this metric; for this example, the metric is named Values. This Values metric is the metric that is included directly on reports for your financial reporting project.
3
To ensure that the Values metric is displayed correctly, you must also perform the following configurations for this metric.
a Open the metric in the Metric Editor and switch to the Subtotals/
Aggregation
tab.
b From the Total subtotal function drop-down list, select Default. c Select the Allow Smart Metric check box near the bottom of the interface.
d From the Tools menu, select Metric Join Type. The Metric Join Type dialog box opens.
e Clear the Use default inherited value check box. f Select the Outer option, and click OK to return to the Metric Editor. g Click Save and Close to save your changes and close the Metric
Editor.
Creating reports for your financial data
Once you have created all of the metrics, filters, and other supporting objects for you financial project, you are ready to begin creating reports on your financial data.
When you create these reports, include the following objects on the reports.
To create reports for your financial data
1
Include the attributes used to create the hierarchal organization of the financial line items. Creating these attributes is described in
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attributes for financial line items, page 392
. As highlighted in the report
below, these attributes provide the names of the financial line items as well as their overall structure:
2
Include the single metric that returns all the data for your financial line
items. Creating this metric is described in
Financial data metric, page 401
. This single metric is highlighted in the report below:
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3
Create thresholds that apply any required metric formatting for the different financial line items. For example, some of the financial line items can be formatted as percentages. For any financial line items that should be formatted as percentages, you must create thresholds to apply this formatting. For example, the report shown below has two financial line items at Level2 and one financial line item at Level3 that need to be displayed as percentages.
You create thresholds for the financial items at each level. For example, the thresholds below define the percentage formatting for the financial line items shown in the report above:
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4
In addition to these percentage formatting thresholds, you must ensure that the percentage formatting is not applied to any financial line items incorrectly.
For example, by applying the percentage format to the Operating Margin financial line item, this also applies the percentage formatting to the
Other Income financial line item. This is because Other Income is within the Operating Margin group. To prevent the Other Income financial line item from using the percentage formatting, you can apply an additional threshold for this financial line item with its required formatting. This threshold should also be included above the threshold for Operating
Margin, which ensures that the Operating Margin threshold is performed first, and then the Other Income threshold overrides that threshold.
These thresholds are displayed below.
Notice that these thresholds are applied to both the metric values and the subtotal values, as indicated by the selection of the metric and subtotal icon on the far right of the toolbar.
5
In addition to these required objects, you can also include any other attributes required to provide additional context to the financial data.
As you create these initial reports for your financial reporting project, you can take advantage of the other reporting and analysis capabilities available in MicroStrategy. Refer to the Basic Reporting Guide and the
Advanced Reporting Guide for additional steps, examples, and best practices for reporting and analysis in MicroStrategy.
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Once the reports for your financial data are created, they can be viewed and analyzed using MicroStrategy Web. Features such as outline mode, graphing, and other analysis features can be used in MicroStrategy Web to further the reporting and analysis capabilities of your financial data.
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A.
M
ICRO
S
TRATEGY
T
UTORIAL
A
Introduction
This appendix provides information on the MicroStrategy Tutorial, including the data model and physical warehouse schema.
What is the MicroStrategy Tutorial?
The MicroStrategy Tutorial is a MicroStrategy project, which includes a warehouse, and a set of demonstration applications designed to illustrate the platform’s rich functionality. The MicroStrategy Tutorial Reporting metadata is provided as part of the MicroStrategy Analytics Module metadata.
A project is the highest-level of intersection of a data warehouse, metadata repository, and user community. Conceptually, the project is the environment in which all related reporting is done. A typical project contains reports, filters, metrics, and functions. You create projects that users access to run reports.
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The theme of the MicroStrategy Tutorial project is a retail store for the time
2009 to 2012 that sells electronics, books, movies, and music. The key features of the MicroStrategy Tutorial project include the following:
•
Hierarchies, including Customer, Geography, Products, and Time. Each hierarchy can be viewed graphically through MicroStrategy Developer and MicroStrategy Web.
•
Numerous customers and purchased items.
•
Reporting areas: Customer Analysis, Enterprise Performance
Management, Human Resources Analysis, Inventory and Supply Chain
Analysis, Sales and Profitability Analysis, and Supplier Analysis.
•
Options to create reports from MicroStrategy Developer and
MicroStrategy Web focusing on a particular analysis area, such as
Customer, Inventory, Time, Products, Category, Employee, or Call
Center.
MicroStrategy Tutorial reporting areas
MicroStrategy Tutorial reports and documents are grouped into various folders within the Public Objects\Reports folder of the MicroStrategy
Tutorial project. These reports and documents are grouped into the following folders:
• Business Roles
: This folder contains subfolders that reflect different types of business intelligence users within an organization, including
Billing Managers, Brand Managers, Category Managers, Company
Executives, District Sales Managers, Operations Managers, Regional
Sales Managers, and Suppliers.
Each subfolder contains reports that would be of interest to the type of business user for which the subfolder is named. For instance, the
Billing Managers folder contains an Invoice report and a customer-level transaction detail report. The Supplier folder contains a
Supplier Sales report, and the Brand Managers subfolder contains a report called Brand Performance by Region.
• Dashboards and Scorecards
: This folder contains various examples of different types of scorecards and dashboards. Using of MicroStrategy
Report Services documents, you can create scorecards and dashboards. A
Report Services document of this type is a visually intuitive display of data that summarizes key business indicators for a quick status check.
Dashboards usually provide interactive features that let users change how they view the dashboard’s data. For information on using dashboards,
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and Dashboard Analysis Guide.
• Enterprise Reporting Documents
: This folder contains examples of different types of standard enterprise reporting documents, such as scorecards and dashboards, managed metrics reports, production and operational reports, invoices and statements, and business reports. They are a sample of the types of reporting documents that can be built using
MicroStrategy Report Services.
• MicroStrategy Platform Capabilities
: This folder contains examples of many of the sophisticated capabilities within the MicroStrategy platform.
Evaluators of the software, as well as customers, can use the examples to get a better feel for many of the platform’s capabilities. Customers can use the examples to guide the development of there own MicroStrategy applications.
The subfolders under these folders are named according to the capabilities that their reports exemplify. For example, the Graph
Styles folder contains examples of most of the graph types that can be created in MicroStrategy, the MicroStrategy Data Mining
Services folder contains examples of Linear Regression models and other data mining models built within MicroStrategy, and the
MicroStrategy Mobile folder contains examples of reports and documents that can be viewed on a mobile device through the use of
MicroStrategy Mobile.
• Subject Areas
: This folder contains reports that are categorized further by topic. Topics covered include Customer Analysis, Enterprise
Performance Management, Human Resource Analysis, Inventory and
Supply Chain Analysis, Sales and Profitability Analysis, and Supplier
Analysis.
Customer Analysis
: Reports analyzing the customer base, studying areas such as Customer Income, Customer Counts, Revenue per
Customer, and Revenue Growth.
Daily Analysis:
Dashboards and reports containing information about sales, cost, revenue, revenue forecast, profit, and profit margins that are used to analyze the typical workflow of a company. The dashboards and reports are useful for a standard MicroStrategy demonstration.
Enterprise Performance Management
: Reports containing information on revenue amounts, trends and forecasts, profits, profit margins, and profit forecasts. These reports make it easy for an executive at any level of the company to understand how the company
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Human Resource Analysis
: Reports containing information on employees, including headcount, birthdays, length of employment, and the top five employees by revenue. These reports are based on employees, time, geography, and sales. The Human Resources
Analysis reports provide insight into human capital so that managers can boost the efficiency and effectiveness of their employees. Human
Resource Representatives can highlight under-performing employees and misallocated headcount. Managers at all levels can focus on the performance of their employees, drill down to an individual employee detail level, view trends, and extract intelligence not otherwise evident.
Inventory and Supply Chain Analysis
: Reports containing information based on supplier, product, cost, revenue and profit, such as Inventory and Unit Sales, or Inventory Received from Suppliers by
Quarter. The Inventory reports track inventory information within the company and through to suppliers. Essentially, these reports show how many units of an item are on hand, how many are expected from a particular supplier, and how many units have been sold. Inventory reports are used to ensure that the supply chain is as efficient as possible. Using these reports, employees can analyze trends and details, quickly adjust inventory and distribution, and understand underlying supply chain costs and inefficiencies.
Sales and Profitability Analysis
: Reports analyzing revenue and profit from multiple perspectives. Examples include Sales by Region,
Revenue over Time, and Brand Performance by Region. The Product
Sales reports allow managers and analysts to monitor and analyze sales trends, track corporate revenue goals, compare store-to-store performance, and respond more quickly and accurately to feedback from the marketplace. In turn, executives can analyze sales trends and details, quickly adjust pricing and promotions, identify product affinities and key profit centers, and understand costs and revenue trends.
Supplier Analysis
: Reports containing supplier, sales, profit, and revenue information, such as Brand Sales by Supplier, Supplier
Sell-Through Percentage, and Units Sold and Profit by Supplier. The
Supplier reports allow managers and analysts to monitor and analyze vendor performance so that they can quickly identify performance problems. These reports track brands and items sold that came from a particular vendor. They also correlate profit and revenue information with particular suppliers so that relationships with key vendors can be strengthened.
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You can deploy the out-of-the-box documents to your project by reconciling the documents' content to your own project objects. For example, you can use any of the Tutorial documents or dashboards mentioned above, or any of the documents and dashboards in the Human Resource Analytic Module, in your own project. To do this, you use the portable documents feature. A portable document contains all the design of the document without the data, allowing you to copy documents between projects, even when the projects do not have the same metadata. When you import the document into the replacement project, you map the document to the new project (referred to as reconciling the document). For instructions on creating and reconciling portable documents, see the Advanced Documents chapter of the Document
Creation Guide.
MicroStrategy Tutorial data model
A logical data model graphically depicts the flow and structure of data in a business environment. It provides a way of organizing facts so that they can be analyzed from different business perspectives. For example, a simple logical data model for a retail company can organize all necessary facts by store, product, and time, which are the three common business perspectives typically associated with retail business.
For detailed information about data modeling, see
.
For MicroStrategy Tutorial, the areas of analysis discussed earlier, Customer
Analysis, Human Resources Analysis, and so on, are organized into the following hierarchical groupings:
•
•
•
•
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Data modeling notations
The following notations are used in graphical depictions of hierarchies.
Symbol Indicates
entry point attribute one-to-many relationship
Definition
An entry point is a shortcut to an attribute element in the Data Explorer.
Creating an entry point grants you faster access to the attribute without having to browse through multiple attributes to reach different levels of the hierarchy.
A data level defined by the system architect and associated with one or more columns in the data warehouse lookup table. Attributes include data classifications like Region, Order, Customer, Age, Item, City, and
Year. They provide a handle for aggregating and filtering at a given level.
An attribute relationship in which every element of a parent attribute relates to multiple elements of a child attribute, while every element of the child attribute relates to only one element of the parent. The one-to-many attribute relationship is the most common in data models.
Geography hierarchy
The Geography hierarchy contains attributes such as Country and Region, as well as Distribution Center, Call Center, and employee-specific attributes. It is easy to understand why Country and Region are in the Geography hierarchy, but what about Distribution Center, Call Center, and the employee-related attributes?
The data used in MicroStrategy Tutorial is based upon a fictitious company that sells electronics, movies, music, and books. The company does not have physical stores, but instead does its business from catalog and Web sales.
Customers review the products in a printed or online catalog and call in their order over the phone. The order is then processed by an employee located at one of the call centers. The order is then fulfilled by a distribution center that holds the correct item and sends it through one of the shippers.
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Attribute
Call Center
Country
Distribution
Center
Employee
Employee
Age
Employee
Birth Date
Employee
Experience
Employee
FTE Flag
Hire Date
Manager
The Geography hierarchy contains the following attributes.
Description Example
Where product phone-in orders are taken. Each call center is located in a different city.
Countries where the company does or hopes to do business in the future. Also refers to countries where employees work.
The location where product orders are sent out to customers.
Currently, each is located in the same city as the call center it services.
Atlanta, Boston,
Charleston.
USA, Spain, France.
Miami, New Orleans,
Fargo.
The lowest level in the Geography hierarchy, representing the individual responsible for each order placed.
The age of each employee.
Jennifer Lee, Laura
Kelly.
29, 36, 52.
The date each employee was born.
The number of years an employee has worked for the organization.
Indicates whether the employee’s status is full-time.
The date on which a particular employee was hired.
Person responsible for a specific call center.
5/6/66, 1/1/77.
3, 5, 6.
Yes, No.
MicroStrategy
User
Login ID of the MicroStrategy user. This attribute is used for implementing data security using system prompts.
Region Each country is split into regions.
Salary The amount of money an employee makes per year.
2/16/97, 3/15/99.
Peter Rose, Alice
Cooper.
Administrator,
Business User, User.
Central, Northeast,
Southwest.
24,000, 35,000.
The attributes listed in the table above are some of the most commonly used attributes that are included in the logical definition of the Geography
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Products hierarchy
The products hierarchy contains attributes such as Category, Brand, Catalog, and Supplier.
The Products hierarchy contains the following attributes.
Attribute
Brand
Catalog
Category
Discontinued
Code
Description
The manufacturer or artist for a particular product.
The medium used to sell products.
Products are organized into categories at the highest level.
0 = discontinued product, 1 = non-discontinued product.
Example
Ayn Rand, 3Com, Sony.
Spring 2006, Fall 2007.
Electronics, Music.
0, 1.
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Attribute
Item
Description
The individual product sold.
Subcategory Used to further differentiate a subset of products within a category.
Supplier The distributor for a set of brands.
Warranty The time period in months during which a manufacturer repairs a broken item (specific to Narrowcast Server).
Example
The Great Gatsby, Sony
Discman.
Business, Cameras,
Drama.
McGraw Hill, Disney
Studios.
3, 5.
The attributes listed in the table above are some of the most commonly used attributes that are included in the logical definition of the Products hierarchy. Refer to the following image for a complete understanding of the logical relationships of all attributes for the Products hierarchy.
Customers hierarchy
The Customers hierarchy contains customer demographic and purchase information, such as Customer Age, Income Bracket, Payment Method, and
Ship Date.
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The Customers hierarchy contains the following attributes.
Attribute Description Example
Age Range
Customer
Customer
Address
The segregation of customers between a certain age group.
24 and under, 28 and above.
The name of the individual customer.
The postal address of the customer.
Selene Allen, Chad Laurie.
1717 Mcknight Ln, 1734
Benson St.
26, 38, 59.
Customer Age The age of a particular customer at a current point in time.
Customer Birth
Date
The date on which the Customer was born.
8/4/50, 4/30/72.
Customer City The city where the customer resides.
Customer
Country
The country where the customer resides.
Customer Email The email address of the customer.
Albany, Chicago, Memphis.
USA, Spain, France.
[email protected], [email protected].
Male, Female.
Customer
Gender
Customer
Region
The gender of the customer.
The region where the customer resides.
Customer State The state where the customer resides.
Days to Ship The number of days remaining for the order to be shipped from the Distribution Center.
Education Level The highest level of education of the customer.
Northeast, South, France.
Maine, North Dakota.
1, 5, 6.
First Order Date The date of the first order placed by the customer.
High School, Undergraduate,
Graduate.
2/14/2007.
Household
Count
Housing Type
The number of houses owned by the customer.
The type of housing owned by the customer.
Income Bracket The salary range reported by the customer.
Last Order Date The date of the last order placed by the customer.
Marital Status
Order
Payment
Method
The marital status of the customer.
The tracking number associated with a particular group of items purchased.
The way a customer pays for an order.
2, 4, over 6.
Renter, Owner, Dependent.
$31,000 - 40,000, $61,000 -
70,000.
2/28/2009.
Married, Single.
167, 2635.
Amex, Check.
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Attribute Description Example
Phone Plan
Phone Usage
The type of phone plan purchased by the customer.
Student, Single, Double,
Family Small.
The current phone usage of the customer that includes the time, date, and billing details of the call.
Active Months: 24, Average
Minutes Off-peak: 412.60294,
Average Minutes On-peak:
91.54584, Expire Date: 12/14/
2009 12:00:00 am.
Promotion Date range for a particular discount period under which an item is purchased (Sales Date).
Promotion Type Type of discount period offered (Sale type).
9/1/06 - 9/4/06, 2/16/07 - 2/19/
07.
Holiday Sale, Back-to-School
Sale.
Rush Order
Ship Date
Shipper
Status
Store
Store City
Indicates whether a customer chose to expedite delivery of an order.
The date on which an order is shipped from the distribution center.
The vendor used to send products to the customer.
The current account status of the customer.
A retail store with location information that supports identification through the use of mobile devices.
The city that a store resides in.
1 (rush order), 0 (not a rush order).
9/15/06, 3/26/07.
Pronto Packages, MailFast.
New, Active.
New Design Export, General
Associates Enterprise, Good’s
Good Goods
Council Bluffs, Elk River,
Liverpool
Store Country
Store State
Store Zipcode
The country that a store resides in.
The state that a store resides in.
The postal zip code for a store.
Canada, Germany, USA
AB, PA, TX
00682, 22043
Zip Code The zip code of the area where the customer resides. 07026, 36303.
The attributes listed in the table above are some of the most commonly used attributes that are included in the logical definition of the Customers
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Time hierarchy
The Time hierarchy contains time-specific attributes such as Year, Quarter,
Month, and Day.
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Attribute
Day
Month
Month of Year
Quarter
Year
The Time hierarchy contains the following attributes.
Description
Calendar date of purchase.
Month of purchase.
Calendar month of purchase.
Calendar quarter of purchase.
Calendar year of purchase.
Example
5/14/09, 12/26/12.
Jul 10, Aug 12.
January, November.
Q2 10, Q3 12.
2009, 2010, 2011.
Refer to the following image for a complete understanding of the logical relationships of all attributes for the Time hierarchy.
Viewing the MicroStrategy Tutorial data model
Although the MicroStrategy Tutorial data model is displayed in the previous pages, you can also view it directly using MicroStrategy Architect.
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To view the MicroStrategy Tutorial data model using Architect
1
If you are not already using the MicroStrategy Tutorial, log in to the project source containing the MicroStrategy Tutorial and expand the
MicroStrategy Tutorial
project. You must log in as a user with administrative privileges.
By default, the MicroStrategy Tutorial project is provided as part of the MicroStrategy Analytics Modules project source.
2
Right-click the MicroStrategy Tutorial project and select Architect.
MicroStrategy Architect opens.
3
From the Hierarchy View, in the Hierarchies drop-down list, select
System Hierarchy
. The system hierarchy is displayed.
A project’s system hierarchy defines the relationships between all the attributes in a project. Attribute relationships determine how the engine generates SQL, how tables and columns are joined and used, and which tables are related to other tables. For information on defining attribute
relationships using Architect, see
Defining attribute relationships, page 174
.
4
From the hierarchies drop-down list you can select a different hierarchy such as Customers, Geography, Products, and Time. These are user hierarchies that define browsing and drilling functionality between attributes. For information on creating user hierarchies using Architect, see
Creating and modifying user hierarchies, page 181
5
To save the layout display of a hierarchy, from the File menu, select
Export Image
. Type a name and select an image type to save the image as, and click Save.
MicroStrategy Tutorial schema
A schema is a logical and physical definition of warehouse data elements, physical characteristics, and relationships, derived from the logical data model.
The logical data model provides a picture of all the pieces of information necessary to understand your data and how it relates to your business. It is a graphic-intensive technique that results in a data model representing the
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The physical warehouse schema is based on the logical data model, such as
Day, Item, Store, or Account. Several physical warehouse schemas can be derived from the same logical data model. While the logical data model tells you what facts and attributes to create, the physical warehouse schema tells you where the underlying data for those objects is stored. The physical warehouse schema describes how your data is stored in the data warehouse.
Exploring the MicroStrategy Tutorial schema
Symbol
LU_
REL_
_SLS
MicroStrategy Architect provides an intuitive way to explore the
MicroStrategy Tutorial schema. Before you begin exploring the Tutorial schema using Architect, there are a few conventions and fact information that can help you understand the overall Tutorial schema.
The following prefixes and suffixes are used to identify different types of tables.
Indicates Definition
a lookup table A database table used to uniquely identify attribute elements. They typically consist of descriptions of dimensions. Lookup tables are usually joined to fact tables to group the numeric facts in the fact table by dimensional attributes in the lookup tables.
a relationship table
While lookup tables store information about one or more attributes, relationship tables store information about the relationship between two attributes. Relationship tables contain the ID columns of two or more attributes, thus defining associations between them.
a table with sales information
A database table used to store sales data (revenue, profit, cost, and so on) at different logical levels. These tables include a combination of attribute and fact definitions. For example, the YR_CATEGORY_SLS table includes the attributes Year and Category, along with facts such as Revenue, Cost, Profit, and so on. Storing these facts in this table makes their data available at the Year and Category level.
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Many tables include a combination of attributes and facts. Some of the basic facts from which metrics in the MicroStrategy Tutorial were created from are listed below.
Fact Description
Begin on hand The number of individual items available at the beginning of each month.
Churn The trends and profiles of the acquired, retained, and lost customers.
Cost The total amount charged by the supplier to the company.
Customer Id The unique identification number allocated to the customer by the company.
Discount A monetary reduction made from a regular price.
End on hand The number of individual items remaining at the close of each month.
Freight
Gross
Revenue
The compensation paid for the transportation of goods.
The total income received from the company’s product sales before deducting the expenses.
Item inventory The month level item inventories used for End on hand and Begin on hand type inventory calculations. This fact also supports year-to-date and month-to-date analysis.
On Order
Quantity
The quantity ordered by the customer.
Profit
Recency
Revenue
The excess of the selling price of goods over their cost.
The number of days elapsed since the last purchase was made by the customer.
The total income produced by a given source accounting for all product sales deducting discounts.
Rush Charge The amount of money charged to expedite delivery service.
Target
Quantity
The target set for the product units sold.
Tenure
Unit Cost
Unit Price
Unit Profit
Units
Received
Units Sold
The duration an employee is employed by the company.
The amount of money charged by the supplier to the company per individual item purchased.
The amount of money charged by the company to the customer per individual item sold.
Unit price - Unit cost.
The number of individual items acquired from a supplier.
The number of individual items bought by customers.
The steps below show you how to explore the MicroStrategy Tutorial schema using Architect.
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To explore the MicroStrategy Tutorial schema using Architect
1
If you are not already using the MicroStrategy Tutorial, log in to the project source containing the MicroStrategy Tutorial and expand the
MicroStrategy Tutorial
project. You must log in as a user with administrative privileges.
By default, the MicroStrategy Tutorial project is provided as part of the MicroStrategy Analytics Modules project source.
2
Right-click the MicroStrategy Tutorial project and select Architect.
MicroStrategy Architect opens.
3
Select the Project Tables View. All the tables included in the
MicroStrategy Tutorial project are displayed.
4
To view the physical columns for each table: a From the Options menu, select Settings. The MicroStrategy
Architect Settings dialog box opens.
b On the Display settings tab, select Display table physical view. c Click OK to return to Architect.
Tables are displayed to show the columns within each table, including the column name and data type.
5
To view the physical columns for each table, along with the MicroStrategy schema object they define: a From the Options menu, select Settings. The MicroStrategy
Architect Settings dialog box opens.
b On the Display settings tab, select Display table logical view. c Click Advanced Options. d Select all the check boxes for the Display table logical view option.
For a description of each option, see
Displaying columns and attribute forms in tables, page 117
e Click OK. f Click OK to return to Architect.
Tables are displayed to show the schema objects and the columns used to define the schema objects.
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To view attribute relationships in the Project Tables View, from the View menu, select Show relationships.
7
From the Properties pane, you can use the Attribute, Facts, and Tables tabs to browse the various tables and schema objects for the Tutorial project.
8
To organize tables for further insight into the Tutorial project, you can create layers. For information on creating layers using Architect, see
Organizing project tables: Layers, page 137
.
9
To save the layout display of the Tutorial project schema, from the File menu, select Export Image. Type a name and select an image type to save the image as, and click Save.
Table column information
This section describes each physical table column used in the Tutorial project.
Column Name Data Type Description
Table: CITY_CTR_SLS
Fact table that stores sales data at the City and Call Center logical level.
CALL_CTR_ID Integer(2)
CUST_CITY_ID Integer(2)
Unique identifier of call center values.
Unique identifier of customer city values.
TOT_COST Double The total amount charged by the supplier to the company.
GROSS_DOLLAR_SALES
TOT_DOLLAR_SALES
TOT_UNIT_SALES
Double
Double
Double
The total income received from the company’s product sales before deducting the expenses.
The total income produced by a given source accounting for all product sales deducting discounts.
The number of individual items bought by a customer.
Table: CITY_MNTH_SLS
Fact table that stores sales data at the City and Month logical level.
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Column Name Data Type Description
CUST_CITY_ID
MONTH_ID
TOT_COST
GROSS_DOLLAR_SALES
Integer(2)
Integer(4)
Double
Double
TOT_DOLLAR_SALES
TOT_UNIT_SALES
Double
Double
Table: CITY_SUBCATEG_SLS
Fact table that stores sales data at the City and Subcategory logical level.
CUST_CITY_ID Integer(2)
SUBCAT_ID Integer(2)
Unique identifier of customer city values.
Unique identifier for the subcategory of an item.
TOT_DOLLAR_SALES Double
The total income produced by a given source accounting for all product sales deducting discounts.
The number of individual items bought by customers.
TOT_UNIT_SALES
TOT_COST
GROSS_DOLLAR_SALES
Double
Double
Double
The total income produced by a given source accounting for all product sales deducting discounts.
The number of individual items bought by customers.
The total amount charged by supplier to the company.
The total income received from the company’s product sales before deducting the expenses.
Table: CUSTOMER_SLS
Fact table that shows sales data at the Customer logical level.
CUSTOMER_ID Integer(4)
Unique identifier of customer city values.
Unique identifier of a month.
The total amount charged by the supplier to the company.
The total income received from the company’s product sales before deducting the expenses.
TOT_DOLLAR_SALES Double
The unique identification number allocated to the customer by the company.
The total income produced by a given source accounting for all product sales deducting discounts.
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Column Name Data Type Description
TOT_UNIT_SALES
TOT_COST
Double
Double
GROSS_DOLLAR_SALES Double
Table: DAY_CTR_SLS
Fact table that shows sales data at the Day and Call Center logical level.
DAY_DATE TimeStamp
The total income received from the company’s product sales before deducting the expenses.
CALL_CTR_ID Integer(2)
The day and date on which the transaction took place.
Unique identifier of call center values.
TOT_DOLLAR_SALES Double
The number of individual items bought by a customer.
The number of individual items bought by a customer.
TOT_UNIT_SALES
TOT_COST
GROSS_DOLLAR_SALES
Double
Double
Double
The total income produced by a given source accounting for all product sales after deducting discounts.
The number of individual items bought by customers.
The total amount charged by the supplier to the company.
The total income received from the company’s product sales before deducting the expenses.
Table: INVENTORY_CURR
Fact table that shows data for the current Inventory logical level.
ITEM_ID Integer(2)
TARGET_QTY Double
Unique identifier of an item.
The target set for the product units sold.
EOH_QTY
ON_ORDER_QTY
UNIT_COST
Double
Double
Double
The number of individual items remaining at the close of each month.
The quantity ordered by the customer.
The amount of money charged by the supplier to the company per individual item purchased.
Table: INVENTORY_ORDERS
Fact table that shows data for the Inventory and Order logical level.
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Column Name Data Type Description
ITEM_ID
UNITS_RECEIVED
Integer(2)
Double
Unique identifier of an item.
The number of individual items acquired from a supplier.
Unique identifier of a month.
MONTH_ID
MONTH_DURATION
Integer(4)
Double
Table: ITEM_CCTR_MNTH_SLS
Fact table that shows sales data at the Item, Call Center, and Month logical level.
CALL_CTR_ID Integer(2)
Duration in months for which the inventory is calculated.
Unique identifier of call center values.
MONTH_ID
ITEM_ID
TOT_DOLLAR_SALES
Integer(4)
Integer(2)
Double
Unique identifier of a month.
Unique identifier of an item.
The total income produced by a given source accounting for all product sales after deducting discounts.
TOT_UNIT_SALES
TOT_COST
Double
Double
The number of individual items bought by customers.
The total amount charged by the supplier to the company.
GROSS_DOLLAR_SALES Double
Table: ITEM_EMP_SLS
Fact table that shows sales data at the Item and Employee logical level.
The total income received from the company’s product sales before deducting the expenses.
ITEM_ID
EMP_ID
Integer(2)
Integer(2)
TOT_DOLLAR_SALES
TOT_UNIT_SALES
TOT_COST
Double
Double
Double
Unique identifier of an item.
Unique identifier of an employee.
The total income produced by a given source accounting for all product sales after deducting discounts.
The number of individual items bought by customers.
The total amount charged by the supplier to the company.
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Column Name Data Type Description
GROSS_DOLLAR_SALES Double The total income received from the company’s product sales before deducting the expenses.
Table: ITEM_MNTH_SLS
Fact table that shows sales data at the Item and Month logical level.
ITEM_ID
MONTH_ID
TOT_DOLLAR_SALES
TOT_UNIT_SALES
TOT_COST
GROSS_DOLLAR_SALES
Integer(2)
Integer(4)
Double
Double
Double
Double
Unique identifier of an item.
Unique identifier of a month.
The total income produced by a given source accounting for all product sales after deducting discounts.
The number of individual items bought by customers.
The total amount charged by the supplier to the company.
The total income received from the company’s product sales before deducting the expenses.
Table: LU_AGERANGE
Lookup table for Customer Demographic: Age Range.
AGERANGE_ID Integer(4) Unique identifier for the age range.
Description of the age range.
AGERANGE _DESC NVarChar(20)
Table: LU_BRAND
Lookup table for Brand data, which lists the manufacturer or artist for a particular product.
BRAND_ID
BRAND_DESC
Integer(2)
NVarChar(50)
Unique identifier for the brand.
Description of the brand.
Table: LU_CALL_CTR
Lookup table for the Call Center data, which lists the Call Center details where product phone-in orders are taken.
CALL_CTR_ID Integer(2) Unique identifier of call center values.
CENTER_NAME
COUNTRY_ID
REGION_ID
NVarChar(50)
Integer(2)
Integer(2)
Name of the call center.
Unique identifier of country values.
Unique identifier of region values.
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Column Name Data Type Description
DIST_CTR_ID
CAT_DESC
Integer(2)
NVarChar(50)
Unique identifier of distribution center values.
Unique identifier for managers.
MANAGER_ID Integer(2)
Table: LU_CATALOG
Lookup table for the Catalog data, which lists the products available in the catalog.
CAT_ID Integer(2) Unique identifier of catalog values.
Description of the catalog values.
Table: LU_CATEGORY
Lookup table for Category data, which lists the products categorized at the highest levels.
CATEGORY_ID Ineteger(2)
CATEGORY_DESC NVarChar(50)
Unique identifier of the category values.
Description for the category values.
Table: LU_COUNTRY
Lookup table for Country data, which represents countries where the company does or hopes to do business in the future. Also includes countries where employees work.
COUNTRY_ID Integer(2) Unique identifier of country values.
Name of the country.
COUNTRY_NAME NVarChar(50)
Table: LU_CUST_CITY
Lookup table for customer geographic information: City.
CUST_CITY_ID Integer(2) Unique identifier of customer city values.
CUST_CITY_NAME
CUST_STATE_ID
NVarChar(50)
Integer(2)
Name of the city in which the customer stays.
Unique identifier of customer state values.
Table: LU_CUST_REGION
Lookup table for customer geographic information: Region.
CUST_REGION_ID Integer(2)
CUST_REGION_NAME
CUST_COUNTRY_ID
NVarChar(50)
Integer(2)
Unique identifier of customer region values.
Name of the region in which the customer stays.
Unique identifier of customer country values.
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Column Name Data Type Description
Table: LU_CUST_STATE
Lookup table for customer geographic information: State.
CUST_STATE_ID Integer(2)
CUST_STATE_NAME NVarChar(50)
Unique identifier of customer state.
Name of the state in which the customer stays.
CUST_REGION_ID Integer(2)
Table: LU_CUSTOMER
Lookup table for Customer data. This table targets a customer as a consumer rather than a business.
CUSTOMER_ID Integer(4)
Unique identifier of customer region.
Unique identification number allocated to the customer by the company.
CUST_FIRST_NAME
CUST_LAST_NAME
CUST_BIRTHDATE
ADDRESS
INCOME_ID
NVarChar(255)
NVarChar(255)
TimeStamp
NVarChar(255)
Integer(2)
First name of the customer.
Last name of the customer.
Birth date of the customer.
Address of the customer.
CUST_CITY_ID
ZIPCODE
GENDER_ID
AGE_YERS
NVarChar(50)
Integer(2)
NVarChar(255)
Integer(4)
Real
Unique identifier of the customer’s income.
Email ID of the customer.
Unique identifier of customer city values.
The zip code of the address where the customer resides.
Unique identifier of the customer’s gender.
Age in years of a customer.
AGERANGE_ID Integer(4)
MARITALSTATUS_ID
EDUCATION_ID
HOUSINGTYPE_ID
Integer(4)
Integer(4)
Integer(4)
Unique identifier of segregation of customers between a certain age group.
Unique identifier of marital status of the customer.
Unique identifier of level of education of the customer.
Unique identifier of type of housing owned by the customer.
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Column Name Data Type Description
HOUSEHOLDCOUNT_ID
FIRST_ORDER
LAST_ORDER
STATUS_ID
Integer(4)
TimeStamp
TimeStamp
Integer(4)
Unique identifier of number of houses owned by the customer.
The date of first order placed by the customer.
The date of last order placed by the customer.
Unique identifier of the current account status of the customer.
TENURE
RECENCY
Real
Real
Table: LU_CUSTOMER_TELCO
Lookup table for Customer Telephone data, which lists the telephone plan details of the customer.
CUSTOMER_ID Integer(4)
PLAN_ID Integer(2)
Unique identification number allocated to the customer by the company.
Unique identifier of the telephone plan purchased by the customer.
AVG_MIN_PEAK Double
The duration an employee is employed by the company.
The number of days elapsed since the last purchase was made by the customer.
AVG_MIN_OFFPEAK
MONTHLY_PROFIT
HELPDESK_CALLS
DROPPED_CALLS
Double
Double
Integer(2)
Integer(2)
Average calls made by the customer in minutes during peak time.
Average calls made by the customer in minutes during off-peak time.
The monthly excess of the selling price of goods over their cost.
Calls made by the customer to the Helpdesk.
Number of calls made by the customer that were dropped or disconnected unexpectedly.
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Column Name
ACTIVE_MONTHS
Data Type
Double
CURRENT_MONTHS
LIFETIME_VALUE
Double
Double
NET_PRESENT_VALUE
REMAINING_VALUE
START_DATE
EXPIRE_DATE
RENEWALS
CANCEL_DATE
CHURN
Double
Double
TimeStamp
TimeStamp
Integer(2)
TimeStamp
Integer(2)
LIFETIMEVALUESCORE
CREDITRISKSCORE
CREDITRISKVALUE
NVarChar(50)
NVarChar(50)
NVarChar(50)
Table: LU_CUSTSTATUS
Lookup table for Customer Statuses.
STATUS_ID Integer(4)
STATUS_DESC NVarChar(20)
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Description
The total number of months the customer has had a telephone plan. This includes the number of months into the current two-year contract, and all months for any previous contracts.
The number of months into the current two-year contract.
A customer score that identifies the customers most likely to be profitable to a company over a period of time.
Net present value of the telephone plan.
The remaining value of the telephone plan.
Start date of the customer telephone plan.
Expiry date of the customer telephone plan.
Renewals made by the customer.
Date on which the customer cancelled the telephone plan.
The trends and profiles of the acquired, retained, and lost customers.
Lifetime value score of the customer.
Credit risk score of the customer.
Credit risk value of the customer.
Unique identifier of customer status.
Description of customer status.
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Column Name Data Type Description
Table: LU_DAY
Lookup table for Day data, which lists the calendar dates for customer transactions.
DAY_DATE
MONTH_ID
TimeStamp
Integer(4)
Day and date.
Month of the date.
QUARTER_ID
YEAR_ID
Integer(2)
Integer(2)
Quarter of the date.
Year of the date.
Table: LU_DIST_CTR
Lookup table for Distribution Center data, which lists the location where product orders are sent out to customers.
DIST_CTR_ID Integer(2) Unique identifier of distribution center.
DIST_CTR_NAME
COUNTRY_ID
NVarChar(50)
Integer(2)
Distribution center name.
Unique identifier of a country.
Table: LU_EDUCATION
Lookup table for Customer Psychographics: Level of Education.
EDUCATION_ID Integer(4)
EDUCATION_DESC NVarChar(20)
Unique identifier of education level of the customer.
Description of education level of the customer.
Table: LU_EMPLOYEE
Lookup table for Employee data, which lists the details of individuals working for the company who receive salary and benefits in return.
EMP_ID Integer(2)
EMP_LAST_NAME
EMP_FIRST_NAME
EMP_SSN
CALL_CTR_ID
DIST_CTR_ID
COUNTRY_ID
MANAGER_ID
NVarChar(50)
NVarChar(50)
NVarChar(50)
Integer(2)
Integer(2)
Integer(2)
Integer(2)
Unique identifier of an employee.
Last name of the employee.
First name of the employee.
Social security number of the employee.
Unique identifier of call center values.
Unique identifier of distribution center values.
Unique identifier of country values.
Unique identifier of a manager.
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Column Name Data Type Description
SALARY
BIRTH_DATE
HIRE_DATE
FTE_FLAG
Integer(4)
TimeStamp
TimeStamp
NVarChar(50)
The amount of money an employee makes per year.
The birth date of each employee.
The date on which a particular employee was hired.
Indicates whether the employee’s status is full-time.
Table: LU_GENDER
Lookup table for Customer Gender data, which lists the gender of the customer.
GENDER_ID Integer(4)
GENDER_DESC NVarChar(20)
Unique identifier of the customer’s gender.
Description of the customer’s gender.
Table: LU_HOUSEHOLDCOUNT
Lookup table for Household Count data, which lists the details of the number of houses owned by the customer.
HOUSEHOLDCOUNT_ID Integer(4)
HOUSEHOLDCOUNT_DESC NVarChar(20)
Unique identifier of the household count of the customer.
Description of the number of houses owned by the customer.
Table: LU_HOUSINGTYPE
Lookup table for Housing Type data, which lists the details of type of house owned by the customer.
HOUSINGTYPE_ID Integer(4)
HOUSINGTYPE_DESC NVarChar(20)
Unique identifier of the housing type of the customer.
Description of the type of house owned by the customer.
Table: LU_INCOME
Lookup table for the Customer Income data, which lists the salary range reported by the customer.
INCOME_ID Integer(2) Unique identifier of the customer’s income.
BRACKET_DESC NVarChar(50) Description for customer’s income.
Table: LU_ITEM
Lookup table for the Item data, which lists the details of the individual products sold.
ITEM_ID
ITEM_NAME
Integer(2)
NVarChar(255)
Unique identifier of an item.
Name of the product sold.
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Column Name Data Type Description
SUPPLIER_ID
ITEM_FOREIGN_NAME
SUBCAT_ID
DISC_CD
ITEM_LONG_DESC
WARRANTY
UNIT_PRICE
BRAND_ID
UNIT_COST
Integer(2)
NVarChar(50)
Integer(2)
Double
NVarChar(255)
NVarChar(50)
Double
Integer(2)
Double
Unique identifier of the supplier that supplies the product.
Name given to the item by other suppliers.
Unique identifier of the subcategory to which the item belongs.
Unique code that identifies items as either available or discontinued.
Detailed description of the item.
The time period in months during which a manufacturer repairs a broken item.
The amount of money charged by the company to the customer per individual item sold.
Unique identifier of manufacturer or artist for a particular product.
The amount of money charged by the supplier to the company per individual item purchased.
Table: LU_MANAGER
Lookup table for Manager data, which lists the details of the person responsible for a specific call center.
MANAGER_ID Integer(2)
MGR_LAST_NAME NVarChar(50)
Unique identifier of the person responsible for a specific call center.
Last name of the person responsible for a specific call center.
MGR_FIRST_NAME NVarChar(50)
ADDRESS_DISPLAY
DEVICE_ID
NVarChar(50)
NVarChar(50)
NVarChar(50)
First name of the person responsible for a specific call center.
Email ID of the person responsible for a specific call center.
Manager’s name displayed in
Last Name, First Name format.
Unique identifier of the device.
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Column Name Data Type Description
MSTR_USER NVarChar(50)
Table: LU_MARITALSTATUS
Lookup table for customer Marital Status, which lists the marital details of the customer.
MARITALSTATUS_ID Integer(4)
Login ID of the MicroStrategy user. This attribute is used for implementing data security using system prompts.
MARITALSTATUS_DESC NVarChar(20)
Unique identifier for the marital status of the customer.
Description of the marital status of the customer.
Table: LU_MONTH
Lookup table for Month data, which lists the month of the customer transaction.
MONTH_ID Integer(4) Unique identifier of month of the date.
MONTH_DESC
MONTH_OF_YEAR
QUARTER_ID
YEAR_ID
NVarChar(50)
Integer(2)
Integer(2)
Integer(2)
Description of the month.
Calendar month of purchase.
Unique identifier of the quarters.
Unique identifier of year of the date.
MONTH_DATE
MONTH_DURATION
TimeStamp
Double
Calendar day of purchase.
Duration in months.
Table: LU_MONTH_OF_YEAR
Lookup table for Month of Year data, which lists the calendar month of purchase.
MONTH_OF_YEAR
MONTH_OF_YEAR_NAME
Integer(2)
NVarChar(50)
Calendar month of purchase.
Name of the calendar month of purchase.
Table: LU_PROMO_TYPE
Lookup table for Promotion Type data, which lists the type of discount offered on the sale product.
PROMO_TYPE_ID
PROMO_TYPE_DESC
Integer(2)
NVarChar(50)
Unique identifier of the type of discount period offered on the product.
Description of the type of discount period offered on the product.
Table: LU_PROMOTION
Lookup table for Promotion data, which lists the discount being offered on the sale item.
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Column Name Data Type Description
PROMOTION_ID
PROMOTION_DESC
PROMOTION_DISCOUNT
PROMO_TYPE_ID
Integer(4)
NVarChar(50)
Integer(4)
Integer(2)
Unique identifier of the date range for a particular discount period under which an item is purchased.
Description of the discount period under which an item is purchased.
Monetary reduction made from a regular price for a particular discount period under which an item is purchased.
Unique identifier of the type of discount period offered on the product.
Table: LU_PYMT_TYPE
Lookup table for Payment Type data, which lists the payment mode used by a customer to pay for an order.
PYMT_TYPE Integer(2)
PYMT_DESC NVarChar(50)
The mode used by a customer to pay for an order.
Description of the way in which a customer pays for an order.
Table: LU_QUARTER
Lookup table for Quarter data, which list the details of the calendar quarter of purchase.
QUARTER_ID Integer(2)
QUARTER_DESC NVarChar(50)
Unique identifier of the calendar quarter of purchase.
Description of the calendar quarter of purchase.
YEAR_ID
QUARTER_DATE
Integer(2)
TimeStamp
Unique identifier of the calendar year of purchase.
Calendar date from which the quarter starts.
Duration of quarter.
QUARTER_DURATION Integer(2)
Table: LU_REGION
Lookup table for Region data, which lists the regions into which a country is split.
REGION_ID Integer(2)
REGION_NAME NVarChar(50)
Unique identifier of region values.
Name given to a region, which is part of a country.
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Column Name Data Type Description
COUNTRY_ID Integer(2)
Table: LU_SHIPPER
Lookup table for Shipper data, which lists the details of the vendor used to send products to the customer.
SHIPPER_ID Integer(2)
Unique identifier of country values.
SHIPPER_DESC NVarChar(50)
Unique identifier of the vendor used to send products to the customer.
Description of the vendor used to send products to the customer.
Table: LU_STORE
Lookup table for Store data, which lists the details of a retail store including the location information.
STORE_ID Integer(4)
STORE_NAME
CUSTOMER_ID
NVarChar(100)
Integer(4)
Unique identifier for store values.
Name given to a store.
STORE_ADDRESS NVarChar(255)
The unique identification number allocated to the customer by the company.
Address where the store is located.
STORE_ZIPCODE
STORE_STATE
STORE_CITY
STORE_PHONE
NVarChar(10)
NVarChar(50)
NVarChar(100)
NVarChar(50)
The postal zip code for a store.
The state that a store resides in.
The city that a store resides in.
Telephone number of the store.
STORE_EMAIL
STORE_USER_RATING
NVarChar(100)
Integer(2)
STORE_WIFI
STORE_DRIVE_THRU
STORE_SERVICE_CENTER
STORE_SUPER
Integer(4)
Integer(4)
Integer(4)
Integer(4)
Email address of the store.
Rating given by customers to the store.
Indicates whether Wi-Fi is available at the store.
Indicates whether a drive-through facility is available at the store.
Indicates whether a service center facility is available at the store.
Indicates whether the store is a super store.
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Column Name Data Type Description
STORE_LATITUDE
STORE_LONGITUDE
STORE_WEBSITE
STORE_SPECIALITY
STORE_STATE_DESC
Double
Double
NVarChar(100)
Integer(4)
NVarChar(255)
Latitude coordinates of the store location.
Longitude coordinates of the store location.
URL of the store website.
Indicates whether the store is a speciality store.
Full name of the state in which the store is located.
STORE_COUNTRY NVarChar(50)
Table: LU_SUBCATEG
Lookup table for Subcategory data, which further differentiates a subset of products within a category.
SUBCAT_ID Integer(2)
The country that a store resides in.
SUBCAT_DESC NVarChar(50)
Unique identifier of the subcategory of a product.
Description of the subcategory of the product.
CATEGORY_ID Integer(2)
Table: LU_SUPPLIER
Lookup table for Supplier data, which lists the distributor for a set of brands.
SUPPLIER_ID Integer(2)
Unique identifier of the category of a product.
SUPPLIER_NAME NVarChar(50)
Unique identifier of supplier values.
Name of the distributor for a set of brands.
CONTACT_LAST_NAME
CONTACT_FIRST_NAME
NVarChar(50)
NVarChar(50)
Last name of the contact person at the supplier’s location.
First name of the contact person at the supplier’s location.
Table: LU_TELCO_PLANS
Lookup table for Telephone Plan data, which lists the telephone plan details.
PLAN_ID Integer(2)
PLAN_DESC NVarChar(255)
Unique identifier of the phone plan purchased by the customer.
Name given to a phone plan.
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Column Name Data Type Description
PLAN_MINUTES
PLAN_PRICE_MO
PLAN_COST_ACQ
PLAN_COST_FIX_MO
PLAN_COST_VAR_MO
PLAN_PRICE_MINPK
PLAN_PRICE_MINOP
Double
Double
Double
Double
Double
Double
Double
Talk time, in minutes, available under the plan selected by the customer.
Price per month of the phone plan.
Acquisition cost of the phone plan.
Monthly fixed cost of the phone plan.
Monthly variable cost of the phone plan.
Price per minute for calls during peak time.
Price per minute for calls during off-peak time.
Table: LU_YEAR
Lookup table for Year data, which lists the calendar year of purchase.
YEAR_ID
YEAR_DATE
Integer(2)
TimeStamp
Unique identifier of year.
Year and date details.
YEAR_DURATION Integer(2) Duration in years.
Table: MNTH_CATEGORY_SLS
Fact table that stores sales data at the Month and Category logical level.
CATEGORY_ID Integer(2) Unique identifier of the category values.
MONTH_ID Integer(4)
TOT_UNIT_SALES
TOT_DOLLAR_SALES
TOT_COST
GROSS_DOLLAR_SALES
Double
Double
Double
Double
Unique identifier of month values.
The number of individual items bought by a customer.
The total income produced by a given source accounting for all product sales deducting discounts.
The total amount charged by the supplier to the company.
The total income received from the company’s product sales before deducting the expenses.
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Column Name Data Type
Table: MTD_DAY
Table that defines the month-to-date time period for a given date.
DAY_DATE
MTD_DAY_DATE
Table: ORDER_DETAIL
Fact table that stores Order Details.
TimeStamp
TimeStamp
ORDER_ID
ORDER_DATE
Integer(4)
TimeStamp
ITEM_ID
QTY_SOLD
UNIT_PRICE
Integer(2)
Double
Double
PROMOTION_ID
DISCOUNT
EMP_ID
UNIT_COST
CUSTOMER_ID
Integer(4)
Double
Integer(2)
Double
Integer(4)
Description
Day and date.
Month-To-Date day and date.
Unique identifier of an order.
Date on which the order was placed.
Unique identifier of an item.
Number of items sold.
The amount of money charged by the company to the customer per individual item sold.
Unique identifier of the date range for a particular discount period under which an item is purchased.
A monetary reduction made from a regular price.
Unique identifier of an employee.
The amount of money charged by the supplier to the company per individual item purchased.
The unique identification number allocated to the customer by the company.
Table: ORDER_FACT
Fact table containing Order data.
ORDER_ID
EMP_ID
Integer(4)
Integer(2)
ORDER_DATE
ORDER_AMT
TimeStamp
Double
Unique identifier of an order.
Unique identifier of an employee.
Date on which the order was placed.
The total amount of money charged by the company to the customer for the order.
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Column Name Data Type Description
FREIGHT
SHIP_DATE
QTY_SOLD
ORDER_COST
CUSTOMER_ID
PYMT_TYPE
SHIPPER_ID
GROSS_DOLLAR_SALES
Double
TimeStamp
Double
Double
Integer(4)
Integer(2)
Integer(2)
Double
The compensation paid for the transportation of goods.
The date on which an order is shipped from the distribution center.
Quantity of items sold in the order.
The total amount charged by the supplier to the company for the ordered items.
The unique identification number allocated to the customer by the company.
The mode used by a customer to pay for an order.
Unique identifier of the vendor used to send products to the customer.
The total income received from the company’s product sales before deducting the expenses.
Table: PROMOTIONS
Fact table for Promotion details.
ITEM_ID
DAY_DATE
Integer(2)
TimeStamp
Unique identifier of an item.
The day and date on which the transaction took place.
Table: QTD_DAY
Table that defines the quarter-to-date time period for a given date.
DAY_DATE TimeStamp Day and date.
QTD_DAY_DATE TimeStamp
Table: QTR_CATEGORY_SLS
Fact table that stores sales data at Quarter and Category logical level.
Quarter-to-date day and date.
CATEGORY_ID Integer(2)
QUARTER_ID Integer(2)
Unique identifier of the category values.
Unique identifier of the quarters.
TOT_UNIT_SALES Double The number of individual items bought by a customer.
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Column Name Data Type Description
TOT_DOLLAR_SALES
TOT_COST
GROSS_DOLLAR_SALES
Double
Double
Double
The total income produced by a given source accounting for all product sales deducting discounts.
The total amount charged by the supplier to the company.
The total income received from the company’s product sales before deducting the expenses.
Table: REL_CAT_ITEM
Relation table showing relation of Category and Item.
ITEM_ID
CAT_ID
Integer(2)
Integer(2)
Unique identifier of an item.
Unique identifier of category values.
Table: RUSH_ORDER
Table for Rush Order data that indicates whether a customer chose to expedite delivery of an order.
ORDER_ID
RUSH_CHARGE
Integer(4)
Real
Unique identifier of an order.
The amount of money charged to expedite delivery service.
Table: STATE_REGION_MNTH_SLS
Fact table that stores sales data at State, Region, and Month logical levels.
CUST_STATE_ID Integer(2)
REGION_ID
MONTH_ID
TOT_DOLLAR_SALES
Integer(2)
Integer(4)
Double
Unique identifier of customer state values.
Unique identifier of region values.
Unique identifier of a month.
The total income produced by a given source accounting for all product sales deducting discounts.
TOT_UNIT_SALES
TOT_COST
Double
Double
The number of individual items bought by a customer.
The total amount charged by the supplier to the company.
GROSS_DOLLAR_SALES Double The total income received from the company’s product sales before deducting the expenses.
Table: STATE_SUBCATEG_MNTH_SLS
Fact table that stores sales data at State, Subcategory, and Month logical levels.
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Column Name Data Type Description
CUST_STATE_ID
SUBCAT_ID
MONTH_ID
TOT_DOLLAR_SALES
Integer(2)
Integer(2)
Integer(4)
Double
Unique identifier of customer state values.
Unique identifier for the subcategory of an item.
Unique identifier of a month.
The total income produced by a given source accounting for all product sales deducting discounts.
TOT_UNIT_SALES
TOT_COST
Double
Double
GROSS_DOLLAR_SALES Double
Table: STATE_SUBCAT_REGION_SLS
Fact table that stores sales data at State, Subcategory, and Region logical levels.
CUST_STATE_ID Integer(2)
The total income received from the company’s product sales before deducting the expenses.
SUBCAT_ID Integer(2)
Unique identifier of customer state values.
Unique identifier for the subcategory of an item.
REGION_ID Integer(2)
The number of individual items bought by a customer.
The total amount charged by the supplier to the company.
TOT_DOLLAR_SALES
TOT_UNIT_SALES
TOT_COST
Double
Double
Double
Unique identifier of region values.
The total income produced by a given source accounting for all product sales deducting discounts.
The number of individual items bought by a customer.
The total amount charged by the supplier to the company.
GROSS_DOLLAR_SALES Double The total income received from the company’s product sales before deducting the expenses.
Table: SUBCAT_MNTH_CTR_SLS
Fact table that stores sales data at Subcategory, Month, and Call Center logical levels.
SUBCAT_ID Integer(2) Unique identifier of the subcategory of an item.
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Column Name
MONTH_ID
CALL_CTR_ID
TOT_DOLLAR_SALES
TOT_UNIT_SALES
TOT_COST
GROSS_DOLLAR_SALES
YTD_DAY_DATE
Data Type
Integer(4)
Integer(2)
Double
Double
Double
Double
TimeStamp
Description
Unique identifier of a month.
Unique identifier of call center values.
The total income produced by a given source accounting for all product sales deducting discounts.
The number of individual items bought by a customer.
The total amount charged by the supplier to the company.
The total income received from the company’s product sales before deducting the expenses.
Table: YR_CATEGORY_SLS
Fact table that stores sales data at Year and Category logical levels.
CATEGORY_ID Integer(2)
YEAR_ID
TOT_UNIT_SALES
TOT_DOLLAR_SALES
TOT_COST
GROSS_DOLLAR_SALES
Integer(2)
Double
Double
Double
Double
Unique identifier of the category values.
Unique identifier of the year of purchase.
The number of individual items bought by a customer.
The total income produced by a given source accounting for all product sales deducting discounts.
The total amount charged by the supplier to the company.
The total income received from the company’s product sales before deducting the expenses.
Table: YTD_DAY
Table that defines the year-to-date time period for a given date.
DAY_DATE TimeStamp The day and date on which the transaction took place.
The year-to-date day and date.
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B.
L
OGICAL
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Introduction
MicroStrategy uses logical tables to relate the data stored in your data warehouse to the schema objects that constitute a MicroStrategy project.
Logical tables and logical table aliases represent physical tables in your data warehouse.
Logical views
are defined using SQL queries against the data warehouse, which can combine data from multiple physical tables into one logical table representation in MicroStrategy.
This chapter introduces you to the different types of logical tables, with a focus on how you can use the logical view feature to take advantage of the enhanced schema support in MicroStrategy.
Logical tables
Logical tables
are MicroStrategy objects that form the foundation of a schema. Physical tables in a data warehouse consist of columns, and logical tables in the MicroStrategy schema relate this column data to attributes and facts. These attributes and facts are part of the report definition that the
MicroStrategy Engine refers to when a report is executed.
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The types of logical tables are:
• Logical table
: A logical representation of a physical table that
MicroStrategy uses to generate SQL. A logical table is created for each physical table that is imported into a project, using the Warehouse
Catalog. This type of logical table maps directly to physical tables in the data warehouse. These physical tables are referenced in the SQL that is generated for the report.
This type of logical table is the most common logical table. Based on these tables, you can create MicroStrategy schema objects, such as attributes and facts. For more information on how to use the Warehouse Catalog, refer to the Help (search for “Warehouse Catalog”).
Logical tables also allow you to support the following configurations:
Defining logical table sizes, page 452
Defining the primary key for a table, page 455
• Logical table alias
: A logical table that uses a different name for a physical table. One physical table can have more than one logical table alias. Logical table aliases are used to create attribute roles.
When an attribute plays more than one role, you need to create an attribute in the logical model for each of the roles. To accomplish this, you create multiple logical table aliases pointing to the same physical table and define those logical table aliases as the lookup tables for the attributes in different roles.
For example, if the Customer table is used to represent both Ship to
Customer and Bill to Customer, you can create a logical table alias to resolve the double usage case. First, create a logical table alias by copying the existing logical table and giving it a different name; then define the new attributes using the appropriate tables.
For detailed information on attribute roles, refer to
Attributes that use the same lookup table: Attribute roles, page 276
alias, right-click the logical table name and select Create Table Alias.
For step-by-step instructions, refer to the Help (search for “Create a table alias”).
• Logical view
: A logical table that is created using a SQL statement instead of being a direct representation of a physical table. Once created, the logical view can be used in the same way as any logical table. The logical view is also referenced in the SQL that is generated for the report; the whole SQL query is displayed in the place of physical tables, as is
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Project Design Guide Logical Tables B standard for other logical tables. Logical views are created using the Table
Editor.
If your project supports data internationalization, you cannot use logical views as lookup tables for attributes that use translated data. For information on supporting data internationalization, see
Supporting data internationalization, page 56
.
Logical views are different from the above-mentioned logical tables and logical table aliases for the following reasons:
Logical views do not map directly to physical tables in the data warehouse, and instead are defined using SQL queries.
Logical views are manually created to return data, from potentially multiple physical tables, as one logical table. Logical tables and logical table aliases can only retrieve data from a single physical table.
However, once logical views are created, they can be used in the same way as logical tables and logical table aliases. This means that you can use the logical views to build attributes and facts, and that you can also create logical table aliases for the logical views.
The biggest benefit of using logical views is that you can model a
MicroStrategy schema that cannot be supported with only the physical database structures in the data warehouse. Many common modeling scenarios are easier to manage using logical views. Examples are:
Slowly-changing dimensions
Attribute form expressions from multiple tables
Consolidated dimension tables
Recursive hierarchies
For common usage examples, refer to
Logical view examples, page 460
.
In the MicroStrategy Tutorial, logical tables and all the other schema objects are stored in the Schema Objects folder. Using the Logical Table Editor, you can define your logical view using a SQL statement, as well as view the content of all the logical tables and their associated warehouse tables.
Whenever you create or add logical tables, logical table aliases, or logical views to the project, you need to update the schema. The Update Schema option can be accessed from the Schema menu.
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Creating logical tables
Logical tables are brought into the project by using the Warehouse Catalog or
Architect, and table aliases are created by duplicating existing logical tables.
Steps on how to add logical tables to a project are provided for both methods, as listed below:
•
Using Architect:
Adding, removing, and administering tables, page 126
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Using the Warehouse Catalog:
Adding and removing tables for a project, page 297
Defining logical table sizes
MicroStrategy assigns a size to every table in the project when you initially add it to the project. These size assignments are based on the columns in the tables and the attributes to which those columns correspond. Because
MicroStrategy uses the conceptual or logical attribute definitions to assign a size to each table in the project, this measurement is referred to as
logical table size
.
The logical table size determines which logical table to use to answer a query when multiple logical tables would provide the same data. The table with the lowest logical table size is used, as a lower logical table size means the table has a higher level of aggregation. The performance of accessing a table with a higher level of aggregation is usually better than accessing a table with a lower level of aggregation.
MicroStrategy initially assigns logical table sizes based on an algorithm that takes into account the number of attribute columns and the various levels at which they exist in their respective hierarchies. If this does not meet your requirements, you can define the logical table size for a table as required.
For example, a base fact table contains millions of rows of transaction-level data. The other tables have only higher-level or summary information. Since the levels of the attributes are lower in the base fact table, the base fact table is assigned a higher value for its logical table size than are the summary tables with higher-level attributes. This means the table with summary information is used to return data rather than the table with transaction-level data when the tables can be used to answer the same query.
However, in the example scenario described above, there may be a reason that you want to access the table with transaction-level data instead of the
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Project Design Guide Logical Tables B table with summary information. In these types of scenarios, you can manually define the logical table size for a table, for which you have the following options:
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Defining the logical table size of a single table, page 453
•
Defining logical table size while comparing all project tables, page 454
Defining the logical table size of a single table
The steps to define the logical table size of a single table using the Table
Editor are described below.
To define the logical table size of a single table
1
From MicroStrategy Developer, log in to a project.
Browse to the location where you store your tables, and double-click the desired table. If a message is displayed asking if you want to use read only mode or edit mode, select Edit and click OK to open the Table Editor in edit mode so that you can make changes to the table. The Table Editor opens.
Note the following:
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If you are only given the option of using read only mode, this means another user is modifying the project’s schema. You cannot use edit mode until the other user is finished with their changes and the schema is unlocked.
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For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
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On the Logical View tab, in the Logical size area, type the value for the table’s logical size.
If multiple tables can be used to answer the same query, the table with the lowest logical table size is used.
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To lock the logical table size of a table, select the Preserve this logical
size when updating schema information
check box. When a table’s logical size is locked, the table is excluded from the logical table size calculation when a schema update is performed.
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Click Save and Close to save your modifications to the table and close the Table Editor.
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You must update the schema for the new logical table size to take effect.
Close all editors, then from the Schema menu, select Update Schema.
Defining logical table size while comparing all project tables
The steps to define the logical table size of a table while comparing all project tables are described below.
To define the logical table size of a table while comparing all project tables
1
From MicroStrategy Developer, log in to a project.
From the Schema menu, select Edit Logical Size. If a message is displayed asking if you want to use read only mode or edit mode, select
Edit
and click OK to open the Logical Size Editor in edit mode so that you can make changes to the table’s logical size. The Logical Size Editor opens.
Note the following:
•
If you are only given the option of using read only mode, this means another user is modifying the project’s schema. You cannot use edit mode until the other user is finished with their changes and the schema is unlocked.
•
For information on how you can use read only mode and edit mode for various schema editors, see
Using read only or edit mode for schema editors, page 99
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In the Size value column for a table, type the desired value for the table's logical size.
Note the following:
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If multiple tables can be used to answer the same query, the table with the lowest logical table size is used.
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You can sort any column in the Logical Size Editor by clicking on the column heading.
•
You can display the number of rows in each table by selecting
Show the table row count
from the Edit menu.
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To lock the size of a table, select that table’s Size locked check box. When a table’s logical size is locked the table is excluded from the logical table size calculation when a schema update is performed. This helps to retain any table sizes that are manually defined.
To unlock the table's size, clear the Size locked check box.
To lock or unlock all table values displayed, click the Lock all or
Unlock all
toolbar options.
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To allow MicroStrategy to calculate the table logical size automatically for all tables, click the Calculate all icon.
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From the File menu, select Save to save your changes.
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From the File menu, select Close to close the Logical Size Editor.
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You must update the schema for any new logical table sizes to take effect.
Close all editors, then from the Schema menu, select Update Schema.
Defining the primary key for a table
MicroStrategy requires each table to have a primary key, which is a unique value identifying each distinct data record or row. A primary key can be defined by one or more columns in the table. For more information on primary keys, see
Uniquely identifying data in tables with key structures, page 37
.
MicroStrategy determines the primary key for a table based on the attributes mapped to the columns of the table. In the Table Editor on the Layout tab, a key icon displayed by the attribute name indicates that the attribute is part of the primary key for this table. The primary key is made up of the lowest level attributes. For example, a warehouse table's primary key is defined using the columns CUSTOMER_ID, PRODUCT_ID, and ORDER_ID. If these three columns are mapped to attributes in MicroStrategy, then the primary key is represented correctly. In this case, from the Table Editor’s Layout tab, you can select the The key specified is the true key for the warehouse table check box. This is the default behavior for all tables added to a MicroStrategy project.
If the primary key for the warehouse table is not identical to the attributes listed as key attributes for the table in MicroStrategy, clear the The key
specified is the true key for the warehouse table
check box. Using the example described above, consider the scenario in which CUSTOMER_ID and
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PRODUCT_ID are mapped to MicroStrategy attributes, but ORDER_ID is not mapped to an attribute in MicroStrategy. In this case, only CUSTOMER_ID and PRODUCT_ID would be recognized by MicroStrategy as part of the table’s primary key. In this scenario, this check box should be cleared so that the warehouse table’s primary key can be verified. Otherwise, selecting this check box for such a scenario could cause incorrect or erroneous data to be returned.
Creating logical views
Logical views can be created in MicroStrategy Developer using the Table
Editor, which is shown in the image below.
The Object Browser on the left-hand side lists all tables and columns that have been imported into the project. Any physical table in the project database instance can be used in the SELECT statement. The SQL statement panel, displayed near the top and on the right-hand side, is where you type in your SQL query. The Mapping panel, displayed just below the SQL statement panel, is where you map the columns returned by the SQL query.
Since SQL queries are required to create logical views, you should be experienced with using SQL before you use the logical view feature. It is your
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Because the MicroStrategy Engine does not parse through the SQL syntax, the statistics log does not contain any information about the actual physical tables accessed; the logical view is logged instead. The same holds true if you use a view in the database, in which case table objects accessed are not logged either.
In the SQL generated for a report, logical views are generated as either a derived table or a common table expression (CTE) depending on the type of database that you use. It is recommended that you use derived tables to define logical views, although CTEs are also supported by some databases.
Derived tables are advantageous because they are nested in the SQL generated by the Engine. CTEs, however, are not nested in the SQL because this would result in invalid SQL syntax. Refer to your third-party database documentation for any applicable SQL syntax requirements.
When the MicroStrategy Engine needs to use a logical table that maps directly to a physical database table, it inserts the name of the table into the
FROM clause. For a logical view, which maps to a SQL statement, the
MicroStrategy Engine inserts the SQL syntax in the FROM clause. The
Engine generates derived table syntax to represent the logical view.
The results of logical views are not cached. Instead, the logical view appears as additional syntax in the report SQL generated by
MicroStrategy.
The steps on how to create a logical view are described below.
Prerequisite
•
You must have the MultiSource Option to create logical views on data sources other than the data source for the primary database instance. If you do not have the MultiSource Option, then you can only create logical views for the data source that belongs to the primary database instance.
To create a logical view in the Table Editor
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From the File menu, select New and then Logical Table. The Table
Editor is displayed with the Physical View tab selected by default.
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If you have the MultiSource Option, from the Select current database
instance
drop-down list, select which data source to retrieve the data from for the logical view. Select a database instance for the data source to use for the logical view.
If you do not have the MultiSource Option, then you can only create logical views for the data source that belongs to the primary database instance. This means that there is no drop-down list to select a database instance.
When choosing a database instance, if you select the database instance listed as the project’s primary database instance, the database instance for the logical view is changed anytime the project’s primary database instance is changed. For examples of when to use each option, see
Importing tables as part of the project’s primary database instance, page 329
.
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In the SQL Statement panel, type your SQL statement. You can drag and drop columns from the Object Browser to insert them into the statement.
It is recommended that you use derived tables to define logical views because the logical view SQL syntax becomes nested inside SQL statements generated by the Engine. Although common table expressions
(CTEs) are also supported for some databases, these expressions cannot be nested in the SQL because this would result in invalid SQL syntax.
Refer to your third-party database documentation for any applicable SQL syntax requirements.
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By default, the Enclose SQL statement in parentheses check box is selected. This encloses the entire SQL statement that you typed in the
SQL statement panel in parentheses when the SQL statement is executed, which is valid if the SQL statement uses a standard select clause.
However, if you include SQL functions to create tables, the parentheses can cause invalid SQL syntax. Table creation syntax in a SQL statement can be of the form TABLE (arg1, arg2, ..., argN). If you include table creation SQL functions or other SQL functions that cannot support parentheses that enclose the entire SQL statement, clear this check box.
Refer to your third-party database documentation for any applicable SQL syntax requirements.
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Click Add to map columns returned by the SQL statement.
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Type in the column name under Column Object. This creates a new column.
Alternatively, you can also drag and drop columns from the Object
Browser to the Column Object cell. By doing this, you map an existing column to the logical view.
The names of the columns must match exactly the column aliases defined in the SQL statement. However, the order of the columns does not have to match the order in which the column aliases appear in the SQL statement.
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Select a Data Type for the column by using the drop-down list.
If you used an existing column in the mapping in this step, the data type of that column is inherited. If you change the data type, the change will affect all the tables with that column.
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Modify the Precision and Scale of the column, if applicable:
• Precision
: The total number of digits used to define a number. For example, the number 1234.56789 has a precision of nine and a scale of five.
• Scale
: The number of digits to the right of the decimal point used to define a number. The scale of a data type must be less than or equal to the precision for the data type. For example, the number 1234.56789 has a scale of five and a precision of nine.
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From the toolbar, click Save and Close to save the logical table and close the Table Editor.
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From the Schema menu, select Update Schema to ensure that the new logical view is loaded into the project.
Based on the logical view that you just created, you can now create new attributes and facts, as you would with any other logical table (see
and
). You can also map the columns of the logical table to existing attributes and facts. This can be done by modifying the attributes or facts to include the logical table as a source table.
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Logical view examples
The following business cases are intended to help you understand how you can use the logical view feature in your applications.
Business case 1: Distinct attribute lookup table
Many star schemas feature a single lookup table that is shared by all the attributes in one dimension (see the following example). While it is possible to model a schema with such a dimension table, often two problems arise:
•
The model cannot support fact tables at the level of attributes that are not keys. This restriction applies to summary tables as well as to intermediate results that may be generated by the SQL Engine.
Usually, in one-SQL-pass reports, the MicroStrategy Engine joins the fact table with one lookup table and does the aggregation. If there is no distinct list of attribute elements, you may double count if you have to join to a table where that attribute is part of the key.
•
Too many rows in the dimension table may slow down the SELECT
DISTINCT query, thus affecting element browsing requests that display a list of attribute elements, for example, when populating pick lists for prompts.
The following is an example lookup table for Store, Market, and Region.
Lookup_store
Store_ID Store_Name Market_ID Market_Name Region_ID Region_Name Level
In this table, Market and Region are not the keys. Therefore, if the requested fact table is at the Market or Region level, a direct join between the fact table and the above lookup table may result in double-counting. To avoid that, you can use the Logical View feature to define another logical table
Lookup_Market as follows:
Select Market_ID, Market_Name, Region_ID
From Lookup_store
Where level=1
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Then use this table as the lookup table for Market. When it is joined with a
Market-level fact table (Market_Sales), the following report SQL is generated:
Select a11.Market_ID,a11.Market_Desc,
SUM(a12.Sales)
From (select Market_ID, Market_Name, Region_ID from Lookup_Store where level=1) a11,
Market_Sales a12
Where a11.Market_ID = a12.Market_ID
Group by a11.Market_ID, a11.Market_Name
Business case 2: Attribute form expression across multiple tables
Customers often request the ability to generate an attribute form expression across multiple tables. Usually, the case is on Date columns. For example, you want to define an attribute based on the Date difference between two Date columns (Ship_Date and Order_Date) in two different tables as follows.
F_Table1
Ship_Date Order_ID Fact1
F_Table2
Order_Date Order_ID Fact2
Using the Logical View feature, you can use the following SQL query to create a logical table to calculate the Date difference and then define the attribute on that new column:
Select Ship_Date-Order_Date Cycle_time,
F_table1.Order_ID, Fact1, Fact2
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