MicroStrategy Functions Reference

Functions Reference
Version: 10.10
10.10, December 2017
Copyright © 2017 by MicroStrategy Incorporated. All rights reserved.
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Patent Information
This product is patented. One or more of the following patents may apply to the product sold herein: U.S. Patent Nos. 6,154,766, 6,173,310, 6,260,050, 6,263,051, 6,269,393, 6,279,033,
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, 6,741,980, 6,765,997, 6,768,788, 6,772,137, 6,788,768, 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, 6,940,953, 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, 7,113,993, 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, 7,486,780, 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, 8,051,168, 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, 8,943,187. 8,958,537, 8,966,597, 8,983,440, 8,984,274, 8,984,288, 8,995,628, 9,027,099, 9,027,105, 9,037, 577, 9,038,152,
9,076,006, 9,086,837, 9,116,954, 9,124,630, 9,154,303, 9,154,486, 9,160,727, 9,166,986, 9,171,073, 9,172,699, 9,173,101, 9,183, 317, 9,195,814, 9,208,213, 9,208,444, 9,262,481, 9,264,415,
9,264,480, 9,269,358, 9,275,127, 9,292,571, 9,300,646, 9,311,683 9,313,206, 9,330,174, 9,338,157, 9,361,392, 9,378,386, 9,386,416, 9,391,782, 9,397,838, 9,397,980, 9,405,804, 9,413,710,
9,413,794, 9,430,629, 9,432,808, 9,438,597, 9,444,805, 9,450,942, 9,450,958, 9,454,594, 9,507,755, 9,513,770, 9,516,018, 9,529,850, 9,563,761, 9,565,175, 9,608,970, 9,640,001, 9,646,165,
9,680,908, 9,697,146, 9,697,350, 9,742,764, 9,742,781, 9,743,235, 9,762,564, 9,794,245, 9,801,053, and 9,807,074. Other patent applications are pending.
1
CONTENTS
Overview and Additional Resources
5
Function categories
6
Function descriptions
6
How to find business scenarios and examples
7
What’s new in this guide
7
Prerequisites
9
Who should use this guide
9
Resources
9
Feedback
1. Understanding Functions in MicroStrategy
17
18
The basics of MicroStrategy objects, object definitions, and functions
18
Function syntax and formula components
20
Using functions in expressions
37
How MicroStrategy processes functions
55
Additional examples of functions in expressions
70
2. Standard Functions
Basic functions
91
92
Date and time functions
116
Internal functions
130
NULL/Zero functions
145
OLAP functions
147
Rank and NTile functions
202
String functions
220
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3. Operators
Arithmetic operators
238
Comparison operators
240
Comparison for rank operators
248
Logical operators
252
4. Plug-In Package Functions
254
Data mining functions
255
Financial functions
257
Mathematical functions
267
Statistical functions
270
A. MicroStrategy and Database Support for Functions
4
238
277
Analytical Engine support for functions
277
Databases that a function can be evaluated on
281
Glossary
592
Index
629
© 2017, MicroStrategy Inc.
1
OVERVIEW AND ADDITIONAL
RESOURCES
Functions in MicroStrategy are powerful tools used in expressions to define MicroStrategy
objects and initiate complex user-selected calculations. Wherever you can define an
expression, you can use a function. From creating basic objects to building complex reports
and analyzing data, you have the ability to create custom expressions using a large library of
functions supported by MicroStrategy. Although functions are most commonly used in metric
expressions, they are also used to create filters, define attribute forms, and so on.
This guide provides the following information:
•
Details of function syntax and the components of a function formula
•
Explanation of where functions can be used in expressions
•
Steps to access functions from MicroStrategy Developer
•
Overview of the MicroStrategy engine and how Intelligence Server uses functions
•
Examples of functions used in complex business scenarios
•
Details of function support in certified databases
This guide does not include information on Data Mining functions. This information can be
found in the Data Mining Services chapter of the Advanced Reporting Guide.
These details are described in the following chapters:
•
Chapter 1, Understanding Functions in MicroStrategy
•
Chapter 2, Standard Functions
•
Chapter 3, Operators
•
Chapter 4, Plug-In Package Functions
•
Appendix A, MicroStrategy and Database Support for Functions
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Function categories
For the purposes of this guide, functions are organized into the following categories:
•
•
•
Chapter 2, Standard Functions
▫
Basic functions, page 92
▫
Date and time functions, page 116
▫
Internal functions, page 130
▫
NULL/Zero functions, page 145
▫
OLAP functions, page 147
▫
Rank and NTile functions, page 202
▫
String functions, page 220
Chapter 3, Operators
▫
Arithmetic operators, page 238
▫
Comparison operators, page 240
▫
Comparison for rank operators, page 248
▫
Logical operators, page 252
Chapter 4, Plug-In Package Functions
▫
Data mining functions, page 255
▫
Financial functions, page 257
▫
Mathematical functions, page 267
▫
Statistical functions, page 270
Function descriptions
For every function identified, the description includes:
6
•
Information returned
•
Function syntax
•
Expression representative of the function (as applicable)
•
Notes regarding restrictions or conditions on execution and use (as applicable)
•
Examples of the function in reports or through simple descriptions
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Functions Reference
The following sections provide the location of additional 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, each function is accompanied by an example, as well as some usage
guidelines. The first chapter includes additional examples that involve several functions in
business use cases.
For examples of reporting functionality, see 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
•
Documentation has been provided for the following new functions:
▫
Condition, page 94: A shortcut function, available for various features in
MicroStrategy Web, that allows you to easily define the condition (filtering) of the
final metric expression.
▫
Level , page 102: A shortcut function, available for various features in MicroStrategy
Web, that allows you to easily define the level (dimensionality) of the final metric
expression.
▫
Transformation, page 114: A shortcut function, available for various features in
MicroStrategy Web, that allows you to easily define transformations for the final
metric expression.
▫
DateDiff, page 119: Calculates the length of time between two dates.
▫
FiscalMonth, page 122: Returns the numeric position of a month within a fiscal year,
for a given input date.
▫
FiscalQuarter , page 122: Returns the numeric position of a quarter within a fiscal
year, for a given input date.
▫
FiscalWeek, page 123: Returns the numeric position of a week within a fiscal year,
for a given input date.
▫
FiscalYear, page 123: Returns the fiscal year of the input date.
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8
▫
QuarterStartDate, page 127: Returns the date of the first day of the quarter in which
a date or timestamp occurs.
▫
ToDateTime (convert string or number to a date or timestamp), page 128: Converts
a string of characters or a number into a date or timestamp.
▫
WeekStartDate, page 129: Returns the date of the first day of the week in which a
date or timestamp occurs.
▫
NullToEmpty, page 146: Converts a value of NULL to an empty string.
▫
WeightedCorr (weighted correlation), page 194: Allows you to apply a weight, or
relative significance to each correlation.
▫
WeightedCov (weighted covariance), page 196: Allows you to apply a weight, or
relative significance to each covariance.
▫
WeightedMean, page 197: Allows you to apply a weight, or relative significance to
each value when determining an average.
▫
WeightedStDev (weighted standard deviation of a sample), page 199: Allows you to
apply a weight, or relative significance to each value in a set of values.
▫
PercentRank , page 214: Displays the ranking of values as a percentage.
▫
PercentRankRelative, page 215: Displays the ranking of values as a percentage,
with the ranking based on a secondary data set.
▫
BeginsWith, page 221: Determines if a text string begins with a specified text
pattern.
▫
Char (convert ASCII code to a character), page 221: Converts a decimal ASCII
code into its associated character.
▫
ConcatAgg (concatenate plus delimiter), page 222: Takes all of the content from a
single input and concatenates the content as a single string.
▫
EndsWith, page 224: Determines if a text string ends with a specified text pattern.
▫
LastPosition (last position of substring), page 225: Returns the starting position of
the last occurrence of a series of characters in the input string.
▫
Match, page 228: Uses regular expressions to search a string for a pattern of
characters and returns any matches that are found.
▫
RepeatStr (repeat string), page 230: Returns a character or string of characters the
specified number of times.
▫
Replace, page 230: Searches a string for a pattern of characters and replaces each
instance of those characters with the new characters you specify.
▫
Split, page 232: Searches a string, separates the contents into groups of characters
based on a delimiter, and returns the string of characters requested.
▫
ToNumber (convert string to a number), page 235: Converts a string of characters
to its applicable numeric value.
© 2017, MicroStrategy Inc.
Functions Reference
•
▫
ToString (convert number, date, or timestamp to a string), page 235: Converts a
number, date, or timestamp to a string of characters.
▫
TitleCap (title capitalization), page 234: Returns a string in which the first letter of
every word in the input string is capitalized.
▫
XIRR (internal rate of return for payments at irregular intervals), page 267: Returns
the internal rate of return on a set of payments that do not occur at regular intervals.
▫
XNPV (net present value of an investment for payments or incomes at irregular
intervals), page 267: Returns the net present value of an investment based on a
discount rate and a set of future payments (negative values) and income (positive
values).
Function support for databases has been updated in Databases that a function can be
evaluated on, page 281.
MicroStrategy Analytics Enterprise
•
The name of MicroStrategy Desktop has been changed to MicroStrategy Developer.
MicroStrategy 9.4
•
Function support for databases has been updated in Databases that a function can be
evaluated on, page 281.
Prerequisites
Before reading this document, you should be familiar with:
•
Basic MicroStrategy terminology such as metrics, facts, attributes, and so on. This
information is found in the Basic Reporting Guide.
•
Standard mathematical function notation.
Who should use this guide
This document is designed for any user who needs to create an expression using any of the
predefined functions MicroStrategy offers.
Resources
This section provides details on how to access books, online help, MicroStrategy Education
and Consulting resources, and how to contact MicroStrategy Technical Support.
© 2017, MicroStrategy Inc.
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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 15.
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:
10
•
For iOS devices, scan the following QR code:
•
For Android devices, scan the following QR code:
© 2017, MicroStrategy Inc.
Functions Reference
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.
•
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.
© 2017, MicroStrategy Inc.
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Functions Reference
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.
•
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.
•
12
MicroStrategy Mobile Analysis Guide: Analyzing Data with MicroStrategy
Mobile
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Functions Reference
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.
•
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
© 2017, MicroStrategy Inc.
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Functions Reference
The Web SDK is available in the MicroStrategy Developer Library, which is part of the
MicroStrategy SDK.
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.
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 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.
14
© 2017, MicroStrategy Inc.
Functions Reference
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 15
•
To access documentation resources on Windows, page 15
•
To access documentation resources on UNIX and Linux , page 15
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.
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.
© 2017, MicroStrategy Inc.
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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.
Type
bold
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 .
italic
• 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
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.
16
© 2017, MicroStrategy Inc.
Functions Reference
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 Developer: Reporting Essentials
•
MicroStrategy Web: Report Analysis
•
MicroStrategy Web: Report Design
For a detailed description of education offerings and course curriculums, 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.
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: documentationfeedback@microstrategy.com.
Send suggestions for product enhancements to: support@microstrategy.com.
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.
© 2017, MicroStrategy Inc.
17
1
UNDERSTANDING FUNCTIONS
IN MICROSTRATEGY
This chapter provides the following information:
•
The basics of MicroStrategy objects, object definitions, and functions, page 18
•
Function syntax and formula components, page 20
•
Using functions in expressions, page 37
•
Adding functions to expressions with the Insert Function Wizard, page 53
•
How MicroStrategy processes functions, page 55
•
Additional examples of functions in expressions, page 70
The basics of MicroStrategy objects, object
definitions, and functions
To understand functions and their role in MicroStrategy, it is important to grasp the basic
concepts underlying objects and expressions.
An object is a basic building block in MicroStrategy. There are three types of objects: schema
objects, application objects, and configuration objects. Schema objects include facts,
hierarchies, and custom groups; application objects include reports, documents, and
metrics; and configuration objects include project sources, database instances, and users.
(This list of objects is not exhaustive.)
MicroStrategy objects are created, maintained and deleted by you. Your Architect, for
instance, will create attributes by mapping conceptual data from your data warehouse to
names like Month, Customer Name, or Product Category that will appear on your reports.
Your Designer will create metrics that access and manipulate numeric data from your data
warehouse.
Many objects require you to specify an expression when creating or modifying them. An
expression is any combination of characters that can be used as a result. Examples include
the following:
•
Month + 5
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•
Sum(Revenue)
•
New York AND Los Angeles
In the first example, each Month element in your data warehouse is assigned a numeric ID
between 1 and 12, where January is 1, February is 2, and so on. You want to generate a
report with a five-month forecast. One approach would begin with creating a new attribute
called 5 Months Ahead, using the expression Month + 5 in its attribute definition.
Expressions can be used in many places. Metrics in particular often require complicated
expressions. In the second example above, the expression Sum(Revenue) is used to
define a relatively simple metric. “Sum” tells MicroStrategy to read individual Revenue
entries from the data warehouse and add them together to produce one single number that
will be displayed as Revenue on your report. This metric is a MicroStrategy object that you
can create.
The last example above shows a logical expression, which might be used in a filter. A filter
comprised of the expression New York AND Los Angeles would let a user answer
questions such as “Which business travelers flew out of New York and out of Los Angeles in
2006?”
Expressions often form the basis of objects, which are the basic building blocks of all
MicroStrategy content and functionality. Objects that rely on these expressions are often
edited by changing the object expressions.
In this book, the terms “object expression” and “object definition” are interchangeable unless
otherwise noted.
To create an expression that accomplishes your goal, you will almost always utilize a
MicroStrategy function. Functions in MicroStrategy are powerful tools used to define
MicroStrategy objects (when they are integrated into object definitions) and initiate complex
user-selected calculations. The example Sum(Profit) in a metric definition uses the Sum
function to add various Profit entries in a data warehouse to arrive at one final number to
display on a report.
Wherever you use an expression, you can use a function. From creating basic objects to
building complex reports and analyzing data, you can create custom expressions using a
large library of functions that come with and are supported by MicroStrategy. Although
functions are most commonly used in metric expressions, they are also used to define
attribute forms, consolidation elements, custom groups, filters, facts, subtotals, and
transformations, all of which are MicroStrategy objects.
Functions commonly used to create specific objects
The following table lists the function types described above, several functions belonging to
each type, and the MicroStrategy objects that can be created using those functions.
Function Type
Single-value
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Function Examples
• Arithmetic operators
(–, +, /, *)
MicroStrategy Objects That Can Use These Functions
• Attribute form
• Consolidation
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Function Type
Function Examples
MicroStrategy Objects That Can Use These Functions
• Abs
• Custom group
• Round
• Fact
• Ln
• Metric
• Cos
• Subtotal
• Transformation
Group-value
• Avg
• Metric
• Count
• Subtotal
• Sum
OLAP
• Rank
Metric
• MovingMin
• NTile
Logical
• And
Filter
• Or
Comparison
• Between
Filter
• ApplyComparison
Apply
• ApplySimple
• Attribute form
• ApplyAgg
• Consolidation
• ApplyOLAP
• Custom group
• ApplyLogic
• Fact
• ApplyComparison
• Metric
• Transformation
• Filter
Function syntax and formula components
You can use functions in any situations where you build an expression. Although an
expression can have unique characteristics, there is a basic syntax for applying functions in
expressions. The use of each function is described in detail in the rest of this book. This
section covers the basics of functions and their key components.
Each function is designated by a function name. Functions operate on an argument that can
be a fact, attribute, metric, or constant, and the function’s behavior is often further specified
using one or more parameters. Function parameters are used to fine-tune the behavior of
functions. Arguments provide the inputs to functions.
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In this guide, MicroStrategy functions are grouped into function types. This section provides
information on function types, as well as their parameters and arguments.
This guide does not include information on Data Mining functions. This information can be
found in the Data Mining Services chapter of the MicroStrategy Advanced Reporting Guide.
Function types
MicroStrategy functions are classified into the following types:
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•
Single-value functions (also known as Non-grouping or Scalar functions): These
functions operate on each individual component of an input variable or argument,
resulting in an output element for each component. Examples of this category are simple
mathematical operators (+, -, *, /), Abs, Accrint, Ddb, Cos, Ln, Round, Truncate,
ApplySimple, and so on. For details, see Single-value functions, page 22.
•
Group-value functions (also known as Grouping or Aggregate functions): These
functions take one or more lists of values as input and generate a single output value for
each list. Examples of this category are Avg, AvgDev, Correlation, Count,
HomoscedasticTTest, Intercept, Slope, StDev, Sum, ApplyAgg, and so on. For details,
see Group-value functions, page 23.
•
OLAP functions (also known as Relative functions): These functions take multiple
elements from a list and return a new list of elements. Each element is related to and
dependent on one or more other elements in the list, and the relative positions of
elements within the list determines how computation is performed. Examples include
Rank, RunningSum, MovingAvg, NTile, ApplyOLAP, and so on. For details, see OLAP
(Relative) functions, page 24.
•
Comparison operators: These operators compare single values or lists of values, or
compare a list to a threshold value. Examples of this category are Between, Like,
Greater than(>), Less than (<), ApplyComparison, and so on. For details, see
Comparison operators, page 25.
•
Logical operators: These operators provide basic comparisons and return TRUE or
FALSE values based on the evaluation of the formula. This type of operator includes
And, Or, and Not. For details, see Logical operators, page 25.
•
Apply functions: These functions provide access to functions and syntactic
constructs that are not standard in MicroStrategy but are offered by various relational
database management system (RDBMS) platforms. Each of the functions in this
category substitutes for one of the function types mentioned above and can be used
wherever that type is used. For example, ApplySimple can be used wherever a singlevalue function is used. For details, see Apply (Pass-through) functions, page 26.
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Single-value functions
A single-value function operates on each individual component of one or more arguments,
resulting in an output component for each set of input components. Subtraction, addition,
division, and multiplication operators (–, +, /, *) are common examples of single-value
functions. Other examples include Abs, Cos, Ln, Round, Sin, Trunc, ApplySimple, and so
on.
Single-value functions can be used to create facts, metrics, attribute forms, consolidation
elements, and transformations.
For example, using a single-value function that operates on four arguments, where each
argument is composed of a five-component list, returns five components. In other words, the
number of output components is equal to the number of input components in each argument.
A simple example using numbers follows:
Using the single-value “+” (addition) function, A+B=C
Where
•
A=1 (an argument containing one component whose value is 1)
•
B=2 (an argument containing a single component whose value is 2)
•
C=3 (the returned value, containing a single output component, whose value is 3)
In the example above, A, B, and C each contain one component. More generally, given
variables D and E used as arguments in the addition function, where D and E each contain a
five-component list, D+E results in a five-component list. Single-value functions need not
take a single value as an argument or even a single argument. Rather, the basic requirement
for a function to be categorized as single-value is that the number of output components
equals the number of input components of the arguments.
The following two examples illustrate the use of single-value functions in the creation of a
transformed fact and a compound metric. Transforming a fact and creating a compound
metric are similar in that both use a single-value function to turn one or more lists of values
into another list of values. They differ in that, for a transformation, the single-value function
must be applied before a group-value function, while in a compound metric the single-value
function is applied after the group-value function.
Example 1: Transformed fact
Avg(Abs([Account Transactions]))
Suppose Account Transactions is a list of the following values: -300.5, -7.7, 900, -80, and
2.2. The single-value function, Absolute, is applied to the list. The result set is the absolute
value of each element in the list: 300.5, 7.7, 900, 80, 2.2. It is important to note that the
single-value function returns five elements of output for five elements of input. Once the
single-value function has been applied, the group-value function, Avg, is applied to produce
an average of those values, 258.08. For more information on the Abs and Avg functions, see
Abs (absolute value), page 267 and Avg (average), page 93.
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Example 2: Compound metric
Avg(Revenue){Quarter} - Avg(Cost){Quarter}
In this example, the group-value function Avg is applied to both the Revenue and Cost facts
in your data warehouse. First, MicroStrategy uses the list of values for the two input variables
Revenue and Cost to generate, using the Avg function twice, two new variables each
containing a single value. The two resulting variables are stored as intermediate results.
Next, the single-value function “-” (subtraction) is applied by MicroStrategy to subtract one
intermediate result from the other, resulting in a single value for the metric. For more
information on the Abs and Avg functions, see Abs (absolute value), page 267 and Avg
(average), page 93.
In both of the previous examples, both single-value and group-value functions were used.
The next section addresses group-value functions in more detail.
Group-value functions
A group-value function takes one or more lists of values as input and returns a single output
value for each list. The existence of a GROUP BY clause in a SQL statement indicates that
you are using a group-value function.
The most common group-value functions include Avg, Count, Max, Median, Min, Stdev,
Sum, Var, ApplyAgg, and so on. First, Last, IRR, and NPV functions also belong to this
category, but they have an additional sort by feature (for more information, see Common
parameters, page 32). Sort by specifies the order that the values returned by an expression
will appear on a report. (For more information on the SortBy parameter, see BreakBy and
SortBy parameters, page 32.)
Group-value functions can be used to create simple metrics, nested metrics, and compound
metrics, as well as in the calculation of subtotals. The following examples illustrate their use.
Example 1: Average
Avg([Employee Age])
In this example, the group-value function Avg operates on the argument Employee Age,
which is a list of the following elements: 27, 35, 32, 47, 43, 40, 30. The function reduces the
seven elements of the input value to a single output value of 36. For more information on the
Avg function, see Avg (average), page 93.
Example 2: Median
Median([Employee Age])
The only difference between Example 2 and Example 1 above is the fact that the groupvalue function, Median, is used, instead of Avg. Again, the function reduces the seven
elements of the input value to a single output value of 35. For more information, see Median,
page 105.
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OLAP (Relative) functions
Online Analytical Processing (OLAP) functions are also referred to as Relative functions
because each element in a list of values is related to and dependent on one or more other
elements in the list, and the positions of the elements within the list determine how
computation is performed.
An OLAP function takes multiple elements from a list and returns a new list of elements.
Unlike group-value functions, though, the number of elements in the input list and the
number of elements in the output list remains the same. Unlike single-value functions, the
computation depends upon the conditions set by the BreakBy parameter that defines when
the calculation restarts and the SortBy parameter that defines how the list of values is sorted
(see Common parameters, page 32).
OLAP functions include Rank, all the functions with Moving as the prefix of the name (for
example, MovingDifference, MovingMin, MovingStdev, and so on), all the functions
with Running as the prefix (for example, RunningAvg, RunningCount, RunningSum,
and so on), and all the NTile functions (such as NTile, NTileSize, NTileValue, and
NTileValueSize). ApplyOLAP also belongs to the OLAP category.
OLAP functions are only used in the creation of metrics. The following is an example.
Example: RunningSum
RunningSum <BreakBy={[Customer Region]}, SortBy=
([Customer State]) >(Revenue)
BreakBy refers to the attribute or hierarchy where calculations for an OLAP function restart.
To break by an attribute or hierarchy means to restart calculations that use OLAP, or
Relative, functions when the analytical engine reaches the next instance of the specified
attribute or hierarchy. Examples of OLAP functions include RunningStdevP, Rank, NTile,
and various expressions that calculate percent values. To break by an attribute or hierarchy
in an expression, you set the BreakBy parameter.
The RunningSum metric computes the sum of the revenue for each Customer Region by
adding the revenue for each Customer State to the revenue of the Customer States in the
rows above it and displaying the incremented total. The BreakBy Customer Region condition
causes calculations to begin again, however, when the next Customer Region is
encountered. (Notice in the figure below that the metrics Total Revenue and the Running
Sum for Arizona are equal because the calculation for Running Sum has restarted, since
Arizona is categorized in Southwest, a different Customer Region than Wyoming, the
previous Customer State on the report.) Because of the SortBy Customer State condition,
the Customer States are listed in ascending (alphabetical) order, as shown in the report
excerpt below.
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Comparison operators
Comparison operators allow you to compare values. Using these operators, you can
compare single values or lists of values, or compare a list to a threshold value.
Comparison operators include < (less than), > (greater than), = (is equal to), Between,
Contains, Ends with, ApplyComparison, and so on. They are only used to create filters,
which limit report data to a subset based on your need.
Example: > (Greater than)
Revenue > 500000
In this example, the filter limits the states in your yearly income report to those with accrued
revenue greater than $500,000.
Logical operators
Logical operators provide basic comparisons and return TRUE or FALSE values based on
the evaluation of the formula. For numeric values, 0 is treated as FALSE, and 1 is treated as
TRUE. These operators provide a means to combine data evaluations and comparison
operators into complex expressions. These expressions, in turn, can answer questions such
as “Which of our regions produced revenue that exceeded a ‘success’ threshold?”
Logical operators include And, Or, Not, and ApplyLogic, all of which can only be used to build
filters where criteria are provided for the inclusion and exclusion of data from a report display
or metric calculation.
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Example: And
((Revenue - Cost) > 50000) And [Sell-through Percentage]
> 25
Built for the attribute State, this filter limits report data to those states where Profit (defined as
Revenue - Cost) is greater than $50,000 and the Sell-through Percentage is greater
than 25%.
Apply (Pass-through) functions
The terms Apply functions and Pass-through functions are interchangeable. They both
denote functions in MicroStrategy that provide access to functions or syntactic constructs
that are not standard in MicroStrategy but are provided by various Relational Database
Management System (RDBMS) platforms. The name “Pass-through” derives from the fact
that MicroStrategy passes information to a database which then uses its own functions.
(Using the native functionality of your RDBMS via Pass-through functions requires that you
know the syntax of your particular RDBMS. That syntax is beyond the scope of this book and
will vary from RDBMS to RDBMS.) RDBMS functions, while necessary, must be used with
care, since they always bypass MicroStrategy’s parsers and validators.
There are five predefined Apply functions that can be used to replace regular or predefined
functions of the same type. The functions are as follows:
•
ApplySimple: These functions are used where simple (e.g., arithmetic) operators can be
used.
•
ApplyAgg: These functions are used where aggregate functions (e.g., Sum) can be
used.
•
ApplyRelative: These functions are used where Online Analytical Processing (OLAP)
functions (e.g., Rank) can be used.
•
ApplyComparison: These functions are used where comparison operators (e.g., >, =,
Like and In) can be used.
•
ApplyLogic: These functions are used where logical operators (e.g., AND, OR, and
NOT) can be used.
With Apply functions, project designers can customize expressions in the Attribute, Filter and
Metric editors to utilize RDBMS functions that are not provided by MicroStrategy.
MicroStrategy strongly advises against using Apply functions when standard MicroStrategy
functions can be used to achieve the same goal, because using RDBMS functions effectively
bypasses the validations and other benefits of MicroStrategy products. Using Apply functions
is recommended only when corresponding functionality does not exist in MicroStrategy.
When you need to use an Apply function, MicroStrategy encourages you to submit an
enhancement request for inclusion of the desired feature in a future product release.
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Example: ApplyComparison used to check a prompted date
In this example, a table in your data warehouse contains the columns Item, Effec Date, and
Term Date (as well as Revenue), as shown below:
Item
Effec Date
Term Date
Revenue
Blouse
06/01/2007
07/30/2007
1000
Jeans
05/30/2007
06/17/2007
500
Gloves
10/01/2007
10/25/2007
150
Leather Shoes
06/15/2007
06/22/2007
750
Winter Hat
11/01/2007
11/08/2007
900
Winter Boots
12/01/2007
12/15/2007
2200
Each row in the table corresponds to an item that was on sale during the time between Effec
Date and Term Date. Your objective is to generate a report that lists all items (and an
associated metric that you choose) that were on sale on a particular date your user chooses
at run time. To generate this report, first create a value prompt named Test Date that allows
the user to input a date. Next, using that prompt, create a report filter using the Custom
expression box located in the Advanced Qualification pane of the Filter Editor, as shown
below.
Even though the filter is validated when you click Validate, MicroStrategy returns an error
when the report is executed. The error results from the fact that you are supplying the SQL
engine with two attributes and a value prompt, while MicroStrategy is expecting to compare
an attribute to the attributes Effec Date and Term Date. In effect, you have a “type mismatch”
problem.
In this case, you can use an Apply function. Instead of having MicroStrategy test the date
value prompt, you instruct your database to perform the test. It is important to remember that
you have chosen to use an Apply function only because MicroStrategy does not have a built-
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in function to accomplish your task. If an appropriate MicroStrategy function existed, you
would have used it instead of an RDBMS function, because the latter does not offer the
validating features that MicroStrategy does. (To use Apply functions, you must know the
syntax of the corresponding function or operation in the RDBMS you are using.)
To test the date prompt, use a custom expression to pass three values to the database for
comparison: the value prompt Test Date, the attribute Effec Date, and the attribute Term
Date. All three of these values are passed to the database using placeholders in the form of
#n, where n is a positive integer that increases by 1 for each successive item being passed,
starting with 0. The first value passed is referred to as #0, the second is #1, the third #2, and
so on. The Custom expression in the Advanced Qualification pane of the Filter Editor
depicted below shows the syntax needed for this example:
Notice that the syntax is nested. The outer portion of the expression contains the
MicroStrategy function ApplyComparison, as well as the MicroStrategy prompt Test Date
and the attributes Effec Date and Term Date.
The inner portion of the syntax, which is contained within double quotes, is the database
operation #0 between #1 and #2. Code that is passed to the database using an Apply
function is always enclosed in quotes, and the arguments that are passed with that code are
written as placeholders in the form of #n, with the specific forms of the passed attributes
specified by the characters after the “@” sign. In this example, [Effec Date]@ID
specifies that MicroStrategy pass the ID form of the Effec Attribute instead of the DESC or
any other form that may exist in the database. At run time, #0, #1, and #2 are replaced by
Test Date, Effec Date, and Term Date, respectively, so that the database effectively receives
the following syntax:
Test Date between Effec Date and Term Date
If the user chooses 06/16/07 as the value of Test Date at run time, the RDBMS reads the
table row by row to see if the date falls between Effec Date and Term Date. Whenever
06/16/07 falls between Effec Date and Term Date on a particular row, the item on that row is
returned in the result set. In this example, Blouse, Jeans, and Leather Shoes are returned.
(You can verify this result by looking at the data warehouse table shown in the beginning of
this section.) If your report is set up with Item as a row attribute, those three items appear on
your report, indicating that they (and only they) were on sale on 06/16/07.
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For additional information about Apply functions, see Apply (Pass-Through) functions, page
131. The syntax of each Apply function as well as examples appear in the sections that
immediately follow.
Example: Test whether Hire Date is in the current year
Your HR department requires a list of employees that have been hired during the current
calendar year. The following custom expression uses the ApplySimple function to test
whether the year of Hire Date is the same as the current year:
ApplySimple ( "datepart(yy, #0)", [Hire Date]@ID) =
ApplySimple ( "datepart(yy, getdate())", [Hire
Date]@ID)
Each piece of the custom expression is explained below. More detailed information on Apply
functions in general can be found in Apply (Pass-through) functions, page 26. More
information on ApplySimple functions, specifically, is found in ApplySimple, page 134.
•
The datepart function extracts a specified part of a given date. The first datepart
function extracts the year (as directed by yy) from the ID attribute form of the Hire Date
attribute. The ID attribute form—as opposed to the DESC or any other attribute form—is
specified by @ID.
•
The placeholder, #0, stands for the argument [Hire Date]@ID that is passed to your
RDBMS. (Apply functions use your database’s computational capabilities instead of
those of MicroStrategy.)
•
The second datepart function extracts the year (as instructed by yy) from the current,
or system, date. The system date is obtained via the RDBMS function getdate().
•
Your RDBMS extracts the year from both the Hire Date and the system date, with
MicroStrategy passing information to it. The container that hands the necessary function
to your RDBMS is an Apply function, ApplySimple. In other words, ApplySimple acts as
an interface between you and the database, and when the RDBMS returns both year
values, they are compared with the = operator. If the year of a particular Hire Date
element is the same as the year of the system date, the custom expression statement
evaluates as true and that Hire Date attribute element is returned in the result set of your
report. If the year of a particular Hire Date element is different than the year of the
system date, the custom expression statement evaluates as false, and that Hire Date
attribute element is filtered out of the report.
The attribute Hire Date is enclosed in brackets. Any time you type an attribute whose
name contains one or more spaces, the attribute must be enclosed in brackets. (The use of
brackets around compound object names is standard for many objects in MicroStrategy and
is not restricted to custom expressions and Apply functions.)
The above example used an Apply function, ApplySimple. The next example uses
ApplyComparison.
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Example: Customer City = Call Center using ApplyComparison
You need a list of customers who live in the same city as one of your call centers. While it is
possible to generate this report with a custom expression that does not use an Apply
function, this example uses an ApplyComparison function to demonstrate Apply functionality
within the custom expression. (For steps to create this report without the use of an Apply
function, see the Attribute-to-attribute qualifications section of the Advanced Filters chapter
of the Advanced Reporting Guide.)
The custom expression used here evaluates whether one attribute is exactly the same as
another:
ApplyComparison (“#0 like #1”,
[Customer City]@DESC, [Call Center]@DESC)
Each piece of the custom expression is explained below:
•
The ApplyComparison function is used with RDBMS comparison operators, such as
the like operator used in this example.
•
#0 like #1 is the actual comparison, comparing the first argument, #0, with the
second argument, #1. Remember that this comparison is done by your RDBMS—not by
MicroStrategy.
•
[Customer City]@DESC sets the first argument passed to your RDBMS as the
description form of the Customer City attribute, while [Call Center]@DESC sets
the second argument passed to your RDBMS as the description form of the Call Center
attribute.
The attributes Customer City and Call Center are enclosed in brackets. Any time you
type an attribute whose name contains one or more spaces, the attribute must be enclosed in
square brackets. (The use of brackets around compound object names is standard for many
objects in MicroStrategy and is not restricted to custom expressions and Apply functions.)
Placing a filter that uses the custom expression above on a report that lists the Customer
City, Customer, and Call Center attributes yields the results below. (Only a portion of the
report is shown. Also, the Revenue metric has been added.)
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Notice that the custom expression qualification filter accomplished the goal of returning only
data that satisfies the criterion that the Customer City attribute is the same as the Call
Center attribute.
Function parameters
Parameters determine how functions perform calculations. Any function, whether standard
or user-defined, can have parameters, which are contained within angle brackets <> in an
expression. If more than one parameter is used, they are separated by commas.
Function parameter notations are only displayed in the Developer interface if the parameter
settings are changed from the default and you have set your View option to Show Function
Parameters.
You define parameters in the Function Name Parameters dialog box, which displays the
tabs Parameters, Break By, and Sort By. For steps on how to access this dialog box and
how to set function parameters with the Insert Function Wizard, see Accessing and
modifying function parameters, page 33.
Since every function object has parameters, the Parameters tab is always displayed. If a
function has additional parameters, such as BreakBy and SortBy, the related tabs are
displayed accordingly. The following subsections discuss the three tabs in more detail:
•
Common parameters, page 32
•
BreakBy and SortBy parameters, page 32
The details of each function are covered in this guide, including a listing and description of
each parameter that is available for the function. All parameters are listed within angle
brackets <> as part of the function syntax. To review the details on the parameters available
for each function, review the functions provided in:
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•
Chapter 2, Standard Functions
•
Chapter 3, Operators
•
Chapter 4, Plug-In Package Functions
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Common parameters
There are three common Parameter settings for a function object:
•
Distinct: is a TRUE/FALSE parameter that allows you to use all values or only the
unique values in the calculation.
•
Fact ID: forces the calculation to take place on a fact table containing the Fact_ID.
•
NULL: is a TRUE/FALSE parameter that determines if the NULL value can be used in
the calculation.
Not all these settings apply to every function. In general, all group-value functions display the
FactID Parameter setting. OLAP functions do not have any of these Parameter settings, but
they may have BreakBy or SortBy parameters.
BreakBy and SortBy parameters
In addition to the Parameters settings, many functions have BreakBy or SortBy parameters,
each of which has its own individual settings:
•
BreakBy: The logical level where the calculation of values for an expression restarts.
To break by an attribute or hierarchy means to restart counting values for expressions
that use relative functions. Examples of relative functions are RunningStdevP, Rank,
NTile, and expressions that calculate rank or percent values. The break by level must at
the same level of aggregation or a higher level of aggregation used for the expression
itself.
For example, in the report shown below the Rank by Value metric ranks the revenue
values. The Rank function for this metric uses a BreakBy of the Customer Region
attribute, which means the rank calculation is restarted for each customer region. This
ensures that the revenue is ranked by customer region, rather than ranking all of the
revenues together across customer regions. While ranking all the revenues across
customer regions can be useful, this report uses the BreakBy parameter to focus on
revenue comparisons within each customer region.
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•
SortBy: The order of the return values of an expression in relation to the order of the
value or metadata object given. A sort by includes whether to sort in ascending or
descending order, and which metadata object to sort by. Sort by may also be performed
on the value of the subexpression, which is the input argument.
For example, in the report shown below the FirstInRange metric returns the first profit
value in a list of profit values. The FirstInRange function for this metric uses a sort by
of the Customer State attribute, which means the first value for each customer state is
returned.
OLAP functions often include BreakBy and SortBy parameters. For example, Rank has a
BreakBy parameter, and MovingAvg has a SortBy parameter.
A few group-value functions (First, Last, IRR, and NPV) are also defined by the SortBy
parameter. The First and Last functions are used effectively to calculate subtotals (see
Subtotal expressions, page 51).
For example, an inventory report lists the on-hand supply for each day. The report subtotals
are the last day’s inventory. Creating a user-defined subtotal that uses the Last function
provides the last day inventory subtotal. If the SortBy parameters of the function are not set
to sort by Day, the function may not provide the correct answer.
Accessing and modifying function parameters
MicroStrategy Developer provides different methods for accessing and modifying function
parameters. You can access and modify function parameters from the following interfaces:
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•
Insert Function Wizard: This interface is used to help guide you in the initial
inclusion of a function in an expression for a MicroStrategy object. The Insert Function
Wizard can be used to build a function and add it to an expression for metrics, attributes,
facts, subtotals, and transformations. For more information, see Adding functions to
expressions with the Insert Function Wizard, page 53.
•
Function Name Parameters dialog box: This interface is used to modify the
parameters of a function that has been added to an object expression and validated.
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To access and modify function parameters with the Insert Function
Wizard
1
Open an object editor for a MicroStrategy object that can include functions in its
expression.
For steps to access the Insert Function Wizard from the different object editors, see the
MicroStrategy online help and search for “Steps to access the Insert Function Wizard”. You
can also see the section Using functions in expressions, page 37 below that discusses the
different types of expressions in MicroStrategy.
2
Click the Insert Function button, labeled as f(x) on the expression toolbar.
The Insert Function Wizard opens.
3
Use the Next and Back buttons to step through the pages of the Insert Function
Wizard. Each page allows you to modify different function parameters.
The pages that you see depend on the function that you select.
To access and modify function parameters with the Function Name
Parameters dialog box
This functionality is available in any editor in Developer where functions are used in
expressions. For more information, see the following sections:
•
Metric expressions, page 38
•
Attribute form expressions, page 44
•
Custom Group expressions, page 47
•
Fact expressions, page 48
•
Filter expressions, page 50
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1
•
Subtotal expressions, page 51
•
Transformation expressions, page 52
Insert a function into an object expression and validate the expression.
You can insert a function by writing the name of the function and all required parameters into
the expression. You can also insert a function using the Insert Function Wizard (see the
procedure To build an expression using the Insert Function Wizard, page 53).
2
Highlight the function name, for example RunningSum, within a validated expression in
the expression box.
3
Right-click the function name and select function name parameters, as
demonstrated in the image below.
The Function Name Parameters dialog box opens. All parameter tabs for the selected
function are available, and settings editable, from this dialog. This example uses the
RunningSum function.
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Examples of function parameter effects
The following examples briefly illustrate the impact of parameters on function execution.
Example 1: Distinct parameter
Count<Distinct=True>(Order)
In this metric, you modify the default parameter setting to Distinct=True and retrieve a count
of only the unique Orders.
Example 2: RunningSum
RunningSum<BreakBy={Quarter}, SortBy=(Region)>(Revenue)
This metric is defined to display the running sum of revenue on a quarterly basis, sorted by
region in ascending order. For another RunningSum example and its report, see OLAP
(Relative) functions, page 24.
Arguments
Arguments are the input data used in the calculation of a function. Arguments can be
numbers, text, or logical values (such as TRUE or FALSE) as well as constants (such as 1,
2, 3, or NULL). They can be lists of values or variables referencing lists of values.
Arguments in MicroStrategy are most often references to lists of values. In function syntax,
the arguments are enclosed in parentheses (). If the argument is a reference to a
MicroStrategy object, and the object name is alphanumeric or contains multiple words, it is
also contained in brackets [ ]. Depending on the function selected and the object being
created, in a MicroStrategy environment the input could comprise one or more of the
following objects:
•
Attributes: Attributes are most often used to group fact data. They are included in
reports to define the level of detail. Typically non-numeric, some common examples of
attributes are Year, Category, and Region.
•
Facts: Facts are the most commonly used input for metrics. They are numerical lists
obtained from specific columns in a fact table. Examples of facts include Units Sold,
Units Received, and Discount.
•
Metrics: Metrics represent calculations performed on data and can themselves be
used as input for further calculation by a function. Examples of metrics include Percent
Growth, Profit Margin, and Sell-through Percentage.
•
Columns: Column data is used when creating attribute form expressions and fact
expressions. The expressions for these objects define how column data is retrieved from
the warehouse. Examples of columns include TOT_DOLLAR_SALES, TOT_COST, and
CUST_CITY_ID.
For an in-depth discussion of attributes, facts, and metrics,see the Advanced Reporting
Guide.
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Using prompts for arguments
Prompts can be used to provide the value for an argument in a function, which allows a user
to determine part of the function definition when a report is executed. Arguments that expect
a single value are the most common arguments to use prompts on. For example, the function
NTileSize (see NTileSize, page 204) has the following syntax:
NTileSize <Ascending, BreakBy> (Argument, Size)
The two arguments for this syntax are:
•
Argument is a metric representing a list of values to be distributed in buckets.
•
Size is a positive integer that designates the number of elements per bucket.
The argument Size is a good candidate to use a prompt for, since it expects a single value.
Using a prompt to provide the value for Size allows a user to determine how many elements
should be included in each NTile bucket. This is a more flexible reporting solution than
defining a static value for the argument that is always used for the calculation.
To include a prompt in a function expression, you can use the following syntax:
?[Prompt Name]
The Prompt Name is the name of the prompt object. For example, you can have the
following definition:
NTileSize([Total Revenue], ?[NTileValue Prompt])
In the syntax shown above, NTileValue Prompt is the name of a value prompt that
supplies the size for the NTileSize function.
When prompts are created, you can choose whether answering the prompt is optional or
required. Since arguments are required for a function to work properly, it is a good practice to
define prompt answers as required if the prompt is going to be used in a function expression.
Many of the financial functions (see Financial functions, page 257) use arguments that
expect a single value, and thus are good candidates for using functions to provide their
values. For example, the function Coupdays (see Coupdays (coupon period, number of
days with settlement), page 259) includes a Frequency argument which can accept the
value of 1, 2, or 4 to determine the number of coupon payments per year. You could use a
value prompt for the Frequency argument to prompt the user to enter the frequency of the
coupon payments.
Using functions in expressions
Functions are the basis for many MicroStrategy objects. Some of the objects they are used
to create includes:
37
•
Metric expressions
•
Attribute form expressions
•
Consolidation expressions
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•
Custom group expressions
•
Fact expressions
•
Filter expressions
•
Subtotal expressions
•
Transformation expressions
•
Derived elements
•
Derived attributes
This section explores the various roles of functions for each type of object and how to access
the expression builder in each case.
Metric expressions
Metrics are MicroStrategy objects that represent calculations performed on data. You can
define metrics by using the available functions to analyze your data and determine business
measures.
Formula and dimensionality are the two important components in a metric. While all metrics
have a formula, not all of them have dimensionality.
•
Formula: Is a mathematical expression using one or more functions, applied to the
data to be used in the calculation (facts, attributes, constants, or metrics). In SQL, the
formula commonly becomes part of the SELECT clause of the SQL command.
You can re-use the same formula in multiple metric definitions. This type of formula is
called a base formula, which can contain arithmetic operators, attributes, facts, group
functions, and non-group functions. A base formula does not have dimensionality (see
below). For more information on base formulas, see the Basic Reporting Guide.
•
Dimensionality: determines the attribute level at which a metric is calculated. After
deciding on the target (the attribute), in dimensionality you can further define filtering and
grouping involved in the metric. All metrics, by default, calculate at the report level.
Other optional components of a metric include condition (filter) and transformation. For the
purposes of this book, we only discuss formula and dimensionality related to the use of
functions. For information on all metric components, including additional information and
examples of level metrics, conditional metrics, and transformation metrics, see the Advanced
Reporting Guide.
There are two types of metrics:
•
Simple metric: has a formula and dimensionality (level). It can stand alone or be used as
a building block for a compound metric. A simple metric must use at least one groupvalue function, such as Sum or Avg. It can also contain a non-group function or
arithmetic operator, in addition to the required group function, for example, Sum
(Revenue - Cost){~+}; however, the outermost formula must be a group function.
•
Compound metric: is a combination of expressions that, through the use of functions,
are themselves metrics. In other words, a compound metric is made of more than one
complete metric. Any metric that is not a simple metric is a compound metric by default.
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For example, all arithmetic functions that are used as the root to connect two metrics
yield compound metrics.
A compound metric cannot have dimensionality placed on the entire metric, although
dimensionality can be set separately on each of its component metrics.
A quick way to check whether a metric is simple or compound is through the Metric Editor,
where you can click the Subtotals/Aggregation tab and check if the Allow Smart Metric option
is enabled. If it is, then it is a compound metric; if not, it is a simple metric.
The following three types of functions can be used to build simple and compound metrics:
•
Single-value functions
•
Group-value functions
•
OLAP functions
The optional condition (filter) component of a metric can contain logical and comparison
operators. See the Filter Expressions subsection for details.
While the single-value and group-value functions are used to create both simple and
compound metrics under different circumstances (see examples to follow), the OLAP
functions always yield compound metrics, due to their unique characteristics (see OLAP
(Relative) functions, page 24).
Examples of dimensionality in metrics
As mentioned previously, all metrics have a formula, but not all metrics have dimensionality.
The following examples and diagrams illustrate the formation of simple and compound
metrics, as well as formula and dimensionality.
Example 1: Simple metric with dimensionality at the report level
Avg(Revenue) {~+}
In this example, the expression is Avg on the fact Revenue. Together they make up the base
formula. This simple aggregation metric has dimensionality, which is indicated by {~+},
meaning that the metric is calculated at the lowest level on the report. For example, if a report
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contains revenue by year and month, the numbers are calculated to reflect monthly sales
data. If month was removed from the report, the metric would automatically be calculated at
the new report level, which would reflect yearly sales data.
All group-value functions are aggregation functions, which, when used alone, yield simple
metrics.
Example 2: Simple metric with dimensionality at Year level
Sum(Cost) {~ +,year +}
In this example, the expression is Sum on the fact Cost. Together they make up the base
formula. This simple aggregation metric has dimensionality. However, unlike in Example 1,
dimensionality is set at the level of the attribute Year, indicated by {Year +}. This means that
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if a report contains cost by year and month, the numbers are calculated to reflect yearly cost
data.
Example 3: Dimensionality for compound metrics
Avg(Revenue){~ +}/ Sum(Revenue) {~ +}
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Dimensionality of compound metrics is defined by the other metric definitions that are
combined to create a compound metric. In this example, the root Division (/) has two
children, Avg(Revenue) and Sum(Revenue), both of which are simple metrics themselves,
and each of which has its own dimensionality. The whole expression itself is a compound
metric because it uses two metrics and does not have its own dimensionality.
Accessing metric functions
You can access functions to create metrics in several ways. You can use the Metric Editor
when creating a new metric in a project. To create a derived metric, which is a metric based
on the existing data in a report, use the options from within a report to insert a new metric.
You can also create metrics in Command Manager. For more information on this method, see
the Advanced Reporting Guide.
Metric Editor
The Metric Editor is used to create new metrics and edit existing metrics in MicroStrategy.
The interface allows you to build metric expressions and validate them.
To access the Metric Editor using Developer
1
Log in to a project.
2
In the MicroStrategy Developer File menu, point to New and then select Metric. The
New Metric dialog box is displayed.
3
Choose a Metric template and click OK to proceed. The Metric Editor displays.
4
Build the metric expression, accessing the functions in one of the following ways:
•
Expand the Functions and Operators folder using the drop-down list or
shortcut list in the Object Browser pane. Then expand the Functions,
Operators, or Plug-In Packages folder to access the various categories of
functions and operators.
•
Click
(Insert Function) in the Definition pane. The Insert Function wizard
opens.
•
Type the function name and all required metric syntax directly in the Enter your
formula here box.
You can also edit existing metrics by using the Metric Editor. To access the editor, select the
appropriate metric in the folder list, object browser, or report view. Then right-click and select
Edit from the shortcut menu. The Metric Editor opens with the selected metric loaded.
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To access the Metric Editor using Web
1
Log in to a project.
2
From the MicroStrategy Web home page, click Create Metric.
3
In the pane below, select the function to use to calculate data in the metric. You can
narrow the list of functions displayed in the pane by doing one of the following:
4
•
To search for the function by name, type the function's name in the search field.
•
Choose a function category from the drop-down list, such as Math Functions or
Financial Functions. The pane is updated to include only the functions that belong to
the selected category.
The Function Editor opens, with different options available depending on the type of
function you selected above:
•
If you selected a grouping function, such as Sum, Average, First, or Maximum, you
are presented with options to define the metric's expression, as well as optional
components such as the level, condition, and transformation. Perform the following
steps:
a
Define the metric's expression by doing one of the following:
— To specify the expression by typing the name of an object, type the name of
the object in the Expression field. As you type, matching objects are
displayed in a drop-down list. You can click an object or continue to type.
You can type multiple objects, such as Revenue-Profit.
— To specify the expression by choosing an object, click the Browse icon.
The Select an Object dialog box opens. Navigate to and select an object, or
search for the object.
b
•
You can further define the metric by adding a level, condition, and
transformation.
If you selected a non-grouping function, such as data mining, date, OLAP, and
ranking functions, you are presented with options to define the input values (called
arguments) for the function, as well as any parameters you can use to determine the
behavior of the function. For example, the NTile function has two parameters,
Ascending and Tiles. Ascending controls whether the NTiles are ordered in
ascending or descending order, while Tiles sets the number of splits. To view a list of
the arguments and parameters for the function, click Details at the bottom of the
dialog box.
Perform the following steps:
43
a
For each argument listed, type a value or click the Browse icon to find the
metric, fact, prompt, or other compatible object to use as input values of the
function.
b
For each parameter listed, type a value or select the parameter value from the
drop-down
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5
Click Save to save your changes. For additional steps to define metrics using Web, see
the MicroStrategy Web Help .
Creating derived metrics
You create derived metrics based on objects already present in the report.
To create derived metrics
1
Log in to the project.
2
Begin to create the derived metric:
3
•
Using Developer, from the Insert menu in the Report Editor, select New Metric.
Or, right-click above a metric heading in the Report Grid, point to Insert, and then
select New Metric in the shortcut menu. The Input Metric Formula dialog box is
displayed.
•
Using MicroStrategy Web from the Data menu in the Report Editor, select Insert
New Metric. The Metric Editor is displayed.
Build a metric using the available report objects.
Attribute form expressions
Attribute forms are identifiers or descriptors of an attribute, such as ID, Name, and Address.
These units are part of an attribute, for example, Customer. Attribute forms are defined by at
least one expression, and these expressions act on column data and can contain functions.
The types of attribute form expressions are as follows:
•
Simple
•
Implicit
•
Derived
•
Heterogeneous
For more information on these types of attribute forms, see the Advanced Reporting Guide.
In the context of MicroStrategy functions, this book discusses derived expressions. A derived
expression can only use single-value functions, and arguments that are used in the
expression are columns. See the examples described below.
Example 1: Subtraction ( - )
(Year(CurrentDate()) - Year([HIRE_DATE]))
The attribute form Employee Experience is defined by the above expression using the simple
mathematical operator, subtraction.
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This example can be found in the MicroStrategy Tutorial project in the following folder:
MicroStrategy Tutorial/Schema Objects/Attributes/Geography.
Example 2: InitCap
InitCap([CUST_LAST_NAME])
Text data is typically in all upper-case or all lower-case letters. This example shows that for
the attribute form of customer’s last name, you can use the single-value function, InitCap, to
make the first letter capitalized and all other letters in lower case.
Example 3: ApplySimple
ApplySimple("Datediff(YY,#0,getdate())", [BIRTH_DATE])
The attribute form, Age, can be defined by using the single-value function, ApplySimple.
Note the following:
•
For all Apply functions, do not use a group for the attribute form expression. Use a
single form because form groups are ignored by the Analytical Engine. For example,
you cannot use Customer@Name, where Name is a form group defined as the
customer’s first name, middle name, and last name.
•
The syntax of apply functions is database-specific. For more information, see
Internal functions, page 130.
To access attribute form expressions
For a new attribute
1
From the MicroStrategy Developer File menu, point to New and then select Attribute.
The Attribute Editor opens three dialog boxes, if the cascading dialog box option is
enabled in the Developer Preference (as it is by default). The three dialog boxes are
New Attribute, Create New Attribute Form, and Create New Attribute Form Expression.
The Create New Attribute Form Expression dialog box is where functions and operators
are used.
2
Build your expression by using the functions in one of the following ways:
•
Click
(Insert Function) in the Definition pane. The Insert Function wizard
opens.
•
45
Type the function name and all required expression syntax directly in the Enter
your formula here box.
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For an existing attribute
1
To access the editor, select the appropriate attribute in the folder list, object browser, or
report view. Then right-click and select Edit from the shortcut menu. The Attribute
Editor opens.
2
Select the attribute form you wish to edit from the Attribute Forms pane and then click
Modify. The Modify Attribute Form dialog box displays.
To add a new form to the existing Attribute, click New . This automatically opens the New
Attribute Form Expression dialog box.
3
Select the expression you want to edit and click Modify.
To add a new expression to the existing form, click New . This automatically opens the New
Attribute Form Expression dialog box.
4
The Modify Attribute Form Expression dialog displays with the selected expression
loaded.
5
Create or edit the expression, accessing the functions in one of the following ways:
•
Click
(Insert Function) in the Definition pane. The Insert Function wizard
opens.
•
Type/edit the function name and all required expression syntax in the Enter your
formula here box.
Consolidation element expressions
Consolidations enable you to group attribute elements for use in a report, without changing
the structure of your metadata or your warehouse definition. The elements contained in a
consolidation are called consolidation elements (CEs). Only single-value functions can be
used in the definition of a consolidation element as well as calculations between
consolidation elements. A consolidation element expression defines how the attribute
elements are calculated.
Only the basic mathematical operators (+, -, *, /) can be used through MicroStrategy
Developer to define consolidation elements. Examples of consolidations, using these
operators, are as follows:
•
CE01 = {Region = North-East}
•
CE02 = {Region = Mid-Atlantic}
•
CE03 = ({CE01} – {CE02})/{CE02}
Other single-value functions can also be used for consolidation elements, but only through
the SDK.
The following examples demonstrate definitions using functions and calculations:
•
CE04 = Ln({Region = North-East})
•
CE05 = Ln({Region = Mid-Atlantic})
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•
CE06 = Abs({CE04} – {CE05})
For more information on consolidations, see the MicroStrategy Advanced Reporting Guide.
To access the consolidation element expressions
1
In the MicroStrategy Developer File menu, point to New and then select
Consolidation. The Consolidation Editor opens.
2
Double-click Click here to add new consolidation element.
3
The New Consolidation Element pane is enabled, in the lower right corner of the editor.
Build the expression for the new element.
You must drag and drop attributes into the Enter your expression here box. Only the
operators can be typed directly in the box.
Custom Group expressions
A custom group is an object that can be placed on a template and is made up of a collection
of elements called custom group elements. A custom group can group attribute elements in a
way that is not defined in the data warehouse. You can create relationships between the
attribute and the custom group. A custom group expression defines how the elements in the
custom group are calculated.
A custom group can organize attribute elements through:
•
Attribute qualification
•
Set qualification
•
A report
•
A filter
•
Banding
•
Advanced qualification
For more information on custom groups, see Custom Groups and Consolidations in the
MicroStrategy Advanced Reporting Guide.
When you define custom group elements through advanced qualification, you can use two
types of functions:
•
Logical functions
•
Comparison functions
See the following examples.
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Example 1: Subtraction ( - )
([Unit_Profit] - [Unit_Cost]) > 500
Example 2: And
([Units Sold] > 500) and ([Unit Profit] > 1000)
To access the custom group element expressions
1
In the MicroStrategy Developer File menu, point to New and then select Custom
Group. The Custom Group Editor opens.
2
Double-click Double-click here or drag an object from the object browser
to add a custom group element.
3
Provide a name for the new element and double-click Add Qualification.
4
In the Custom Group Options pane, select Add an Advanced qualification and
click OK.
5
The Advanced Qualification pane opens. This is where the expression is built. The
functions are accessed in one of the following ways:
•
Click
(Insert Function) in the Definition pane. The Insert Function wizard
opens.
•
Type the function name and all required expression syntax directly in the Enter
your formula here box.
Fact expressions
Facts are objects created by and shared between MicroStrategy users. They relate numeric
data values from the data warehouse to the MicroStrategy reporting environment. A fact
expression defines how a fact is calculated. The fact expression is part of the Fact Definition
component of a fact structure. You can use functions to create fact expressions, and
arguments that are used in the expressions are columns.
Facts can be defined as:
•
Implicit
•
Derived
•
Heterogeneous
For more information on facts and fact structure, see the MicroStrategy Project Design
Guide.
In the context of MicroStrategy functions, this book discusses derived fact expressions.
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Derived fact expressions can only use single-value functions, including simple arithmetic
operators (+, -, /, *). See the following two examples.
Example 1: Subtraction ( - )
([UNIT_PRICE] - [UNIT_COST])
The fact Unit Profit is defined using the table columns Unit_Price and Unit_Cost and a simple
mathematical operator, subtraction.
Example 2: Multiplication ( * )
([QTY_SOLD] * ([UNIT_PRICE] - DISCOUNT))
The fact Revenue is defined using three table columns and two arithmetic operators,
subtraction and multiplication.
Both examples can be found in the MicroStrategy Tutorial project in the following folder:
MicroStrategy Tutorial/Schema Objects/Facts.
To access fact expressions
For a new fact with an expression
1
In the MicroStrategy Developer File menu, point to New and then select Fact. The Fact
Editor opens two pages: Fact and Create New Fact Expression.
2
The Expression page is where the expression is built. The functions are accessed in one
of the following ways:
•
Click
(Insert Function) in the Definition pane. The Insert Function wizard
opens.
•
Type the function name and all required expression syntax directly in the Enter
your formula here box.
For an existing fact
49
1
Select an existing fact in the MicroStrategy Developer main screen. Then right-click and
select Edit from the shortcut menu. The Fact Editor displays with the selected fact
loaded.
2
Click New to add a new expression to the fact, or select an existing expression and click
Modify. The Create New Fact Expression or Modify Fact Expression page
displays.
3
Build an expression by using functions in one of the following ways:
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•
Click
(Insert Function) in the Definition pane. The Insert Function wizard
opens.
•
Type the function name and all required expression syntax directly in the Enter
your formula here box.
You can use either the Fact Editor or the Fact Creation Wizard to create facts; however, only
the Fact Editor allows you to use advanced expressions to define the fact.
Filter expressions
A filter specifies the conditions that data must meet to be included in the report results. In
SQL, a filter is specified after the WHERE clause. A filter can be a report object, that is, a
report filter, a report limit, or a view filter, that restricts the data returned or the display or view
of data on a report. For detailed information on report filters, see the Advanced Reporting
Guide .
A filter can also be a metric qualifier created using custom expressions employing functions.
It is used as the optional condition component of a metric. You can create this type of filters
by using the Advanced Qualification dialog box within the Filter Editor. Only the following
types of functions can be used in filter expressions:
•
Logical operators
•
Comparison operators
Single-value functions can be used at the sublevel in a filter expression, as long as the root
node is a logical or comparison function (see Example 1).
Example 1: Greater than ( > )
((Revenue - Cost) > “5000”)
This example uses a simple comparison operator to create a filter to limit the display of profit
to values greater than $5,000.
You can achieve the same result by using a set (metric) qualification on a compound metric
(such as the one in the example defined as Revenue - Cost). The custom filter
expression is used here for illustration purposes. It is up to you which method you prefer to
use.
Example 2: ApplyComparison
ApplyComparison("#0 between #1 and #2", ?[Value Prompt
Date], [Order Date]@ID, [Ship Date]@ID)
ApplyComparison is commonly used to create custom filters. In this example, the filter
compares a user-entered date to see if it is between the Order_Date and the Ship_Date.
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To access an advanced filter qualification
1
In the MicroStrategy Developer File menu, point to New and then select Filter. The
Filter Editor is displayed.
2
In the Filter Definition pane, double-click Double-click here to add a
qualification or drag an object from the Object Browser. The Filtering
Options pane is displayed.
3
In the Filtering Options pane, select the Add an Advanced qualification option.
Then click OK. The Advanced Qualification pane is displayed.
4
Select Custom Expression from the Option list. Then use the Custom Expression
box to build and validate your custom filter. Access the functions in one of the following
ways:
•
Expand the Functions and Operators folder using the drop-down list or folder
list. Then expand the Functions, Operators, or Plug-In Packages folder to
access the various categories of functions and operators.
•
Click
(Insert Function) in the Definition pane. The Insert Function Wizard
opens.
•
Type the function name and all required metric syntax directly in the Enter your
formula here box.
Subtotal expressions
Subtotals allow you to dynamically control the computation and display of report data within
desired groupings. Subtotals are applied to report metrics to calculate totals and for dynamic
aggregation.
The standard predefined subtotal functions, which are automatically available for use with
every metric and report, are simple aggregate functions that satisfy many subtotaling
requirements. If they do not answer your particular needs, you can create a user-defined
subtotal using the Subtotal Editor or through the SDK. User-defined subtotals allow you to
develop your own subtotals, using single-value functions or group-value functions.
Both predefined and user-defined subtotals can be applied to reports from the
Subtotals/Aggregation tab in the Metric Editor.
For more information on subtotals and the procedures for creating and applying them, see the
Reports chapter of the Advanced Reporting Guide or the MicroStrategy online help.
Example: Division ( / )
Sum(Sum(x*[Units Sold]){Year}/Sum([Units Sold]){Year}){}
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To access functions in the Subtotal Editor
1
Log in to a project.
2
In the MicroStrategy Developer File menu, point to New and then select Subtotal. The
Subtotal Editor displays.
3
The Subtotal Editor is very similar to the Metric Editor. To access the functions used to
create a new subtotal, you can do one of the following:
•
Expand the Functions and Operators folder using the drop-down list or
shortcut list in the Object Browser pane. Then expand the Functions, Operators
or Plug-In Packages folder to access the various categories of functions and
operators.
•
Click
(Insert Function) in the Definition pane. The Insert Function wizard
opens.
•
Type the function name and all required subtotal syntax directly in the Enter your
formula here box.
Transformation expressions
Transformations are schema objects that use business rules to compare values at different
time periods. A typical example of this type of analysis is a TY/LY comparison (This Year
versus Last Year).
Transformations are schema objects; therefore, you must have the appropriate privileges to
create or modify them.
There are two types of transformations: expression-based and table-based. Single-value
functions can be used in expression-based transformations, and specifically in the definition
of member expressions. These expressions define how (and from where) the information is
retrieved for the transformation of the specified attribute.
For more information on transformations and their components, see the Advanced Reporting
Guide or the MicroStrategy online help.
To access functions in transformations
1
In the MicroStrategy Developer File menu, point to New and then select
Transformation. The Transformation Editor is displayed. The Select Member
Attribute dialog box also opens.
2
Select the attribute on which to base the transformation. Then click OK. The Expression
Editor opens.
3
Build an expression for the transformation of the selected member attribute, accessing
functions in one of the following ways:
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•
Click
(Insert Function) in the Definition pane. The Insert Function wizard
opens.
•
Type the function name and all required expression syntax directly in the Enter
your formula here box.
Adding functions to expressions with the Insert
Function Wizard
The Insert Function Wizard is available wherever functions are used. It guides you through
the process of selecting a function, choosing the data on which the function acts, and setting
the available parameters.
To build an expression using the Insert Function Wizard
For detailed information on each page of the Insert Function Wizard, see the MicroStrategy
online help and search for “Using the Insert Function Wizard.”
1
Click the
Insert Function button to access the wizard. The Select Function
page of the Insert Function Wizard is displayed.
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2
Navigate through the folder structure to the function you want to use. Select the function
and click Next to continue. The Arguments page opens.
When a function is highlighted, its syntax and a short description are displayed in the lower
left corner of the window.
3
Include the arguments for your function by typing the values or names of the arguments
or by browsing for the arguments:
•
Type the appropriate values or name of the arguments into the available boxes.
•
Click
(the Browse button). The Open dialog box opens, which is a standard
Windows dialog box for file selection.
Select the argument on which the function acts. Click Open to select the argument
and return to the Insert Function Wizard.
4
Click Next when you have provided all arguments. The Parameters page is displayed (if
applicable).
For group-value metrics such as Count, Min, and Max, standard parameters (Distinct, NULL,
and FactID) display together on the only page available, called Parameters. The default for
FactID is (Nothing), meaning that the calculation searches for the input argument from the
lookup table. Otherwise, make a selection from the pull-down list to force the calculation to
look in another table.
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5
Set the values of the parameters.
6
Define the SortBy and BreakBy parameters in their separate windows, if applicable.
7
Click Finish. The expression built through the Insert Function Wizard displays in the
appropriate editor.
Parameter values are displayed only if you have set your view to Show Function
Parameters and have modified a parameter from its default setting.
How MicroStrategy processes functions
MicroStrategy Intelligence Server has an engine component, which comprises the following:
•
SQL Engine: Generates the SQL and associated logic for functions performed by the
database, and communicates with the Analytical Engine as necessary.
•
Query Engine: Sends the SQL generated by the SQL Engine to the data warehouse
for execution.
•
Analytical Engine: Extends the capability of the system beyond what the RDBMS
provides. For example, it performs complex calculations on a result set returned from the
data warehouse, such as statistical and financial functions, subtotal calculations on the
result set, metric calculations that are not or cannot be performed using SQL, such as
complex functions, and so on.
While this extends the capabilities of the RDBMS that you are using, calculations that
require the Analytical Engine can require additional system resources and processing
time. To determine if a function is supported by your RDBMS or will be evaluated by the
Analytical Engine, review the function support listed in Appendix A, MicroStrategy and
Database Support for Functions.
Types of function processing
Functions supported by the Intelligence Server are of three types:
•
Those that can be processed only by the Analytical Engine, such as finance functions.
If the Analytical Engine does not support a given function, a compound metric containing
the function cannot be smart metric enabled. This is because smart metrics change the
default order of metric evaluation and only the Analytical Engine can support such a
change.
•
Those that can be processed only by the database, such as date-and-time functions.
If a database platform does not support a given function that can only be processed by
the database, that function cannot be calculated. For a list of functions supported for
each certified database type, see Appendix A, MicroStrategy and Database Support for
Functions.
•
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If the database can perform the calculation, the SQL Engine sends the instructions to the
database; otherwise, the Analytical Engine processes the task.
How Intelligence Server uses functions
Recall that there are several categories of functions recognized by Intelligence Server.
These function types include group-value, single-value, and OLAP functions. Within these
categories are functions supported by only the Analytical Engine, only the database, or by
both the engine and the database. The SQL generated to process the request differs
depending on what processes the request. The SQL syntax also differs depending on the
database used, when the database supports the function.
The following subsections explore through examples of how the various uses of functions
result in different SQL syntax. All of these examples use the functions in the context of
metrics.
Most of these examples (except where noted) can be recreated using the objects in the
MicroStrategy Tutorial. Tutorial data is stored in a Microsoft® Access database.
Using group-value functions
The computation of group-value functions is done by either the Intelligence Server or the
database depending upon the function used and support available. The following examples
discuss how MicroStrategy performs the group-value computations by providing the SQL
syntax for specific situations.
Group-value functions in simple metrics
This subsection contains two examples that illustrate the processing of group-value
functions. Each example contains two reports, the first one showing the SQL for a function
supported by the database and the second one by the Analytical Engine.
Example 1: Sum(Revenue) vs. AvgDev(Revenue)
Consider a simple report, Report 1A, where the attribute Region is placed on the row axis
and a simple metric defined as M1A=Sum(Revenue){~} is on the column axis. This report
shows the sales for each region. The database, in this case Microsoft Access, supports the
function Sum. The following SQL is generated:
•
Report 1A (SQL Group-value function) - Microsoft Access
select a12.[REGION_ID] AS REGION_ID,
max(a13.[REGION_NAME]) AS REGION_NAME,
sum(a11.[TOT_DOLLAR_SALES]) AS WJXBFS1
from [CITY_CTR_SLS] a11,
[LU_CALL_CTR] a12,
[LU_REGION] a13
where a11.[CALL_CTR_ID] = a12.[CALL_CTR_ID] and
a12.[REGION_ID] = a13.[REGION_ID]
group by a12.[REGION_ID]
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Now, consider Report 1B, again with the same attribute, Region, on the row axis, but with a
different simple metric defined as M1B=AvgDev(Revenue){~} on the column axis. This
report shows how revenue data varies from its mean for each region. The database does not
support the function Average Deviation; therefore, the computation is performed by the
Analytical Engine. The following SQL is generated:
•
Report 1B (MicroStrategy Group-value function)
select a11.[CALL_CTR_ID] AS CALL_CTR_ID,
a11.[CUST_CITY_ID] AS CUST_CITY_ID,
a12.[REGION_ID] AS REGION_ID,
a13.[REGION_NAME] AS REGION_NAME,
a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [CITY_CTR_SLS] a11,
[LU_CALL_CTR] a12,
[LU_REGION] a13
where a11.[CALL_CTR_ID] = a12.[CALL_CTR_ID] and
a12.[REGION_ID] = a13.[REGION_ID]
[An analytical SQL]
In Report 1B, Intelligence Server performs the following steps:
•
It retrieves all fact data from the warehouse. [TOTAL_ DOLLAR_SALES] is the column
alias used for the fact (Revenue) in the temporary table during SQL generation.
•
It uses the result set (that is held in memory) to compute the metric, namely AvgDev
([TOTAL_ DOLLAR_SALES]){~} for each region.
The notation [An analytical SQL] indicates that the computation is taking place in the
Analytical Engine.
•
It displays the final result.
In the previous examples, the dimensionality of both metrics is defined as {~}, which means
that they both are calculated at the report level of Region, since the attribute Region is on the
reports.
The next example explains how Intelligence Server processes dimensionality. When metric
dimensionality is defined, the Analytical Engine can insert records back into the temporary
database structures after the function calculation is performed so that dimensionality can be
applied.
Example 2: Sum(Revenue) {~, Country} vs. AvgDev(Revenue) {~, Country}
Now use the same report template as in Example 1, but add a dimensionality to each metric
for comparison. Use Sum(Revenue){~,Country} for Report 2A and AvgDev
(Revenue){~,Country} for Report 2B. Notice that attribute Country is a parent of
Region, and the relationship is one to many. In Report 2A, the Sum function is supported by
the database; in Report 2B, the AvgDev function is supported by Intelligence Server. The
SQL generated for both reports is as follows:
Report 2A (SQL Group-value function) - Microsoft Access
create table ZZT1Y03009ZMD000 (
COUNTRY_ID BYTE,
WJXBFS1 DOUBLE)
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insert into ZZT1Y03009ZMD000
select a12.[COUNTRY_ID] AS COUNTRY_ID,
sum(a11.[TOT_DOLLAR_SALES]) AS WJXBFS1
from [CITY_CTR_SLS] a11,
[LU_CALL_CTR] a12
where a11.[CALL_CTR_ID] = a12.[CALL_CTR_ID]
group by a12.[COUNTRY_ID]
select a11.[REGION_ID] AS REGION_ID,
a11.[REGION_NAME] AS REGION_NAME,
pa1.[WJXBFS1] AS WJXBFS1
from [ZZT1Y03009ZMD000] pa1,
[LU_REGION] a11
where pa1.[COUNTRY_ID] = a11.[COUNTRY_ID]
drop table ZZT1Y03009ZMD000
The first pass of SQL creates a temporary table to hold the data. The second pass computes
the metric at the Country level, while the third pass joins with attribute Region since the result
of the aggregation has to be displayed for each region. The final pass drops the temporary
table.
Report 2B (MicroStrategy Group-value function)
select a11.[CUST_CITY_ID] AS CUST_CITY_ID,
a12.[COUNTRY_ID] AS COUNTRY_ID,
a11.[CALL_CTR_ID] AS CALL_CTR_ID,
a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [CITY_CTR_SLS] a11,
[LU_CALL_CTR] a12
where a11.[CALL_CTR_ID] = a12.[CALL_CTR_ID]
create table ZZMD00 (
COUNTRY_ID BYTE,
WJXBFS1 FLOAT)
[An analytical SQL]
insert into ZZMD00 values ([DummyInsertValue])
select a11.[REGION_ID] AS REGION_ID,
a11.[REGION_NAME] AS REGION_NAME,
pa1.[WJXBFS1] AS WJXBFS1
from [ZZMD00] pa1,
[LU_REGION] a11
where pa1.[COUNTRY_ID] = a11.[COUNTRY_ID]
drop table ZZMD00
In Report 2B, an Analytical SQL pass is necessary to compute AvgDev since it is not a
database supported group-value function. In the next pass, the results of the calculation are
inserted back into the temporary database structures. The last SQL pass is the same as
Report 2A, since it is used to display the result for all regions.
Using single-value functions
The key to understanding the computation of a single-value function is to identify the way it is
used. The next two subsections provide examples of the two uses of single-value functions
and how they are processed.
The first example shows a single-value function applied before a group-value function. This
is referred to as transforming a fact. The second example shows a single-value function
applied after the group-value function. This is referred to as a compound metric.
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Each example contains two reports, the first one showing the SQL syntax when calculations
are processed by the database and the second one when calculations are processed by the
Intelligence Server.
The use of transform in this context refers to retrieving a fact in a different form. For example,
you can obtain the absolute value for a fact or the natural logarithm, but the fact is the same.
This differs from a transformation where the data retrieved is different when a transformation
is applied, for example, last year’s revenue instead of this year’s revenue.
Transforming a fact into another fact
In this case, the fact Revenue is transformed into another fact, namely the natural logarithm
values defined as Ln(Revenue) or the truncated revenue values defined as Trunc
(Revenue).
The following example describes this type of usage and displays the SQL that is generated.
Consider the two metrics defined as follows:
•
M3A = Sum(Ln(Revenue)){~+}
•
M3B = Sum(Trunc(Revenue)){~+}
Put M3A with Region on the template. The database, in this case, SQL Server 2000,
supports the function Ln. The following SQL is generated:
Report 3A (SQL single-value before group-value function) - SQL Server 2000
Pass0 - Duration: 0:00:40.45
select a13.REGION_ID REGION_ID,
max(a14.REGION_NAME) REGION_NAME,
sum(LOG(a11.ORDER_AMT)) WJXBFS1
from ORDER_FACT a11
join LU_EMPLOYEE a12
on (a11.EMP_ID = a12.EMP_ID)
join LU_CALL_CTR a13
on (a12.CALL_CTR_ID = a13.CALL_CTR_ID)
join LU_REGION a14
on (a13.REGION_ID = a14.REGION_ID)
group by a13.REGION_ID
Based on the SQL, this is what happened:
1
The Ln function is applied to the fact Revenue, which is defined in the warehouse by the
column ORDER_AMT.
The Ln function uses the syntax LOG in SQL Server 2000.
2
Then, the Sum function is performed on the new fact, namely Ln(Revenue).
Now, put M3B with Region on the template, the following SQL is generated:
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Report 3B (MicroStrategy single-value before group-value function)
select a11.[CALL_CTR_ID] AS CALL_CTR_ID,
a11.[CUST_CITY_ID] AS CUST_CITY_ID,
a12.[REGION_ID] AS REGION_ID,
a13.[REGION_NAME] AS REGION_NAME,
a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [CITY_CTR_SLS] a11,
[LU_CALL_CTR] a12,
[LU_REGION] a13
where a11.[CALL_CTR_ID] = a12.[CALL_CTR_ID] and
a12.[REGION_ID] = a13.[REGION_ID]
[An Analytical SQL]
In this case, as noted by the text [An Analytical SQL], the functions Trunc and Sum
are computed by the Intelligence Server. Intelligence Server computes the new fact Trunc
([Dollar Sales]) first and then uses the Sum function to sum the new fact for each
region.
Calculating a compound metric
Single-value functions can be used to create compound metrics. Subtraction, addition,
division, and multiplication operators (–, +, /, *) are common examples of single-value
functions. See the following examples:
•
(Sum([Dollar Sales]){~}/Sum([Dollar Sales])
{~,Country})
•
(Sum([Dollar Sales]){~} + Sum([Freight]) {~})
The examples below use a compound metric and a metric that transforms a fact in the same
report. The examples illustrate the SQL generated when the function is supported by the
database and the Intelligence Server, respectively.
Consider the following metric definitions:
•
M4A = Ln(Sum(Revenue){~})
•
M4B = Trunc(Sum(Revenue){~})
Put metric M3A, from the previous example, and metric M4A together with attribute Region
on the template. The database, in this case SQL Server 2000, supports the Ln function. The
following SQL is generated:
Report 4A (SQL single-value before and after group-value functions) - SQL Server
2000
Pass0 - Duration: 0:00:02.58
select a13.REGION_ID REGION_ID,
max(a14.REGION_NAME) REGION_NAME,
sum(LOG(a11.ORDER_AMT)) WJXBFS1,
LOG(sum(a11.ORDER_AMT)) WJXBFS2
from ORDER_FACT a11
join LU_EMPLOYEE a12
on (a11.EMP_ID = a12.EMP_ID)
join LU_CALL_CTR a13
on (a12.CALL_CTR_ID = a13.CALL_CTR_ID)
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join LU_REGION a14
on (a13.REGION_ID = a14.REGION_ID)
group by a13.REGION_ID
To process metric M3A, the single-value function Ln is calculated before the group-value
function Sum, and for metric M4A, Ln is calculated after Sum.
The Ln function uses the syntax LOG in SQL Server 2000.
Similarly, if you put metrics M3B and M4B together with attribute Region on the template,
and the database does not support the Trunc function, then the following SQL is generated:
Pass0 - Duration: 0:00:12.67
select a11.ORDER_DATE DAY_DATE,
a11.EMP_ID EMP_ID,
a11.ORDER_ID ORDER_ID,
a13.REGION_ID REGION_ID,
a14.REGION_NAME REGION_NAME,
a11.ORDER_AMT WJXBFS1
from ORDER_FACT a11
join LU_EMPLOYEE a12
on (a11.EMP_ID = a12.EMP_ID)
join LU_CALL_CTR a13
on (a12.CALL_CTR_ID = a13.CALL_CTR_ID)
join LU_REGION a14
on (a13.REGION_ID = a14.REGION_ID)
Pass1 - Duration: 0:00:08.46
[An Analytical SQL]
The fact, Revenue, which is defined as column ORDER_AMT in the ORDER_FACT table,
is retrieved and used to compute both metrics.
Using OLAP functions
You can better understand how Intelligence Server computes OLAP functions by observing
several examples based on the following properties:
•
Window size
•
BreakBy
•
SortBy
•
NULL handling
•
Tie handling
There are two examples for each subsection, comparing the SQL syntax when the database
performs the OLAP function calculations with the one when the Intelligence Server performs
the computation.
Window Size in Moving functions
For this example, define metric M1A as Sum(Revenue){~+}. Then create an OLAP metric
called OM1, that is defined as
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MovingAvg <BreakBy = {Category}, SortBy = (Value)>
([M1A],5).
OM1 computes the moving average of M1A with a window size of 5. The OLAP metric is
computed after it is sorted by the value of M1A in ascending order. Moreover, the
computation restarts for every category.
Now, put the attributes Category and Item on the Row axis and the above metrics on the
Column axis. Run this report with an empty filter against a database that does not support
OLAP functions (in this case Microsoft Access), the following SQL is generated:
Report 5A (MicroStrategy OLAP function - window size) - Microsoft Access
Pass0 - Duration: 0:00:00.60
select a13.[CATEGORY_ID] AS CATEGORY_ID,
max(a14.[CATEGORY_DESC]) AS CATEGORY_DESC,
a11.[ITEM_ID] AS ITEM_ID,
max(a12.[ITEM_NAME]) AS ITEM_NAME,
sum(a11.[TOT_DOLLAR_SALES]) AS WJXBFS1
from [ITEM_MNTH_SLS] a11,
[LU_ITEM] a12,
[LU_SUBCATEG] a13,
[LU_CATEGORY] a14
where a11.[ITEM_ID] = a12.[ITEM_ID] and
a12.[SUBCAT_ID] = a13.[SUBCAT_ID] and
a13.[CATEGORY_ID] = a14.[CATEGORY_ID]
group by a13.[CATEGORY_ID],
a11.[ITEM_ID]
Pass1 - Duration: 0:00:00.01
[An Analytical SQL]
Alternatively, if you execute the above report against a database that does support OLAP
computations (in this case Oracle 9i), then the following SQL is generated:
Report 5B (DB OLAP function - window size) - Oracle 9i
Pass0 - Duration: 0:00:10.15
select a13.CATEGORY_ID CATEGORY_ID,
a14.CATEGORY_DESC CATEGORY_DESC,
a11.ITEM_ID ITEM_ID,
a12.ITEM_NAME ITEM_NAME,
sum((a11.QTY_SOLD * (a11.UNIT_PRICE - a11.DISCOUNT)))
WJXBFS1,
avg(sum((a11.QTY_SOLD * (a11.UNIT_PRICE a11.DISCOUNT)))) over(partition by a13.CATEGORY_ID
order by sum((a11.QTY_SOLD * (a11.UNIT_PRICE a11.DISCOUNT))) asc rows 4 preceding) WJXBFS2
from ORDER_DETAIL a11,
LU_ITEM a12,
LU_SUBCATEG a13,
LU_CATEGORY a14
where a11.ITEM_ID = a12.ITEM_ID and
a12.SUBCAT_ID = a13.SUBCAT_ID and
a13.CATEGORY_ID = a14.CATEGORY_ID
group by a13.CATEGORY_ID,
a14.CATEGORY_DESC,
a11.ITEM_ID,
a12.ITEM_NAME
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The following example shows how the Intelligence Server computes OLAP functions when
the database does not support OLAP functions. The Intelligence Server retrieves all
components: the input metric, BreakBy parameter, and SortBy parameter. In the above
examples, since the SortBy parameter setting is by Value, it is sufficient to bring back just the
input metric (WJXBFS1).
BreakBy in OLAP functions
In the Intelligence Server, the BreakBy parameter is only available at the attribute level. In
other words, you can start over certain computations of OLAP functions when a part of the
metric belongs to a different attribute element.
In the previous example (window size), there is an OLAP function with Category in the
BreakBy parameter. If the Analytical Engine computes the OLAP function, Intelligence
Server must select this attribute in the select clause. If the database computes the OLAP
function, then this attribute must also be in the partition by clause.
Attributes in the BreakBy parameter of an OLAP metric are always applied, meaning that they
are always in the Select clause, and if the database computes it, they are also in the
Partition by clause. This is true whether the attribute is on the template or not.
Run a report similar to the one for window size, deleting the attributes Category and Item
from the template and adding Subcategory. The results for metric M1A are completely
different from the previous report because if Category and Item are not on the template, then
the level of aggregation for metric M1A is replaced by Subcategory, which is on the template.
On the other hand, OLAP metric OM1 must still restart the calculation (break by) for each
Category and therefore remains in the appropriate Select and Partition by clauses.
Notice that in both the SQLs below, the Intelligence Server always selects Category even
though Category is not on the template.
For a database that does not support OLAP functions (in this case Microsoft Access), the
following SQL is generated:
Report 6A (MicroStrategy OLAP function <BreakBy>) - Microsoft Access
Pass0 - Duration: 0:00:00.63
select a11.[SUBCAT_ID] AS SUBCAT_ID,
max(a12.[SUBCAT_DESC]) AS SUBCAT_DESC,
a12.[CATEGORY_ID] AS CATEGORY_ID,
sum(a11.[TOT_DOLLAR_SALES]) AS WJXBFS1
from [CITY_SUBCATEG_SLS] a11,
[LU_SUBCATEG] a12
where a11.[SUBCAT_ID] = a12.[SUBCAT_ID]
group by a11.[SUBCAT_ID],
a12.[CATEGORY_ID]
Pass1 - Duration: 0:00:00.00
[An Analytical SQL]
If the database supports computation of OLAP functions (in this example Oracle), then the
following SQL is generated:
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Report 6B (DB OLAP function) - Oracle
Pass0 - Duration: 0:00:09.39
select a12.SUBCAT_ID SUBCAT_ID,
a13.SUBCAT_DESC SUBCAT_DESC,
a13.CATEGORY_ID CATEGORY_ID,
sum((a11.QTY_SOLD * (a11.UNIT_PRICE - a11.DISCOUNT)))
WJXBFS1,
avg(sum((a11.QTY_SOLD * (a11.UNIT_PRICE a11.DISCOUNT)))) over(partition by a13.CATEGORY_ID
order by sum((a11.QTY_SOLD * (a11.UNIT_PRICE a11.DISCOUNT))) asc rows 4 preceding) WJXBFS2
from ORDER_DETAIL a11,
LU_ITEM a12,
LU_SUBCATEG a13
where a11.ITEM_ID = a12.ITEM_ID and
a12.SUBCAT_ID = a13.SUBCAT_ID
group by a12.SUBCAT_ID,
a13.SUBCAT_DESC,
a13.CATEGORY_ID
Sorting in OLAP functions
For OLAP functions, sorting is done before performing computations. Basically, there are
two choices for the SortBy parameter setting:
•
Sort by Value in Subexpression: See Reports 5B (in Report 5B (DB OLAP
function - window size) - Oracle 9i, page 62) and 6B (in Report 6B (DB OLAP function) Oracle , page 64) for examples. Note that if the database can perform computation of
OLAP functions, then the definition of the subexpression M1A displays in the order by
clause.
For example, the above reports contain: “order by sum((a11.QTY_SOLD *
(a11.UNIT_PRICE - a11.DISCOUNT))) asc” to represent the <SortBy = Value
ascending> parameter setting.
•
Sort by Objects: This type of sorting can use either attributes or metrics. The
following examples demonstrate the differences in processing when an attribute or
metric is used.
In this example, the OLAP metric is sorted by a normal attribute (either by ID or Desc).
Create an OLAP metric defined as:
OM2 = RunningSum<BreakBy = {[Customer Region]}, SortBy =
([Customer City]@ID asc, Customer@Name dsc)>([M1A])
Then add the attributes Customer Region and Customer, and the metric OM2 to the
template.
The metric OM2 is sorted by Customer City@ID in ascending order, then by
Customer@Name in descending order, though the attribute Customer City is not on the
template. Assume that attribute Customer is a child of the attribute Customer City, and
Customer City is a child of Customer Region.
For a database that does not support OLAP functions (in this case Microsoft Access), the
following SQL is generated:
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Report 7A (MicroStrategy OLAP function <SortBy>) - Microsoft Access
select a14.[CUST_REGION_ID] AS CUST_REGION_ID,
a15.[CUST_REGION_NAME] AS CUST_REGION_NAME,
a11.[CUSTOMER_ID] AS CUSTOMER_ID,
a12.[CUST_LAST_NAME] AS CUST_LAST_NAME,
a12.[CUST_FIRST_NAME] AS CUST_FIRST_NAME,
a12.[CUST_CITY_ID] AS CUST_CITY_ID,
a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [CUSTOMER_SLS] a11,
[LU_CUSTOMER] a12,
[LU_CUST_CITY] a13,
[LU_CUST_STATE] a14,
[LU_CUST_REGION] a15
where a11.[CUSTOMER_ID] = a12.[CUSTOMER_ID] and
a12.[CUST_CITY_ID] = a13.[CUST_CITY_ID] and
a13.[CUST_STATE_ID] = a14.[CUST_STATE_ID] and
a14.[CUST_REGION_ID] = a15.[CUST_REGION_ID]
[An Analytical SQL]
If the database supports computation of OLAP functions (in this example Oracle 9i), the
following SQL is generated:
Report 7B (DB OLAP function <SortBy>) - Oracle
Pass0 - Duration: 0:00:15.00
select a15.CUST_REGION_ID CUST_REGION_ID,
a16.CUST_REGION_NAME CUST_REGION_NAME,
a12.CUSTOMER_ID CUSTOMER_ID,
a13.CUST_LAST_NAME CUST_LAST_NAME,
a13.CUST_FIRST_NAME CUST_FIRST_NAME,
a13.CUST_CITY_ID CUST_CITY_ID,
sum(sum(a11.ORDER_AMT)) over(partition by
a15.CUST_REGION_ID order by a13.CUST_CITY_ID asc,
a13.CUST_LAST_NAME desc, a13.CUST_FIRST_NAME desc rows
unbounded preceding) WJXBFS1
from ORDER_FACT a11,
LU_ORDER a12,
LU_CUSTOMER a13,
LU_CUST_CITY a14,
LU_CUST_STATE a15,
LU_CUST_REGION a16
where a11.ORDER_ID = a12.ORDER_ID and
a12.CUSTOMER_ID = a13.CUSTOMER_ID and
a13.CUST_CITY_ID = a14.CUST_CITY_ID and
a14.CUST_STATE_ID = a15.CUST_STATE_ID and
a15.CUST_REGION_ID = a16.CUST_REGION_ID
group by a15.CUST_REGION_ID,
a16.CUST_REGION_NAME,
a12.CUSTOMER_ID,
a13.CUST_LAST_NAME,
a13.CUST_FIRST_NAME,
a13.CUST_CITY_ID
In both SQLs, the Intelligence Server selects a13.CUST_CITY_ID, even though attribute
Customer City is not on the report. In Report 7B, this attribute is also in the group by
clause. Sort by attribute Customer City@ID is done because of attribute Customer that is a
child of Customer City.
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In Report 7B, the SQL has to issue group by CUST_LAST_NAME and CUST_FIRST_
NAME due to the sort by Customer@Name. For optimization purposes, the Intelligence
Server only groups by the ID column.
There is a VLDB setting that allows you to group by non-ID columns. This VLDB setting can
be set for any report that uses an OLAP metric with a sort by attribute description.
In this example, an OLAP metric is sorted by an attribute and a metric.
Create an OLAP metric defined as:
OM3 = RunningSum<BreakBy = {[Customer Region]}, SortBy =
(Customer@[Last Name] desc, [M1A] asc)>([M1A])
Then add attributes Customer Region and Customer, and metric OM3 to the template. The
OLAP metric OM3 is sorted by Customer Last Name and then by the metric [M1A].
For databases that do not support OLAP functions (in this case Microsoft Access), the
following SQL is generated:
Report 8A (MicroStrategy OLAP function <SortBy>) - Microsoft Access
Pass0 - Duration: 0:00:00.47
select a14.[CUST_REGION_ID] AS CUST_REGION_ID,
a15.[CUST_REGION_NAME] AS CUST_REGION_NAME,
a11.[CUSTOMER_ID] AS CUSTOMER_ID,
a12.[CUST_LAST_NAME] AS CUST_LAST_NAME,
a12.[CUST_FIRST_NAME] AS CUST_FIRST_NAME,
a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [CUSTOMER_SLS] a11,
[LU_CUSTOMER] a12,
[LU_CUST_CITY] a13,
[LU_CUST_STATE] a14,
[LU_CUST_REGION] a15
where a11.[CUSTOMER_ID] = a12.[CUSTOMER_ID] and
a12.[CUST_CITY_ID] = a13.[CUST_CITY_ID] and
a13.[CUST_STATE_ID] = a14.[CUST_STATE_ID] and
a14.[CUST_REGION_ID] = a15.[CUST_REGION_ID] and
a14.[CUST_REGION_ID] in (3, 7)
Pass1 - Duration: 0:00:00.06
[An Analytical SQL]
If the database supports computation of OLAP functions (in this example Oracle), the
following SQL is generated:
Report 8B (DB OLAP function <SortBy>) - Oracle
select a15.CUST_REGION_ID CUST_REGION_ID,
a16.CUST_REGION_NAME CUST_REGION_NAME,
a12.CUSTOMER_ID CUSTOMER_ID,
a13.CUST_LAST_NAME CUST_LAST_NAME,
a13.CUST_FIRST_NAME CUST_FIRST_NAME,
sum(a11.ORDER_AMT) WJXBFS1,
sum(sum(a11.ORDER_AMT)) over(partition by
a15.CUST_REGION_ID order by a13.CUST_LAST_NAME desc,
sum(a11.ORDER_AMT) desc rows unbounded preceding)
WJXBFS2
from ORDER_FACT a11,
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LU_ORDER a12,
LU_CUSTOMER a13,
LU_CUST_CITY a14,
LU_CUST_STATE a15,
LU_CUST_REGION a16
where a11.ORDER_ID = a12.ORDER_ID and
a12.CUSTOMER_ID = a13.CUSTOMER_ID and
a13.CUST_CITY_ID = a14.CUST_CITY_ID and
a14.CUST_STATE_ID = a15.CUST_STATE_ID and
a15.CUST_REGION_ID = a16.CUST_REGION_ID
and a15.CUST_REGION_ID in (7, 3)
group by a15.CUST_REGION_ID,
a16.CUST_REGION_NAME,
a12.CUSTOMER_ID,
a13.CUST_LAST_NAME,
a13.CUST_FIRST_NAME
NULL handling in OLAP functions
When sorted by Intelligence Server, NULL is placed at the end of a list. Databases also put
NULL at the end, as required by the ANSI standard.
For RunningCount or MovingCount functions, NULL is always ignored. For other
computations, such as RunningSum or MovingAvg, NULL is treated as zero for the sum of
values across the function parameters. This behavior is consistent for Intelligence Server
and ANSI-compliant database OLAP functions.
Tie handling in OLAP functions
Tie Handling is related to the SortBy parameter when you sort a tie in a metric. Intelligence
Server uses a Merge-Sort algorithm, which always preserves the original order retrieved
from the database (via ODBC) to the Intelligence Server.
It is not known whether databases use the same sort algorithm; therefore, the Intelligence
Server and database computation of OLAP functions can produce different results
whenever there is a tie on the data.
Using custom plug-in functions
Function Plug-Ins in the MicroStrategy Engine allow users to define their own collection of
functions, and then let the Engine use them for further analysis. The plug-in functions,
usually called user-defined functions, behave as if they are an integrated part of
MicroStrategy and are indistinguishable from all other MicroStrategy functions or operators,
such as Sum, Average, Min, Max, Count, -, +, /, or *. The Intelligence Server performs
standard computations such as Sum, Average, +, /, and so on that are usually calculated by
the database.
MicroStrategy is equipped with approximately 250 functions and operators, including
predefined plug-in functions created by MicroStrategy. They are intended to provide three
full-featured libraries of functions that are most commonly used by customers, which
includes financial functions, math functions, and statistical functions. Examples include
Accrint, IRR, NPV, Abs, Ln, Log, AvgDev, HomoscedasticTTest, Confidence, and so on.
These functions are located at ...Schema Objects/Functions and
Operators/Plug-In Packages.
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For detailed information on individual functions, see Chapter 4, Plug-In Package Functions
and the Function Plug-in Wizard online help.
Creating user-defined plug-in functions
Using the MicroStrategy Function Plug-In Wizard, you can define custom functions relevant
to your business case scenarios. You can create individual functions or even entire function
packages, such as the financial, mathematical, and statistical packages, provided by
MicroStrategy. Guided by the wizard, you start by creating a Microsoft Visual C++ project
with placeholders where you can add custom analytic code. After the new plug-in function is
created, you need to launch MicroStrategy Developer to import it so it can be used for all the
reports. As mentioned previously, once a function is imported, it will be used in the same way
as any other standard MicroStrategy function.
For instructions on creating a plug-in function, see the Function Plug-in Wizard online help.
The main stages of the creation process are described as follows:
•
Designing: determines how to implement the analytical procedures into a computer
algorithm.
•
Creating: creates the Microsoft Visual C++ project, which is used to build a library
containing your algorithms.
•
Implementing: creates the code that embodies the algorithms and compiling this
code into a library that is used by MicroStrategy.
•
Importing: adds the library to a MicroStrategy project so that its algorithms are
available for use in the project.
•
Executing: creates new metrics that use the algorithms and using those metrics in a
MicroStrategy report.
You can create the following types of functions:
•
Single-value functions
•
Group-value functions
•
OLAP functions
The datatypes of input arguments can be:
•
Numeric
•
Date
•
String
A plug-in function can contain more than one parameter. Supported datatypes for
parameters include:
•
Byte
•
Short
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•
Long
•
Float
•
Double
•
String (BSTR)
•
Bool
•
Date
The following two examples illustrate how plug-in functions could be defined:
•
FORECAST<n>(numeric_vector1) = numeric1
▫
Aggregate function with a numeric vector as an input argument and a numeric
scalar as an output argument.
▫
One parameter, n, which is a long integer.
▫
Given a series of values, and given that those values are each separated by one
time interval, this function predicts what the value will be after n additional time
intervals.
For example, you include the unit cost of an item in order to predict how much the unit
cost will be two years later.
FORECAST<2>(UnitCost)
In this example, UnitCost includes a four year history of data, providing the following
yearly values to the FORECAST function.
FORECAST<2>(100,120,140,160)
This results in a value of 200, which is the predicted unit cost two years from the time of
the most recently recorded cost data.
•
CUSTOMNUMBERFORMAT(numeric1) = string1
▫
Simple function with numeric scalar as input argument and string scalar as output
argument.
▫
No parameters.
▫
Transforms a number into a string representation of that number not supported by
any of MicroStrategy's out-of-the-box metric format strings.
Example: CUSTOMNUMBERFORMAT(123456789) = '1 2345.6789'
To install the Function Plug-In Wizard
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1
From ...\MicroStrategy\Desktop, click FPWizard.exe.
2
Follow the prompts to install.
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To activate the Function Plug-In Wizard
1
From the Start button, select Programs, then Microsoft Visual Studio, and then
Microsoft Visual C++. The InstallShield window is displayed.
2
From the File menu, select New. The New window is displayed.
3
On the Projects tab, select MicroStrategy Function Plug-in Wizard.
4
Provide a name in the Project Name text box.
Do not use spaces in the project name. Because the project name will become the name of
the library, use something short and descriptive. For illustration purposes, the project name
myFP is used throughout the online help.
5
Change the location of the project, if needed. Then click OK. The Function Plug-in
Wizard - Step 1 of 2 window is displayed.
6
Click Help for the Function Plug-in Wizard online help whenever needed.
To access the Function Plug-in Wizard online help from the
MicroStrategy online help
1
In the master MicroStrategy online help, search for custom functions, or open the
Creating and Modifying Additional Report Objects folder, expand the
Metrics folder, and then expand the Using custom functions folder.
2
Launch the Using custom functions topic. This topic contains a link to the Function
Plug-in Wizard online help.
3
Click the Function Plug-in Wizard on-line help link. The Function Plug-in
Wizard online help opens in a new help window.
Additional examples of functions in expressions
Hypothesis Testing example
The reports and report objects in this example can be found in the following folder:
MicroStrategy Tutorial\Public Objects\Reports\
MicroStrategy Platform Capabilities\Advanced Analytics\
Statistics and Forecasting\Hypothesis Testing
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Which call centers had a statistically
significant increase in the average daily sales
in recent years?
To answer this question, you must first find the average of daily sales for recent years for
each call center. You accomplish this using the Average Daily Sales metric, defined as:
Average Daily Sales = Avg(Sum(Revenue) {~+, Day+} ) {~+}
This is a simple metric that contains the nested group-value functions Sum and Avg.
Next, create a report that uses this metric, placing the Call Center attribute on the row axis
and the Year attribute on the column axis. A report with this definition is shown below.
In this report it looks like all call centers had a significant increase in the average daily sales in
each successive year. However, an average value by itself does not represent the complete
picture.
To get more information, you can check the standard deviation of daily sales between each
successive year for each call center. You can accomplish this by adding Standard Deviation
in Daily Sales to the report. The metric is defined as:
StDev Daily Sales = Stdev(Sum(Revenue) {~+, Day+} ) {~+}
In statistics, standard deviation is a value which shows how widely a set of values differs
from the mean.
The resulting report provides the average and the standard deviation of daily sales for all
years, for each call center.
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This report shows that all call centers have greater average daily sales in each successive
year. Additionally, the standard deviation of daily sales is higher for each successive year,
with a number of exceptions. A couple of these exceptions include Atlanta between 2009
and 2010, as well as San Francisco between 2008 and 2009. The higher standard deviation
values mean that there is more volatility in these results. Therefore, the higher average daily
sales in each successive year are not necessarily a progressive increase over previous
years; they may have been caused by random fluctuations.
Based on this observation, you next need to find out which call centers have a statistically
significant difference in their average daily sales between these successive years. You can
get the result by testing the hypothesis that the average daily sales are the same, against the
hypothesis that the average daily sales are significantly different. This must be tested for
each call center.
The hypothesis testing is accomplished by computing the p-value. In statistics, p-value is the
probability of making a decision to reject a fact, given that the fact is correct. In the context of
this example, it is the probability of making a mistake by concluding that the average daily
sales in 2008 is significantly different from 2009, given that actually they are about the same.
In general, you want to restrict this type of error so that it is smaller than a certain tolerance
level. This tolerance level is usually set to between 2.5% to 10.0%.
If you assume that the standard deviations of daily sales for all years are the same, then you
can use a function called HomoscedasticTTest to compute the p-value. If you assume that
the daily standard deviations are different, then you must use the function
HeteroscedasticTTest. The following examples consider both of these assumptions and
compute the p-value using HomoscedasticTTest and HeteroscedasticTTest.
This report uses simple metrics based on other metrics, because to calculate the p-value,
you need to compute the daily sales for each call center for each day. You also need to group
the fact Revenue at the levels of Day and Call Center for all years. The fact table DAY_CTR_
SLS is available at the levels of Employee, Order, and Day.
Call Center is related to the fact table via Employee.
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You need a table with a structure similar to the following:
Call Center
Day
Daily Sales (last
year)
Daily Sales (current
year)
Northwest
1
123456
123456
...
123456
123456
N
123456
123456
1
123456
123456
...
123456
123456
N
123456
123456
Southeast
You need to create a temporary table with this structure using metrics. These metrics are
then used in simple metrics to calculate the p-value.
Follow the steps below to build the necessary metrics for the Hypothesis Testing report.
1
Build a metric to create the column for [Daily Sales (current year)] in the temporary table.
This metric is defined as:
Daily Sales (current year)= Sum(Revenue) {~+, Day+} <
[Current year in sample DB]; @2; ->
The default level notation {~+} is placed on the metric since the default is to group by an
attribute on the report. In this example, that report attribute is Call Center. The metric
condition Current year in sample DB is also applied to calculate the data for the
current year available in the MicroStrategy Tutorial data.
2
Build a metric to create the column for [Daily Sales (last year)] in the temporary table.
This metric is defined as:
Daily Sales (last year) = Sum(Revenue) {~+, Day+} <
[Last year in sample DB]; @2; -> | [Last Year's] |
This metric uses a metric condition and a transformation.
3
•
The metric condition Last year in sample DB is applied to calculate the data
for the previous year available in the MicroStrategy Tutorial data.
•
The transformation Last Year's is necessary because attribute Day is a child of
attribute Year. To show Daily Sales in Day 1, 2, …, N of last year together with Daily
Sales in Day 1, 2, …, N of the current year, you must use the transformation to
supply this data.
Build the simple metrics to compute the p-value for each call center by using the metrics
above. The simple metrics are defined as follows:
HeteroscedasticTTest([Daily Sales (last year)], [Daily
Sales (current year)]) {~+}
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HeteroscedasticTTest([Daily Sales (last year)], [Daily
Sales (current year)]) {~+}
Create a report with the two metrics shown above (for p-value) and the attribute Call Center.
The Hypothesis Testing report is shown below.
The resulting report, Hypothesis Testing, is shown above. All call centers, except for Atlanta,
Salt Lake City, Seattle, and Fargo, have a p-value of less that 5%. This indicates that the
probability of making an error in concluding that the sales have significantly increased is low
for all but these four call centers. Additionally, even Salt Lake City and Fargo are at 20% or
lower. This is strong evidence that average daily sales for the majority of the call centers can
be attributed to a steady increase, rather than random fluctuations.
The SQL generated for the report Hypothesis Testing is displayed below, along with a
summary of the actions taken at the end of the SQL statement:
Pass0 - Execution Duration:0:00:00.06
create table ZZTH0FDXLS4MD000 (
DAY_DATE TIMESTAMP,
CALL_CTR_ID SHORT,
WJXBFS1 DOUBLE)
Pass1 - Execution Duration:0:00:00.34
insert into ZZTH0FDXLS4MD000
select a12.[DAY_DATE] AS DAY_DATE,
a11.[CALL_CTR_ID] AS CALL_CTR_ID,
a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [DAY_CTR_SLS] a11,
[LU_DAY] a12
where a11.[DAY_DATE] = a12.[LY_DAY_DATE]
and a12.[YEAR_ID] in (2010)
Pass2 - Execution Duration: 0:00:00.00
create table ZZTU1RY0YSKMD001 (
DAY_DATE TIMESTAMP,
CALL_CTR_ID SHORT,
WJXBFS1 DOUBLE)
Pass3 - Execution Duration: 0:00:00.03
insert into ZZTU1RY0YSKMD001
select a11.[DAY_DATE] AS DAY_DATE,
a11.[CALL_CTR_ID] AS CALL_CTR_ID,
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a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [DAY_CTR_SLS] a11,
[LU_DAY] a12
where a11.[DAY_DATE] = a12.[DAY_DATE]
and a12.[YEAR_ID] in (2010)
Pass4 - Execution Duration: 0:00:00.07
select pa11.[CALL_CTR_ID] AS CALL_CTR_ID,
a13.[CENTER_NAME] AS CENTER_NAME,
pa11.[DAY_DATE] AS DAY_DATE,
pa11.[WJXBFS1] AS WJXBFS1,
pa12.[WJXBFS1] AS WJXBFS2
from [ZZTH0FDXLS4MD000] pa11,
[ZZTU1RY0YSKMD001] pa12,
[LU_CALL_CTR] a13
where pa11.[CALL_CTR_ID] = pa12.[CALL_CTR_ID] and
pa11.[DAY_DATE] = pa12.[DAY_DATE] and
pa11.[CALL_CTR_ID] = a13.[CALL_CTR_ID]
Pass5 - Execution Duration: 0:00:00.00
[Analytical SQL calculated by the Analytical Engine:
select CALL_CTR_ID,
CENTER_NAME,
HomoscedasticTTest(WJXBFS1, WJXBFS2),
HeteroscedasticTTest(WJXBFS1, WJXBFS2)
from [previous pass]
]
Pass6 - Execution Duration: 0:00:00.00
[Populate Report Data]
Pass7 - Execution Duration: 0:00:00.06
drop table ZZTH0FDXLS4MD000
Pass8 - Execution Duration: 0:00:00.01
drop table ZZTU1RY0YSKMD001
•
Pass0 and Pass1 are issued by Intelligence Server to compute the metric Daily Sales
(last year). The Intelligence Server prepares the temporary table with Call Center and
Day as its key. Then, it retrieves last year’s sales using the transformation Last Year’s.
•
Pass2 and Pass3 are issued to compute the metric Daily Sales (current year). Attributes
Call Center and Day are used as keys to the temporary table. This pass is similar to
Pass0 and Pass1 with the key difference being that the metric does not have a
transformation.
•
In Pass4 and Pass5, the Intelligence Server computes the p-value for each call center,
using the HeteroscedasticTTest and the HomoscedasticTTest functions.
•
The remaining passes perform final report preparation and drop the temporary tables.
Confidence level example
The reports and report components in this example can be found in the following folder:
MicroStrategy Tutorial\Public Objects\Reports\
MicroStrategy Platform Capabilities\Advanced Analytics\
Statistics and Forecasting\Confidence Level
Confidence level is used to determine valuable customers in two slightly different ways:
75
•
Who are my valuable customers? (Example 1), page 76: Determines valuable
customers as those whose average spending is above an upper bound of sales orders.
•
Who are my valuable customers? (Example 2), page 80: Determines valuable
customers as those whose average spending is above an upper bound of sales orders.
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Additionally, valuable customers are compared for each Customer Region, rather than
comparing customers in all regions.
Who are my valuable customers? (Example 1)
The basic goal is to define a cut-off value that represents the minimum requirement to be
classified as a valuable customer. To identify the valuable customers in your customer base,
you must determine the parameters that help differentiate those customers from the others.
In this example, valuable customers are those whose average spending is above an upper
bound of sales orders.
The ORDER_FACT table contains all orders received. Assume that the sales order amount is
normally distributed with a certain mean and standard deviation. Based on the assumption of
normal distribution and a confidence level of 99%, you can define valuable customers as
those who have a spending average above an upper bound of sales orders.
To determine the valuable customers based on this criteria, you need to know several
values. Use the following metrics to obtain these values.
1
You must know the average sales order from all orders in the ORDER_FACT table. This
is the arithmetic mean of the normal distribution. You can get this value using the Avg
function. The metric to compute the average of sales orders from the ORDER_FACT
table can be defined as:
M01 = Avg(Revenue) {![Call Center]+, !Year+,
!Employee+, !Order+, !Day+}
Since Day, Employee, and Order are the highest attributes that are parents of the keys
in the ORDER_FACT table, include them as the level of aggregation to make sure that the
ORDER_FACT table is used. Also, the average must be calculated over all sales order
amounts, so you must set the group-by on the level attributes to none. This ensures that
the metric does not group by any of these attributes.
2
You must know the standard deviation of sales orders from all orders. This number is the
standard deviation of the normal distribution. You can get this value using the StDevP
function (see StDevP (standard deviation of a population), page 110).
StDevP is the standard deviation of a population, while StDev is the standard deviation of a
sample. Therefore, the StDevP function is used when there is enough data to fully represent a
scenario.
The metric to compute the population standard deviation of sales orders from the
ORDER_FACT table is defined as follows:
M02 = StdevP(Revenue) {![Call Center]+, !Year+,
!Employee+, !Order+, !Day+}
3
You must know the number of rows in the ORDER_FACT table. You need this value to
calculate the cut-off value. You can retrieve this value by using the Count function, in
particular, Count from the ORDER_FACT table. The metric to count the data in the
ORDER_FACT table is defined as follows:
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M03 = Count(Revenue) {![Call Center]+, !Year+,
!Employee+, !Order+, !Day+}
4
Under the assumption of normal distribution with the parameters given above, you must
determine the cut-off value that represents the top 5% of sales order amounts. To do
this, you can use the Confidence plug-in function. The metric to compute the cut-off
number is defined as follows, using some of the metrics defined above:
M04 = Confidence(0.01, [M02], [M03])
The number 0.01 comes from a normal distribution with 99% confidence level.
5
The metric that calculates the upper bound uses some of the metrics defined above, and
is defined as follows:
M05 = ([M01] + [M04])
6
You must calculate the average sales for each customer to get a list of valuable
customers based on the criteria. The metric is defined as follows:
Average Sales = Avg(Revenue) {~+}
You do not need to include the level of aggregation for Average Sales since the default
{~+} notation is replaced by the attribute Customer.
7
You must also apply a metric qualification in a filter to restrict the rows returned to those
that meet the definition of valuable customers. The filter is defined as follows:
F01 = Set of Customers where (Average Sales Greater
than [M05]
To generate a report listing valuable customers, create a report with the attribute Customer
on the row axis and the attribute Customer Region on the page-by axis. Apply filter F01 to
the report to produce a list of valuable customers for each attribute element in Customer
Region. The Valuable Customers 01 report is shown below:
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The SQL generated for the report Valuable Customers 01 is as follows:
Pass0 - Execution Duration: 0:00:00.07
create table ZZTLZV82TJ0MD000 (
CUSTOMER_ID LONG,
WJXBFS1 DOUBLE)
Pass1 - Execution Duration: 0:00:00.20
insert into ZZTLZV82TJ0MD000
select a11.[CUSTOMER_ID] AS CUSTOMER_ID,
avg(a11.[TOT_DOLLAR_SALES]) AS WJXBFS1
from [CUSTOMER_SLS] a11
group by a11.[CUSTOMER_ID]
Pass2 - Execution Duration: 0:00:00.04
select a11.[CUSTOMER_ID] AS CUSTOMER_ID,
a11.[ORDER_ID] AS ORDER_ID,
a11.[ITEM_ID] AS ITEM_ID,
a11.[EMP_ID] AS EMP_ID,
a11.[ORDER_DATE] AS DAY_DATE,
a11.[CUSTOMER_ID] AS CUSTOMER_ID0,
(a11.[QTY_SOLD] * (a11.[UNIT_PRICE] - a11.[DISCOUNT]))
AS WJXBFS1
from [ORDER_DETAIL] a11
Pass3 - Execution Duration: 0:00:00.10
create table ZZTW4A12VECMD001 (
CUSTOMER_ID LONG,
WJXBFS1 DOUBLE)
Pass4 - Execution Duration: 0:00:00.00
[Analytical SQL calculated by the Analytical Engine:
select CUSTOMER_ID0,
(ISNULL(avg(WJXBFS1), 0) + Confidence(0.01,
StdevP(WJXBFS1), count(WJXBFS1)))
from [previous pass]
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]
Pass5 - Execution Duration: 0:00:00.00
insert into ZZTW4A12VECMD001 values (1, 33.4351602545)
Pass6 - Execution Duration: 0:00:00.00
create table ZZTPDKUSLWSMQ002 (
CUSTOMER_ID LONG)
Pass7 - Execution Duration: 0:00:00.10
insert into ZZTPDKUSLWSMQ002
select pa11.[CUSTOMER_ID] AS CUSTOMER_ID
from [ZZTLZV82TJ0MD000] pa11,
[ZZTW4A12VECMD001] pa12
where pa11.[CUSTOMER_ID] = pa12.[CUSTOMER_ID]
and (pa11.[WJXBFS1] > pa12.[WJXBFS1])
Pass8 - Execution Duration: 0:00:01.53
select a11.[CUSTOMER_ID] AS CUSTOMER_ID,
max(a13.[CUST_LAST_NAME]) AS CUST_LAST_NAME,
max(a13.[CUST_FIRST_NAME]) AS CUST_FIRST_NAME,
a15.[CUST_REGION_ID] AS CUST_REGION_ID,
max(a16.[CUST_REGION_NAME]) AS CUST_REGION_NAME0,
avg(a11.[TOT_DOLLAR_SALES]) AS WJXBFS1
from [CUSTOMER_SLS] a11,
[ZZTPDKUSLWSMQ002] pa12,
[LU_CUSTOMER] a13,
[LU_CUST_CITY] a14,
[LU_CUST_STATE] a15,
[LU_CUST_REGION] a16
where a11.[CUSTOMER_ID] = pa12.[CUSTOMER_ID] and
a11.[CUSTOMER_ID] = a13.[CUSTOMER_ID] and
a13.[CUST_CITY_ID] = a14.[CUST_CITY_ID] and
a14.[CUST_STATE_ID] = a15.[CUST_STATE_ID] and
a15.[CUST_REGION_ID] = a16.[CUST_REGION_ID]
group by a11.[CUSTOMER_ID],
a15.[CUST_REGION_ID]
Pass9 - Execution Duration: 0:00:00.00
[Populate Report Data]
Pass10 - Execution Duration: 0:00:00.06
drop table ZZTLZV82TJ0MD000
Pass11 - Execution Duration: 0:00:00.00
drop table ZZTW4A12VECMD001
Pass12 - Execution Duration: 0:00:00.00
drop table ZZTPDKUSLWSMQ002
79
•
Pass0 and Pass1 compute the average sales per customer (Average Sales metric) and
put the results in a temporary table.
•
Pass2 retrieves ORDER_DETAIL data, puts the data into memory, and uses the data to
compute M01, M02, and M03.
•
Pass3 creates a temporary table used to store the results of the calculations.
•
Pass4 calculates M02 using the Intelligence Server because the group-value function
StDevP is not supported by the Access database. The values of M01, M02, and M03 are
used to compute M05.
•
Pass5 inserts the results of the calculations into the temporary table.
•
Pass6 and Pass7 use the earlier two temporary tables to qualify on valuable customers
based on the definition.
•
Pass8 displays the report with attributes Customer and Customer Region.
•
The remaining passes perform additional report preparation and drop the temporary
tables.
© 2017, MicroStrategy Inc.
Functions Reference
Who are my valuable customers? (Example 2)
This example generates a list of valuable customers based on a different definition from the
previous example.
Assume a normal distribution of sales orders is still valid, but the cut-off value is for each
Customer Region. This means you must compare the average spending of each customer
against the average spending of the Customer Region to which the customer belongs. To
allow for more variation, take the average value from the current year data, but use sample
standard deviations for all available years. Use a confidence level of 95%.
Valuable customers are defined as those customers who have total spending above the cutoff value. The cut-off value is calculated using the filters and metrics defined below:
1
The filter used to limit the calculation of average sales orders to only the data for the
current year is defined as follows:
Current year in sample DB = Year In list (2010)
2
The metric used to calculate the average sales order is defined as follows:
M07 = Avg(Revenue) {[Customer Region]+, !Year+,
!Employee+, !Order+, !Day+} <[Current year in sample
DB]; @2; ->
3
The metric used to calculate the standard deviation of sales orders is defined as follows:
M08 = Stdev(Revenue) {[Customer Region]+, ![Call
Center]+, !Year+, !Employee+, !Order+, !Day+}
Notice that the function Stdev (standard deviation of a sample) is used in this metric
because the amount of data is limited to each Customer Region. This means the data
can be considered as more of a sample than a full population.
4
The metric used to determine the cut-off value is defined as follows, using the preceding
metrics:
M09 = ([M07] + (1.96 * [M08]))
The number 1.96 comes from a normal distribution with 95% confidence level. In practice,
this number is often rounded to 2 instead of 1.96.
5
The metric used to determine each customer’s spending so that it can be compared to
the cut-off value is defined as follows:
M10 = Sum(Revenue){~+}
6
The metric qualification (filter) needed to restrict the report to a list of valuable customers
based on Definition 2 is defined as follows:
F03 = Set of Customer where (M10 Greater than [M09])
7
To view the last order sales from all valuable customers, use the metric defined as
follows:
Last Order Sales = Sum(Revenue) {~+, >|Day+}
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You must compute order sales for each customer at the Day level from the ORDER_
FACT table, but must take it from the last Day on that fact table. The {~+} is replaced by
any attribute on the report. In this example, it is the Customer attribute.
Build a report putting the attribute Customer on the row axis, the attribute Customer Region
on the page-by axis, and the Last Order Sales metric on the column axis, then applying the
filter F03. The Valuable Customers 02 report is shown below.
The SQL generated by the Valuable Customers 02 report is as follows:
Pass0 - Execution Duration: 0:00:00.00
create table ZZT3WSP7T8BMD000 (
CUSTOMER_ID LONG,
WJXBFS1 DOUBLE)
Pass1 - Execution Duration: 0:00:00.06
insert into ZZT3WSP7T8BMD000
select a11.[CUSTOMER_ID] AS CUSTOMER_ID,
sum(a11.[TOT_DOLLAR_SALES]) AS WJXBFS1
from [CUSTOMER_SLS] a11
group by a11.[CUSTOMER_ID]
Pass2 - Execution Duration: 0:00:00.00
create table ZZT36UF785VMD001 (
CUST_REGION_ID SHORT,
WJXBFS1 DOUBLE)
Pass3 - Execution Duration: 0:00:02.18
insert into ZZT36UF785VMD001
select a14.[CUST_REGION_ID] AS CUST_REGION_ID,
avg((a11.[QTY_SOLD] * (a11.[UNIT_PRICE] -
81
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a11.[DISCOUNT]))) AS WJXBFS1
from [ORDER_DETAIL] a11,
[LU_CUSTOMER] a12,
[LU_CUST_CITY] a13,
[LU_CUST_STATE] a14,
[LU_DAY] a15
where a11.[CUSTOMER_ID] = a12.[CUSTOMER_ID] and
a12.[CUST_CITY_ID] = a13.[CUST_CITY_ID] and
a13.[CUST_STATE_ID] = a14.[CUST_STATE_ID] and
a11.[ORDER_DATE] = a15.[DAY_DATE]
and a15.[YEAR_ID] in (2010)
group by a14.[CUST_REGION_ID]
Pass4 - Execution Duration: 0:00:00.03
select a11.[CUSTOMER_ID] AS CUSTOMER_ID,
a11.[ORDER_ID] AS ORDER_ID,
a11.[ITEM_ID] AS ITEM_ID,
a11.[EMP_ID] AS EMP_ID,
a11.[ORDER_DATE] AS DAY_DATE,
a14.[CUST_REGION_ID] AS CUST_REGION_ID,
(a11.[QTY_SOLD] * (a11.[UNIT_PRICE] - a11.[DISCOUNT]))
AS WJXBFS1
from [ORDER_DETAIL] a11,
[LU_CUSTOMER] a12,
[LU_CUST_CITY] a13,
[LU_CUST_STATE] a14
where a11.[CUSTOMER_ID] = a12.[CUSTOMER_ID] and
a12.[CUST_CITY_ID] = a13.[CUST_CITY_ID] and
a13.[CUST_STATE_ID] = a14.[CUST_STATE_ID]
Pass5 - Execution Duration: 0:00:00.00
create table ZZTS6MZ3N2BMD002 (
CUST_REGION_ID SHORT,
WJXBFS1 DOUBLE)
Pass6 - Execution Duration: 0:00:00.00
[Analytical SQL calculated by the Analytical Engine:
select CUST_REGION_ID,
ISNULL((1.96 * Stdev(WJXBFS1)), 0)
from [previous pass]
]
Pass7 - Execution Duration: 0:00:00.00
insert into ZZTS6MZ3N2BMD002 values (1, 127.4088118401)
Pass8 - Execution Duration: 0:00:00.00
create table ZZTSMWZF9SRMQ003 (
CUSTOMER_ID LONG)
Pass9 - Execution Duration: 0:00:00.36
insert into ZZTSMWZF9SRMQ003
select distinct pa11.[CUSTOMER_ID] AS CUSTOMER_ID
from [ZZT3WSP7T8BMD000] pa11,
[LU_CUSTOMER] a12,
[LU_CUST_CITY] a13,
[LU_CUST_STATE] a14,
[ZZT36UF785VMD001] pa15,
[ZZTS6MZ3N2BMD002] pa16
where pa11.[CUSTOMER_ID] = a12.[CUSTOMER_ID] and
a12.[CUST_CITY_ID] = a13.[CUST_CITY_ID] and
a13.[CUST_STATE_ID] = a14.[CUST_STATE_ID] and
a14.[CUST_REGION_ID] = pa15.[CUST_REGION_ID] and
pa15.[CUST_REGION_ID] = pa16.[CUST_REGION_ID]
and (pa11.[WJXBFS1] > (IIF(ISNULL(pa15.[WJXBFS1]), 0,
pa15.[WJXBFS1]) + IIF(ISNULL(pa16.[WJXBFS1]), 0,
pa16.[WJXBFS1])))
Pass10 - Execution Duration: 0:00:00.00
create table ZZTRX7EEWHNNB004 (
DAY_DATE TIMESTAMP,
CUSTOMER_ID LONG,
WJXBFS1 DOUBLE)
Pass11 - Execution Duration: 0:00:07.89
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insert into ZZTRX7EEWHNNB004
select a11.[ORDER_DATE] AS DAY_DATE,
a11.[CUSTOMER_ID] AS CUSTOMER_ID,
sum((a11.[QTY_SOLD] * (a11.[UNIT_PRICE] a11.[DISCOUNT]))) AS WJXBFS1
from [ORDER_DETAIL] a11,
[ZZTSMWZF9SRMQ003] pa12
where a11.[CUSTOMER_ID] = pa12.[CUSTOMER_ID]
group by a11.[ORDER_DATE],
a11.[CUSTOMER_ID]
Pass12 - Execution Duration: 0:00:00.00
create table ZZTQFB0EMCJMB005 (
CUSTOMER_ID LONG,
WJXBFS1 TIMESTAMP)
Pass13 - Execution Duration: 0:00:00.59
insert into ZZTQFB0EMCJMB005
select pc11.[CUSTOMER_ID] AS CUSTOMER_ID,
max(pc11.[DAY_DATE]) AS WJXBFS1
from [ZZTRX7EEWHNNB004] pc11
group by
pc11.[CUSTOMER_ID]
Pass14 Execution Duration:
0:00:00.78
select distinct pa11.[CUSTOMER_ID] AS CUSTOMER_ID,
a13.[CUST_LAST_NAME] AS CUST_LAST_NAME,
a13.[CUST_FIRST_NAME] AS CUST_FIRST_NAME,
a15.[CUST_REGION_ID] AS CUST_REGION_ID,
a16.[CUST_REGION_NAME] AS CUST_REGION_NAME0,
pa11.[WJXBFS1] AS WJXBFS1
from
[ZZTRX7EEWHNNB004]
pa11,
[ZZTQFB0EMCJMB005]
pa12,
[LU_CUSTOMER]
a13,
[LU_CUST_CITY] a14,
[LU_CUST_STATE] a15,
[LU_CUST_REGION]
a16
where
pa11.[CUSTOMER_ID] = pa12.[CUSTOMER_ID] and
pa11.[DAY_DATE] = pa12.[WJXBFS1] and
pa11.[CUSTOMER_ID] = a13.[CUSTOMER_ID] and
a13.[CUST_CITY_ID] = a14.[CUST_CITY_ID] and
a14.[CUST_STATE_ID] = a15.[CUST_STATE_ID] and
a15.[CUST_REGION_ID] = a16.[CUST_REGION_ID]
Pass15 Execution Duration:
0:00:00.00
[Populate Report Data]
Pass16 Execution Duration:
0:00:00.00
drop table ZZT3WSP7T8BMD000
Pass17 Execution Duration:
0:00:00.00
drop table ZZT36UF785VMD001
Pass18 Execution Duration:
0:00:00.01
drop table ZZTS6MZ3N2BMD002
Pass19 Execution Duration:
0:00:00.00
drop table ZZTSMWZF9SRMQ003
Pass20 Execution Duration:
0:00:00.00
drop table ZZTRX7EEWHNNB004
Pass21 Execution Duration:
0:00:00.00
drop table ZZTQFB0EMCJMB005
83
•
Pass0 and Pass1 compute metric M10.
•
Pass2 and Pass3 compute metric M07.
•
Pass4, Pass5, Pass6, and Pass7 compute (1.96 * M08). The group-value function for
M08, StDev, is not supported by the database (Microsoft Access) so it is calculated by
Intelligence Server.
•
Pass8 and Pass9 obtain the list of valuable customers, evaluating the filter condition
F03.
© 2017, MicroStrategy Inc.
Functions Reference
•
Pass10, Pass11, Pass12, and Pass13 compute the Last Order Sales metric.
•
Pass 14 displays the results for the report.
•
All other passes drop the temporary tables.
Statistical descriptors - Simple example
The report and report components used in this example can be found in the MicroStrategy
Tutorial project in the following folder:
MicroStrategy Tutorial\Public Objects\Reports\
MicroStrategy Platform Capabilities\Advanced Analytics\
Statistics and Forecasting\Statistical Descriptors
The Component Objects - Advanced folder contains additional statistical descriptor
examples.
How do I learn more about my customers?
You can get more information about the customers in each region by performing statistical
calculations on their spending. This example uses analysis to answer the following
questions:
•
How many customers exist in each customer region?
•
What is the average spending of customers in each customer region?
•
What is the median spending of customers in each customer region?
•
What is the standard deviation of customer spending in each customer region?
•
How much is the 25th and 75th percentile of customer spending in each customer
region?
These are statistical descriptors about customers in each customer region. This type of data
can help you understand how customers spend their money.
To answer the questions above, you must create a set of simple metrics based on other
metrics. These metrics are defined using nested group-value functions.
1
This metric determines the number of customers in each region:
Count of Customers = Sum(Count(1) {~+, Customer+} )
{~+}
2
This metric determines the average spending of customers in each region:
a.Mean Revenue Per Customer = Avg(Sum(Revenue) {~+,
Customer+} ) {~+}
3
This metric determines the median spending of customers in each region:
b.Median Revenue Per Customer = Median(Sum(Revenue)
{~+, Customer+} ) {~+}
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4
This metric determines the standard deviation of customer spending in each region:
e.StDev of Revenue Per Customer = Stdev(Sum(Revenue)
{~+, Customer+} ) {~+}
5
This metric determines the threshold for the 25th percentile:
25th Percentile of Revenue Per Customer = Percentile
(Sum(Revenue) {~+, Customer+} , 0.25) {~+}
6
This metric determines the threshold for the 75th percentile:
75th Percentile of Revenue Per Customer = Percentile
(Sum(Revenue) {~+, Customer+} , 0.75) {~+}
Create a report and place all of the metrics described above on the row axis and place the
attribute Customer Region on the column axis. Execute the Statistical Descriptors - Simple
report, which is shown below:
This report provides information on the behavior of customer spending in each customer
region. For example, the Northwest customer region has the least number of customers, but
most customers in that region spend more than any other region, as seen in the larger mean
and median revenues per customer. This can highlight that it is worthwhile to gain a larger
customer base in this region. However, the somewhat lower value for the standard deviation
of revenue per customer in the Northwest region also indicates that this result might be due
to the smaller sample size and a few customers that spend abnormally large amounts. This
standard deviation value helps to show that while a campaign to increase the customer base
in the region is still an attractive idea, the results should be monitored to determine if
revenues continue to be higher per customer.
The SQL generated for the Statistical Descriptors - Simple report is as follows:
Pass0 - Execution Duration: 0:00:00.00
create table ZZT42W71C95MD000 (
CUSTOMER_ID LONG,
WJXBFS1 LONG,
WJXBFS2 DOUBLE)
Pass1 - Execution Duration: 0:00:00.06
insert into ZZT42W71C95MD000
select a11.[CUSTOMER_ID] AS CUSTOMER_ID,
count(1.0) AS WJXBFS1,
sum(a11.[TOT_DOLLAR_SALES]) AS WJXBFS2
from [CUSTOMER_SLS] a11
group by a11.[CUSTOMER_ID]
Pass2 - Execution Duration: 0:00:00.09
select distinct pa11.[CUSTOMER_ID] AS CUSTOMER_ID,
a14.[CUST_REGION_ID] AS CUST_REGION_ID,
a15.[CUST_REGION_NAME] AS CUST_REGION_NAME0,
pa11.[WJXBFS1] AS WJXBFS1,
pa11.[WJXBFS2] AS WJXBFS2
from [ZZT42W71C95MD000] pa11,
[LU_CUSTOMER] a12,
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[LU_CUST_CITY] a13,
[LU_CUST_STATE] a14,
[LU_CUST_REGION] a15
where pa11.[CUSTOMER_ID] = a12.[CUSTOMER_ID] and
a12.[CUST_CITY_ID] = a13.[CUST_CITY_ID] and
a13.[CUST_STATE_ID] = a14.[CUST_STATE_ID] and
a14.[CUST_REGION_ID] = a15.[CUST_REGION_ID]
Pass3 - Execution Duration: 0:00:00.00
[Analytical SQL calculated by the Analytical Engine:
select CUST_REGION_ID,
CUST_REGION_NAME0,
sum(WJXBFS1),
avg(WJXBFS2),
Median(WJXBFS2),
Stdev(WJXBFS2),
Percentile(WJXBFS2, 0.25),
Percentile(WJXBFS2, 0.75)
from [previous pass]
]
Pass4 - Execution Duration: 0:00:00.00
[Populate Report Data]
Pass5 - Execution Duration: 0:00:00.00
drop table ZZT42W71C95MD000
•
The first two passes of SQL (Pass0 and Pass1) prepare and calculate the values used in
other metrics.
The Intelligence Server optimizes the process by pulling only a single column of revenue
data even though it is used in several other metrics.
•
In Pass2 and Pass3 the Intelligence Server retrieves the results from Pass1 to compute
the other metrics. In Pass3 the Intelligence Server acts as an in-memory database,
using the values from Pass2 to calculate the following group-value functions: Sum, Avg,
Median, Stdev, and Percentile.
•
The remaining passes prepare the report and drop the temporary tables.
Forecasting example
The report and report components in this example can be found in the MicroStrategy Tutorial
project in the following folder:
MicroStrategy Tutorial\Public Objects\Reports\
MicroStrategy Platform Capabilities\Advanced Analytics\
Statistics and Forecasting\Forecasting
How to forecast future sales based on existing sales data?
The MicroStrategy Tutorial project has sales data for multiple years. Using this data, you can
forecast potential sales for the upcoming years.
To plot a sales line for the expected future sales data, apply linear extrapolation techniques
to the historical data. Linear extrapolation involves the assumption that the trend of past data
will continue in a linear fashion. The slope and Y-intercept values are calculated based on
historical data, and the same slope and Y-intercept are applied to extend the data into the
future.
This is just one type of forecasting analysis that can be done in MicroStrategy. There are
additional MicroStrategy functions that provide other variations on calculating a forecast of
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values. For information on these functions, how they forecast values, and forecasting
examples using these functions, see:
•
ForecastV (forecast, vector input), page 271
•
GrowthV (growth, vector input), page 272
•
TrendV (trend, vector input), page 276
To perform the analysis, build a set of metrics that calculate each of the components in the
analysis, then use those metrics together to create the final Forecasting metric.
1
The metric that calculates sales revenue at the Year level is defined as follows:
Revenue {Year} = Sum(Revenue) {~+, Year+}
2
The metric that expresses the Year elements as a series of integers, to simplify the
analysis, is defined as follows:
YearNumber = RunningSum<SortBy= (Year@ID) >(Sum(1) {~+,
Year } )
3
The metric that calculates the slope of the line produced by the Revenue {Year} metric is
defined as follows:
SlopeMetric = Slope([Revenue {Year}], YearNumber) {~+,
!Year+}
4
The metric that calculates the Y-intercept of the line produced by the Revenue {Year}
metric is defined as follows:
InterceptMetric = Intercept([Revenue {Year}],
YearNumber) {~+, !Year+}
5
The final metric combines the three preceding metrics into a forecast value. This formula
follows the standard formula for a line: y = mx + b. The metric is defined as follows:
Forecast = ((SlopeMetric * YearNumber) +
InterceptMetric)
Place the Forecast metric on a report with Year. A sales forecast is generated for all
available years. The Forecast (Graph) report displays as follows:
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The sales for 2008, 2009, and 2010 are actual recorded values, and the sales for 2011 are a
linear extrapolation of the existing data into the future.
Another report in the Forecasting folder, Forecast (Grid) places the Forecast metric alongside
the Revenue metric for comparison. However, it requires the use of outer joins to work
properly. The MicroStrategy Tutorial includes Microsoft Access as its default database; in
order for the Forecast (Grid) report to work, the Tutorial warehouse must be moved into a
database platform that fully supports outer joins.
The following SQL is generated for the Forecast (Graph) report:
Pass0 - Execution Duration: 0:00:00.20
create table ZZT16JMSID1MD000 (
YEAR_ID SHORT,
CATEGORY_ID SHORT,
WJXBFS1 DOUBLE)
Pass1 - Execution Duration: 0:00:00.10
insert into ZZT16JMSID1MD000
select a11.[YEAR_ID] AS YEAR_ID,
a11.[CATEGORY_ID] AS CATEGORY_ID,
a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [YR_CATEGORY_SLS] a11
Pass2 - Execution Duration: 0:00:00.23
select a12.[YEAR_ID] AS YEAR_ID,
a11.[CATEGORY_ID] AS CATEGORY_ID,
1.0 AS WJXBFS1
from [LU_CATEGORY] a11,
[LU_YEAR] a12
Pass3 - Execution Duration: 0:00:00.00
create table ZZT3M2QSNWDMD001 (
YEAR_ID SHORT,
CATEGORY_ID SHORT,
WJXBFS1 DOUBLE)
Pass4 - Execution Duration: 0:00:00.00
[Analytical SQL calculated by the Analytical Engine:
select YEAR_ID,
CATEGORY_ID,
RunningSum<SortBy= ([YEAR_ID])>(WJXBFS1)
from [previous pass]
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]
Pass5 - Execution Duration: 0:00:00.00
insert into ZZT3M2QSNWDMD001 values (2008, 1, 1)
Pass6 - Execution Duration: 0:00:00.00
create table ZZTDMBESMXPOJ002 (
YEAR_ID SHORT,
CATEGORY_ID SHORT)
Pass7 - Execution Duration: 0:00:00.00
insert into ZZTDMBESMXPOJ002
select pa11.[YEAR_ID] AS YEAR_ID,
pa11.[CATEGORY_ID] AS CATEGORY_ID
from [ZZT16JMSID1MD000] pa11
Pass8 - Execution Duration: 0:00:00.00
insert into ZZTDMBESMXPOJ002
select pa11.[YEAR_ID] AS YEAR_ID,
pa11.[CATEGORY_ID] AS CATEGORY_ID
from [ZZT3M2QSNWDMD001] pa11
Pass9 - Execution Duration: 0:00:00.00
create table ZZT9MCAB4STOD003 (
YEAR_ID SHORT,
CATEGORY_ID SHORT)
Pass10 - Execution Duration: 0:00:00.00
insert into ZZT9MCAB4STOD003
select distinct pa11.[YEAR_ID] AS YEAR_ID,
pa11.[CATEGORY_ID] AS CATEGORY_ID
from [ZZTDMBESMXPOJ002] pa11
Pass11 - Execution Duration: 0:00:00.00
select pa11.[YEAR_ID] AS YEAR_ID,
pa11.[CATEGORY_ID] AS CATEGORY_ID,
pa12.[WJXBFS1] AS WJXBFS1,
pa13.[WJXBFS1] AS WJXBFS2
from [ZZT9MCAB4STOD003] pa11,
[ZZT16JMSID1MD000] pa12,
[ZZT3M2QSNWDMD001] pa13
where pa11.[CATEGORY_ID] = pa12.[CATEGORY_ID] and
pa11.[YEAR_ID] = pa12.[YEAR_ID] and
pa11.[CATEGORY_ID] = pa13.[CATEGORY_ID] and
pa11.[YEAR_ID] = pa13.[YEAR_ID]
Pass12 - Execution Duration: 0:00:00.00
create table ZZTMYCAAR65MD004 (
CATEGORY_ID SHORT,
WJXBFS1 DOUBLE,
WJXBFS2 DOUBLE)
Pass13 - Execution Duration: 0:00:00.00
[Analytical SQL calculated by the Analytical Engine:
select CATEGORY_ID,
Slope(WJXBFS1, WJXBFS2),
Intercept(WJXBFS1, WJXBFS2)
from [previous pass]
]
Pass14 - Execution Duration: 0:00:00.00
insert into ZZTMYCAAR65MD004 values (1,
58938.0812500116, 585341.077083335)
Pass15 - Execution Duration: 0:00:00.04
select pa11.[CATEGORY_ID] AS CATEGORY_ID,
a13.[CATEGORY_DESC] AS CATEGORY_DESC0,
pa11.[YEAR_ID] AS YEAR_ID,
(IIF(ISNULL((pa12.[WJXBFS1] * pa11.[WJXBFS1])), 0,
(pa12.[WJXBFS1] * pa11.[WJXBFS1])) +
IIF(ISNULL(pa12.[WJXBFS2]), 0, pa12.[WJXBFS2])) AS
WJXBFS1
from [ZZT3M2QSNWDMD001] pa11,
[ZZTMYCAAR65MD004] pa12,
[LU_CATEGORY] a13
where pa11.[CATEGORY_ID] = pa12.[CATEGORY_ID] and
pa11.[CATEGORY_ID] = a13.[CATEGORY_ID]
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Pass16 - Execution Duration:
[Populate Report Data]
Pass17 - Execution Duration:
drop table ZZT16JMSID1MD000
Pass18 - Execution Duration:
drop table ZZT3M2QSNWDMD001
Pass19 - Execution Duration:
drop table ZZTDMBESMXPOJ002
Pass20 - Execution Duration:
drop table ZZT9MCAB4STOD003
Pass21 - Execution Duration:
drop table ZZTMYCAAR65MD004
0:00:00.00
0:00:00.00
0:00:00.00
0:00:00.00
0:00:00.00
0:00:00.00
•
Pass0 and Pass1 calculate the Revenue {Year} metric.
•
Pass2 calculates the inner portion of the YearNumber metric, assigning the integer 1 to
each year available in the LU_YEAR lookup table. Note that this metric was defined by
adding Year dimensionality with filtering set to None. This forces the metric into its own
pass of SQL, to ensure that all years in the lookup table are numbered, not just the years
with data in the fact table.
•
Passes 3 to 14 calculate the metrics YearNumber, SlopeMetric, and InterceptMetric.
The Intelligence Server performs the calculations based on the data in the temporary
table from Pass0, Pass1, and Pass2. Values are calculated and inserted back into the
temporary tables.
•
In Pass15, the Forecast metric is calculated based on the information from all of the
previous calculations. The mx+b calculation is performed as part of the SQL statement.
•
The remaining passes prepare the report and drop the temporary tables.
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2
STANDARD FUNCTIONS
The standard functions in this chapter range from simple operations like Sum and Product
to internal functions that allow you to define your own metrics, facts, and filters. Taken as a
set, they provide a powerful toolset for performing common mathematical calculations,
grouping data, examining correlation, validating data, and formatting report information for
display.
The following categories of functions are covered:
•
Basic functions, page 92
•
Date and time functions, page 116
•
Internal functions, page 130
•
NULL/Zero functions, page 145
•
OLAP functions, page 147
•
Rank and NTile functions, page 202
•
String functions, page 220
Each section in this chapter describes a function category and presents a list of functions
within each category. Each section also presents the data necessary to understand and
implement each individual function. The information provided for each function includes
•
An explanation of the data returned by the function
•
The syntax of the function including function name, available parameters, the parameter
setting defaults, and the types of data that can be used with the function
•
The mathematical expression illustrating exactly how the calculation is defined in
MicroStrategy (if applicable)
•
Usage notes describing any error conditions, invalid data types, or key information to
know before using the function (if applicable)
•
An example of the function in use; this may be either a report example or a simple text
description of the data returned based on the specified input
Note the following:
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•
The report examples in this section use objects and data found in the MicroStrategy
Tutorial.
•
See Appendix A, MicroStrategy and Database Support for Functions, for a list of the
databases and the functions they support.
Basic functions
These are basic mathematical functions like Avg, Greatest, Least, Max, Min, and so on, and
are among the most commonly used functions.
Add
Returns the sum of two or more values. The Add function differs from the Sum function (see
Sum , page 113), which returns the sum of values in a single value list. Add can take multiple
metrics as inputs and add the values of the metrics.
You can also construct these types of metrics using the plus operator (see Plus (+), page
239) instead of the Add function.
Syntax
Add(Argument1, Argument2,..., ArgumentN)
Where
The arguments must be metrics or constants.
Example
You can use the Add function to return the sum of related metrics. In the report shown
below, the Total Paid Compensation metric uses the formula Add([Paid Bonus],
[Paid Salary]) to return the total compensation paid to each employee. This report was
created in the MicroStrategy Human Resources Analysis Module.
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Average
Average performs a sum of a set of values and divides this total by the number of values in
the set. This returns the average (also known as arithmetic mean) for the set of values.
The Average function differs from the Avg function (see Avg (average), page 93), which
returns the average of values in a single value list. The Average function can take multiple
metrics as inputs and average the values of the metrics. You can use this function to
compute and display the average of two or more metrics on a report.
Syntax
Average(Argument1, Argument2,..., ArgumentN)
Where:
The arguments must be metrics or constants.
Avg (average)
Avg calculates the sum of a single value list and divides the result by the number of values in
the list. This returns the average (arithmetic mean) of the listed values.
Avg is often used to create subtotals and metrics based on fact data. This is a group-value
function.
To calculate an average while applying a weight to the values, see WeightedMean, page
197.
Syntax
Avg<Distinct, FactID, UseLookupForAttributes>(Argument)
Where:
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
Distinct is a TRUE/FALSE parameter that allows you to use all values in the
calculation or to calculate using only the unique values.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
UseLookupForAttributes is a TRUE/FALSE parameter that can be used when
performing an aggregation of an attribute. The Count function is most commonly used
to aggregate attributes. For information on this parameter, including an example of using
it with the Count function, see Count , page 95.
Usage notes
The Avg function ignores NULL values but uses zero values in its calculation.
Example
Example 1: In this simple example the average of a value list containing the values (4,9,2,9)
is calculated as follows:
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(4+9+2+9)/4 = 6
Example 2: This report obtains the average salary for employees in each region. The report
contains the attributes Region, Employee, and Salary as well as the metric Average Salary.
A report filter limits the regions displayed to Northeast and Southeast. The metric Average
Salary is defined as follows: Avg(Salary){Region}
The function sums all salaries within a region and divides by the number of values, resulting
in the regional average salary.
For further examples of using the Avg function, see Example 1: Transformed fact, page 22
and Example 2: Compound metric, page 23, as well as Example 1: Average, page 23.
Condition
Condition is a shortcut function, available for various features in MicroStrategy Web, that
allows you to easily define the condition (filtering) of the final metric expression. A conditional
metric allows you to apply a filter to only one metric on a report without affecting the other
metrics. The metric filter can be either a filter or a prompt that returns a list of filters to choose
from. Only one filter or prompt can be associated with each metric, but the metric filter can
contain multiple qualifications. For additional information on when to create conditional
metrics, see the Advanced Reporting Guide.
Once you select the Condition shortcut function, you can then select the group function
for the calculation, such as Sum or Max. Additionally, the options to add or remove a
condition are displayed:
•
To add a condition to the metric, click the Browse icon (
that returns a list of filters.
•
To remove the condition from a metric, click the Delete icon ( ).
) to select a filter or a prompt
You can also define how the condition is evaluated by clicking Condition advanced
options:
•
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By default, the parts of the report filter that are based on parent or child attributes of the
attribute in the metric condition are ignored, and do not affect the calculation of the
conditional metric. To apply all criteria in the report filter to the conditional metric, clear
the Ignore related report filter elements check box. This option is selected by
default.
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•
By default, the report filter is applied to the metric data and then the metric filter is applied
to those results. You can determine the order in which the report filter and metric filter
are applied to the metric. From the Interaction between metric filter and
report filter drop-down list, select one of the following:
▫
To apply the report filter criteria first, then apply the metric filter to the results, select
Merge report filter into metric (default). For example, the metric filter is
revenue greater than $100 and the report filter is bottom 10 items for sales. In this
example, the report filter narrows the result set to only 10 items, and then the metric
condition filters out, from those 10 items, any items with a revenue above $100.
▫
To evaluate the metric filter first, then apply the report filter to the results, select
Merge metric condition into report. Using this option with the example
above, the metric condition returns all items with a revenue above $100. The report
filter then filters out all but the bottom 10 of those items, based on sales.
▫
To combine the metric filter and the report filter, select Merge into new. Only
those results that meet both the metric filter and the report filter are included in the
metric. Using this option with the example above, the two filters are merged, so that
only those items that are in the bottom 10 in terms of sales and that have sales
greater than $100 are included.
Count
Count returns the number of elements in a list of values. This is a group-value function.
Syntax
Count<Distinct, FactID, UseLookupForAttributes, Null> (Argument)
Where:
•
Argument is an attribute, fact, or metric representing a list of values.
•
Distinct is a TRUE/FALSE parameter that allows you to count all elements in a list or
only the unique elements.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
UseLookupForAttributes is a TRUE/FALSE parameter that can be used when
performing an aggregation of an attribute:
▫
If you set this parameter to TRUE, the aggregation is done for the unique set of
attribute elements, excluding any duplicates caused by additional attributes on the
report.
▫
If you set this parameter to FALSE, the aggregation is done for all elements of an
attribute, including duplicate elements that can be included by displaying additional
attributes on a report.
For an example of how to use this parameter, see Performing counts of attributes, page
96.
Example
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This example creates a report that calculates the count of employees in each region. The
report contains the attributes Region and Employee, and the metric # of Employees. A report
filter limits the regions displayed to Central, South, Southeast, and Southwest. The metric #
of Employees is defined as follows: Count(Employee){Region, ~}.
The function counts each entry within a region and returns the last number in the count.
Performing counts of attributes
While most functions are commonly used with facts and metrics, the Count function is
commonly used to count the number of elements for an attribute. For example, the report
shown below displays the customer revenue for each item they purchased.
This report also uses two derived metrics to display the following count information:
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•
Number of Customers: This derived metric displays the count of customers. The
expression used to define the derived metric is
Count<UseLookupForAttributes=True>(Customer){}. By defining the
parameter UseLookupForAttributes=True, the Count function ignores any
multiple listings of each Customer attribute element. In the report shown above, each
separate item is ignored and only the unique attribute elements for Customer are
counted.
•
Number of Customer Purchases: This derived metric displays the count of
customer purchases. The expression used to define the derived metric is
Count<UseLookupForAttributes=False>(Customer){}. By defining the
parameter UseLookupForAttributes=False, the Count function counts each
listing of the Customer attribute element. In the report shown above, each separate item
is counted, which provides a count of customer purchases.
For information on creating derived metrics, along with other OLAP Services features, see
the In-memory Analytics Guide.
First
Returns the result of an aggregate applied over a set of rows that ranks as the first within a
specified order. This is a group-value function that shares the sort by capability of the OLAP
functions.
Syntax
First<FactID, SortBy>(Argument)
Where:
•
Argument is an attribute, fact, or metric representing a list of values.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
SortBy is a parameter that allows you to define the attribute or metric on which to sort
the data.
Example
The First function is often used to define subtotals. In this example, the First function defines
the subtotal for the metric units received, returning the units received for the first quarter
containing a value in 2003. The expression defines the subtotal as follows:
First<SortBy= (Day, Month, Quarter, Year) >([Units
Received]){@}
Argument is the metric to which the subtotal is applied.
For detailed instructions on creating and applying user-defined subtotals, see the
MicroStrategy Advanced Reporting Guide.
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GeoMean (geometric mean)
This function can be used to find the average for a set of values contributing to a product.
While the Avg function is used to find the arithmetic mean for a set of values contributing to a
sum, GeoMean can be used to determine the average growth rate for a given compound
interest with variable rates.
This function takes the product of a set of values of size N and returns the Nth root of the
result (also known as geometric mean). GeoMean is a group-value function.
Syntax
GeoMean <FactID>(Argument)
Where:
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
Expression
Usage notes
This function returns an error if a value in the value list is negative.
Example
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Suppose you have an investment that earns 10% the first year, 60% the second year, and
20% the third year, and you want to answer the question “What is its average rate of return?”
You do not want to use the Avg function to obtain the arithmetic mean, because these
numbers show that in the first year your investment was multiplied by 1.10, in the second
year by 1.60, and in the third year by 1.20. The average is the geometric mean of these three
numbers. This can be understood as looking for a constant that you can multiply each year’s
investment by, and get the same result as multiplying the first year by 1.10, the second year
by 1.60, and so forth. This constant is the geometric mean.
(1.10 * 1.60 * 1.20)1/3 = 1.283
The geometric mean is 1.283, so the average rate of return is about 28%.
Greatest
Returns the larger of two or more values. The Greatest function differs from the Max
function, which returns the largest value in a single value list. Greatest can take multiple
lists as input and compare the elements in the lists. It is used for comparisons between
metrics.
Syntax
Greatest(Argument1, Argument2,..., ArgumentN)
Where:
The arguments must be metrics.
Example
This simple example illustrates how data is returned by the Greatest function.
Given two value lists, the function compares the values in each position in the list and returns
a list containing the highest numbers from each position.
List 1
List 2
Result List
21
50
50
18
3
18
42
22
42
30
6
30
7
20
20
Histogram Median
Histogram Median is a distributive implementation to calculate approximation of median over
large datasets.
Formula
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Where:
Lm is the lower limit of the median bar.
n is the total number of observations in the set.
Fm-1 is the total number of observations in all bars below the median bar.
fm is the frequency of the median bar.
c is the median bar width.
Syntax
HistogramMedian([ValueList], NumberOfHistograms)
Where:
ValueList is an attribute, fact, or metric representing a list of values.
NumberOfHistograms represents the histograms to be analyzed.
Example
This example shows a report to find the median Revenue per Customer for each Income
Bracket in the Central Region.
HistogramMedian([Max Revenue per Customer], [Max Revenue per
Customer]) {~ , Region}
Last
The Last function returns the last value in a sorted set of values. It is often used to define
subtotals, as shown in the example below. This is a group-value function.
Syntax
Last<FactID, SortBy>(Argument)
Where:
•
100
Argument is an attribute, fact, or metric representing a list of values.
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•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
SortBy is a parameter that allows you to define the attribute or metric on which to sort
the data.
Example
In this example, Last is used in the subtotal for the metric End on Hand, returning the total
items on hand in the last quarter of 2003. The subtotal expression is defined as follows:
Last<SortBy= (Day, Month, Quarter, Year) >(End on Hand)
{@ }
For detailed instructions on creating and applying user-defined subtotals, see the
MicroStrategy Advanced Reporting Guide.
Least
This function returns the smaller of two or more values. The Least function differs from the
Min function, which returns the smallest value in a single value list. Least can take multiple
lists as input and compare the elements of the lists. It is used for comparisons between
metrics. This is a single-value function.
Syntax
Least(Argument1, Argument2,..., ArgumentN)
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Where:
The arguments must be metrics.
Example
This simple example illustrates how data is returned by the Least function.
Given two values lists, the function compares the values at each position in the lists and
returns a list of the lowest values from each position.
List 1
List 2
Result List
21
50
21
18
3
3
42
22
22
30
6
6
7
20
7
Level
Level is a shortcut function, available for various features in MicroStrategy Web, that allows
you to easily define the level (dimensionality) of the final metric expression. The level of a
metric determines the conceptual level at which a calculation is performed. For example,
rather than just calculating your revenue, you want to specify that a metric always calculates
yearly revenue. For additional information on when to create level metrics, see the Advanced
Reporting Guide.
Once you select the Level shortcut function, you can then select the group function for the
calculation, such as Sum or Max. Additionally, the options to add and remove levels are
displayed:
•
Report Level is the default level. If you remove Report Level as a level of the metric, you
can add it back by typing Report Level in the level text field.
•
To add a level to the metric, click the Browse icon (
•
To remove a level from a metric, click the Delete icon ( ).
) to select an attribute.
You can also define how levels are evaluated:
•
To define the level options, for each level, click the Level options icon (
Drop-down List
From the Relationship
with Report Filter
drop-down list, you can
define how the report
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).
Level Options
To include only data that meets the conditions in the report filter in the
metric calculation, select Standard filtering.
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Drop-down List
filter affects the metric
calculation.
Level Options
To raise the level of the report filter to the level of the target, if possible,
then apply the report filter to the metric calculation, select Absolute
filtering. For example, the report filter contains the Washington, DC,
Boston, and New York call centers, but the Revenue metric is calculated at
the Region level. Because Call Center is a child attribute of Region, the
report filter's level is raised to the Region level, and the report filter is
treated as if it includes the regions that contain Washington, DC, Boston,
and New York (in this case, Mid-Atlantic and Northeast). Data from all call
centers in the Mid-Atlantic or Northeast regions are included in the metric
calculation, including call centers that are not Washington, DC, Boston, or
New York.
To ignore any conditions in the report filter that are based on the target
attribute, as well as any parent or child attributes of the target, select
Ignore filtering. For example, if you have a regional revenue metric on
a report, any conditions based on Country, Region, or Call Center in the
report filter are ignored when calculating the metric. However, a condition
based on Year would not be ignored, since Year is not directly related to
Region.
To allow the target and group components of the level to define the filter,
select None .
From the Metric
Aggregations dropdown list, you can
determine how the
metric is grouped, or
aggregated, when
displayed on a report.
To group data in the metric by the attribute level of the target, select
Standard.
To exclude the target attribute from being used to group data in the metric,
select None . Any children of the target attribute are also excluded. This
option is available for metrics calculated at a set level, as opposed to the
report level.
The following options are only used for nonaggregatable metrics. A
nonaggregatable metric, such as an inventory metric, is one that should not
be aggregated across an attribute. For example, if you have monthly
inventory numbers in your data warehouse and want to calculate the yearly
inventory, adding the monthly numbers together does not provide a useful
business measure. Instead, you may want to use the end-on-hand and
beginning-on-hand inventory numbers to see how the total inventory
changed during the year. The following options are available:
• To use the first value in the lookup table, select Beginning lookup.
• To use the last value in the lookup table, select Ending lookup.
• To use the first value in the fact table, select Beginning fact.
• To use the last value in the fact table, select Ending fact.
•
To define the advanced level options, such as whether to apply the metric filter to the
metric calculation, click Level advanced options:
▫
Allow other users to add extra units to this definition: This option is
used to emulate MicroStrategy 6.x behavior and affects only those projects that
have been upgraded from 6.x. Clear the check box only if your project was
upgraded from MicroStrategy 6.x. This option is selected by default.
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Include filter attributes which are not in report or level in metric
calculation: Determine whether to apply the metric filter when the metric is
calculated. If this option is cleared, filter attributes that are not on the report or in the
level of the metric are not included in the metric calculation. This options lets you
determine which parts of the metric filter are applied based on the data that has
been included on the report. This option can help you re-use the same metric in
multiple reports, eliminating the need to create and maintain multiple metrics,
particularly if the metric and filter qualifications are complex. For an example,
including an explanation of how the report SQL is affected by this option, see the
Advanced Reporting Guide. This option is selected by default.
▫
Max (maximum)
Max returns the largest value in a list of values. For example, it can be used on a list of prices
to determine the maximum cost of an item. This is a group-value function.
To compare the highest values in multiple lists of values, use the Greatest function.
Syntax
Max <FactID, UseLookupForAttributes>(Argument)
Where:
•
Argument is an attribute, fact or metric representing a list of numbers.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
UseLookupForAttributes is a TRUE/FALSE parameter that can be used when
performing an aggregation of an attribute. The Count function is most commonly used
to aggregate attributes. For information on this parameter, including an example of using
it with the Count function, see Count , page 95.
Example
This example shows a report displaying the cost range of items within a subcategory. The
Max function is used to determine the highest cost of an item within a subcategory. The
metric, Maximum Unit Cost, is defined as follows:
Maximum([Unit Cost]) {~}
The metric is placed on a report with the attributes Category and Subcategory and the metric
Minimum Unit Cost. The resulting report appears as follows:
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Median
Median returns the value in the middle of a set of listed values. The result will be greater
than half the values in the list and less than the other half. The median can be used as an
alternative to the arithmetic mean when handling values that are not evenly distributed or
contain outliers. This is a group-value function.
Syntax
Median <FactID>(Argument}
Where:
•
Argument is an attribute, fact or metric representing a list of numbers.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
Usage notes
This function provides a location measure: the value returned has a relative position with
regard to other values in the list. Mode is another function that provides a location measure.
Example
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This example shows a report built to obtain the median age of employees within each region.
The report includes the attributes Region, Employee, and Employee Age, and the metric
Median Age. A report filter limits the regions displayed to Mid Atlantic, Northeast, and
Southeast. The metric Median Age is defined as follows:
Median([Employee Age){Region,~}
The function evaluates the list of employee ages within a region and selects a value in the
middle of the value list.
For another example of using the Median function, see Example 2: Median, page 23.
Min (minimum)
Min returns the lowest value in a list of values. For example, it can be used on a list of prices
to determine the minimum cost of an item. This is a group-value function.
To compare the lowest values in multiple lists of values, use the Least function.
Syntax
Min <FactID, UseLookupForAttributes>(Argument)
Where:
106
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
UseLookupForAttributes is a TRUE/FALSE parameter that can be used when
performing an aggregation of an attribute. The Count function is most commonly used
to aggregate attributes. For information on this parameter, including an example of using
it with the Count function, see Count , page 95.
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Example
This example shows a report displaying the cost range of items within a subcategory. The
Min function is used to determine the lowest cost of an item within a subcategory. The metric
Minimum Unit Cost is defined as follows:
Minimum([Unit Cost]) {~}
The metric is placed on a report with the attributes Category and Subcategory and the metric
Maximum Unit Cost.
Mode
Returns the most frequently occurring value in a given list. This is a group-value function.
Syntax
Mode <FactID, IsRemovable>(Argument)
Where:
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
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•
IsRemovable is a parameter that determines whether the Mode of a single value
returns that value or a NULL value, as described below:
▫
IsRemovable=True returns the single value as the result of the Mode function.
For example, Mode<IsRemovable=True>(4) would return the value 4.
▫
IsRemovable=False returns a NULL value as the results of the Mode function
when a single value is supplied. For example, Mode<IsRemovable=False>(4)
would return NULL.
If multiple values are supplied to the Mode function, the IsRemovable parameter has
no effect on the result of the Mode function.
•
Argument is an attribute, fact, or metric representing a list of numbers.
Usage notes
•
This function provides a location measure: the value returned has a relative position with
regard to other values in the list. Median is another function that provides a location
measure.
•
Returns a NULL if there are no recurring values in the range or list.
Example
This example shows a report that retrieves the mode of the unit cost for items in a
subcategory. The report returns the most frequently repeated unit cost within the various
subcategories, and, in the cases of Electronics, TVs, and Video Equipment, it returns a
NULL value, because there are no recurring unit cost values for them. The report contains
the attributes Category and Subcategory, and the metric Mode Unit Cost. A report filter limits
the categories displayed to Books and Electronics. The Mode Unit Cost metric is defined as
follows:
Mode([Unit Cost])
The formula listed above uses the Unit Cost fact rather than the Unit Cost metric.
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Multiply
Multiply returns the product of two or more values. This function differs from the
Product function (see Product, page 109), which returns the product of values in a single
value list. The Multiply function can take multiple metrics as inputs and multiply the values
of the metrics.
You can also construct these types of metrics using the times operator (see Times (*), page
239) rather than the Multiply function.
Syntax
Multiply(Argument1, Argument2,..., ArgumentN)
Where:
The arguments must be metrics or constants.
Example
In the report shown below, the Unit Profit metric uses the formula Multiply([Unit
Price],[Units Sold]) to return the revenue for each item. This report was created in
the MicroStrategy Tutorial project.
Product
Multiplies all values in a list. This is a group-value function.
Syntax
Product<FactID>(Argument)
Where
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
Example
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Given a value list containing the values (1,2,3,4), the product calculates as 1*2*3*4 = 24.
StDevP (standard deviation of a population)
The standard deviation is a value which shows how widely values in a population differ from
the mean. It is useful for comparing different sets of values with a similar mean.
StDevP returns the standard deviation of a population represented by list of values. It is a
group-value function.
Syntax
StDevP <FactID >(Argument)
Where:
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
UseLookupForAttributes is a TRUE/FALSE parameter that can be used when
performing an aggregation of an attribute. The Count function is most commonly used
to aggregate attributes. For information on this parameter, including an example of using
it with the Count function, see Count , page 95.
Expression
Usage notes
•
For this function, arguments correspond to an entire population as opposed to a
population sample. For population samples, see StDev (standard deviation of a sample),
page 111.
•
When very large population samples are used, this function and the StDev (standard
deviation of a sample), page 111 function return approximately equal results.
Example
This example shows a report where the standard deviation of the revenue is calculated. This
calculation is based on the assumption that the list of values supplied in the metric represents
the entire population of the data for which you want to obtain the standard deviation. The
calculation is based on the revenue values for each state within a region and calculated at
the region level.
Compare this example report to the example for StDev to see the different values returned
when calculating for a population as opposed to a sample.
The report contains the attributes Customer Region and Customer State, and the metrics
Total Revenue and StDevP. A report filter limits the regions displayed to South, Northwest,
and Southwest. The definition of the StDevP metric is as follows:
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StDevP([Total Revenue]){[Customer Region], ~}
StDev (standard deviation of a sample)
The standard deviation is an indicator of how widely values in a group differ from the mean. It
is useful for comparing different sets of values with a similar mean.
StDev returns the standard deviation of a population based on a sample. This is a groupvalue function.
Syntax
StDev <Distinct, FactID>(Argument)
Where:
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
Distinct is a TRUE/FALSE parameter that allows you to calculate using all values in
the list or only the unique values.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
UseLookupForAttributes is a TRUE/FALSE parameter that can be used when
performing an aggregation of an attribute. The Count function is most commonly used
to aggregate attributes. For information on this parameter, including an example of using
it with the Count function, see Count , page 95.
Expression
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Usage notes
•
In this function, arguments correspond to a population sample as opposed to the entire
population. For entire populations, see StDevP (standard deviation of a population),
page 110.
•
To perform a weighted standard deviation, see WeightedStDev (weighted standard
deviation of a sample), page 199.
•
When very large population samples are used, this function and the StDevP (standard
deviation of a population), page 110 function return approximately equal results.
Example
This example shows a report where the standard deviation of the revenue is calculated. This
calculation is based on the assumption that the list of values supplied in the metric represents
a sample of the data for which you want to obtain the standard deviation. The calculation is
based on the revenue values for each state within a region and calculated at the region level.
Compare this example report to the example for StDevP to see the different values returned
when calculating for a population as opposed to a sample.
The report contains the attributes Customer Region and Customer State, and the metrics
Total Revenue and StDev. A report filter limits the regions displayed to South, Northwest,
and Southwest. The definition of the StDev metric is as follows:
StDev([Total Revenue]){[Customer Region], ~}
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Sum
Returns the sum of all numbers in a list of values. This function is commonly used in metrics
and subtotals. Sum is a group-value function.
Syntax
Sum <Distinct, FactID, UseLookupForAttributes>(Argument)
Where:
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
Distinct is a TRUE/FALSE parameter that allows you to calculate using all values in
the list or only the unique values.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
UseLookupForAttributes is a TRUE/FALSE parameter that can be used when
performing an aggregation of an attribute. The Count function is most commonly used
to aggregate attributes. For information on this parameter, including an example of using
it with the Count function, see Count , page 95.
Example
In this example, the metric Regional Revenue is defined as follows:
Sum(Revenue) {[Customer Region], ~}
This simple report uses the attributes Customer Region and Customer State, and the
metrics Revenue and Regional Revenue to generate a report showing the sum of the
revenue for several regions. A report filter limits the regions displayed to Northeast,
Northwest, and Southeast.
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Transformation
Transformation is a shortcut function, available for various features in MicroStrategy
Web, that allows you to easily define transformations for the final metric expression.
Transformations allow you to apply an attribute-element based offset to compare metric
data. For example, a transformation metric can help a user compare last month's revenue to
this month's revenue. Although transformations can be applied to any attribute hierarchy, the
Time hierarchy is used most often. For the Time hierarchy, the offset can be set as a fixed
number of days, weeks, months, or years. For additional information on when to create
transformation metrics, see the Advanced Reporting Guide.
Once you select the Transformation shortcut function, you can then select the group
function for the calculation, such as Sum or Max. Additionally, the options to add or remove a
transformation are displayed:
•
To add a transformation to the metric, click the Browse icon (
transformation.
•
To remove a transformation from a metric, click the Delete icon ( ).
) to select a
VarP (variance of a population)
Variance is a measure of how spread out a set of values is.
VarP returns this measure based on an entire population. This is a group-value function.
Syntax
VarP <FactID>(Argument)
Where:
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
Expression
Usage notes
For this function, arguments relate to an entire population as opposed to a population
sample.
Example
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Variance is calculated as the average squared deviation of each number from its mean. This
example creates a report in which the variance of the units sold in the subcategories within a
category is calculated with the assumption that the data provided represents the entire
population.
Compare this example report to the example for variance of a sample to see the different
values returned when calculating for a population as opposed to a sample.
The report includes the attributes Category and Subcategory, and the metrics Units Sold
and VarP by Category. A report filter limits the categories displayed to Books and Movies.
The VarP by Category metric is defined as follows:
VarP([Units Sold]) {Category, ~}
Var (variance of a sample)
Variance is a measure of how spread out a set of values is. It is calculated as the average
squared deviation of each number from its mean.
This function calculates the variance based on a sample of a population. This is a groupvalue function.
Syntax
Var <Distinct, FactID>(Argument)
Where:
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
Distinct is a TRUE/FALSE parameter that allows you to calculate using all values in
the list or only the unique values.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
Expression
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Usage notes
Assume when using this function that arguments constitute a population sample, as opposed
to a entire population.
Example
This example creates a report in which the variance of the units sold in the subcategories
within a category is calculated, with the assumption that the data provided is a sample.
Compare this example report to the example for variance of a population to see the different
values returned when calculating for a population as opposed to a sample.
The report includes the attributes Category and Subcategory, and the metrics Units Sold
and Var by Category. A report filter limits the categories displayed to Books and Movies. The
Var by Category metric is defined as follows:
Var([Units Sold]) {Category, ~}
Date and time functions
This section describes the date and time functions. These functions are not supported by the
Analytical Engine, so they must be calculated by the database.
The MicroStrategy Analytical Engine does not calculate date and time functions. If your
database does not include SQL syntax support for a date and time function, the function
cannot be calculated in your environment. For information on whether your database
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supports various date and time functions, see Appendix A, MicroStrategy and Database
Support for Functions.
The results returned by the date/time functions may differ depending on the way your
database stores and interprets dates, for example, whether Monday is considered the first or
second day of the week.
AddDays
AddDays is used to calculate dates that occur N days before or after a given date. It returns
this information in the form of a date or timestamp.
Syntax
AddDays(Date/Time,Offset)
Where:
•
Date/Time is the input date or timestamp.
•
Offset is an integer number of days to add.
For information on whether your database supports various date and time functions, see the
Appendix A, MicroStrategy and Database Support for Functions.
Example
AddDays('2004-07-29', 4) = 2004-08-02
AddDays('2004-07-29 02:00:00', 4) = 2004-08-02 02:00:00
AddDays('2004-03-01', -4) = 2004-02-26
AddMonths
AddMonths is used to calculate dates that occur N months before or after a given date. It
returns this information in the form of a date or timestamp.
If the new date does not occur in the new month, the last date of the new month is returned.
Syntax
AddMonths(Date/Time,Offset)
Where:
•
Date/Time is the input date or timestamp.
•
Offset is an integer number of months to add.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
AddMonths('2004-07-29', 3) = 2004-10-29
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AddMonths('2004-07-29 10:00:00', 3) = 2004-10-29 10:00:00
AddMonths('2003-03-31', -1) = 2003-02-28
CurrentDate
Returns the current date as provided by the database timer.
This function does not take input variables.
Syntax
CurrentDate()
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
CurrentDateTime
Returns the current date and time as provided by the database timer.
This function does not take input variables.
Syntax
CurrentDateTime()
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
CurrentTime
Returns the current time as provided by the database timer.
This function does not take input variables.
Syntax
CurrentTime()
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Date
Returns only the date portion of the date-time column. The time is truncated, not rounded.
Syntax
Date(Date/Time)
Where:
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Date/Time is the input date or timestamp.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 2004-07-29; output is the date 2004-07-29.
Input is the date-time 2004-07-29 02:00:00; output is the date 2004-07-29.
DateDiff
DateDiff is used to calculate the length of time between two dates. A numeric value is
returned.
Syntax
DateDiff<firstWeekDay>(Date1, Date2, Unit)
Where:
•
Date1 and Date2 are the inputs used for date or timestamp values. You can use
metrics, constants, attribute forms, or functions that result in a date or timestamp. For
example, you can include CurrentDate() as an input to compare historical dates from an
attribute form to the current date.
•
Unit is the unit of time that is to be measured. You can return the length of time in one of
the following units:
Unit of time
•
Value in the function expression
Seconds
“s”
Minutes
“mn”
Hours
“h”
Days
“d”
Weeks
“w”
Months
“m”
Quarters
“q”
Years
“y”
firstWeekDay (default value is 1) is a parameter that can be used if you are returning
the length of time in weeks. This parameter determines which day of the week is
considered as the first day of the week, so that the difference in weeks can be accurately
determined. You can type an integer value from 1 to 7, with 1 representing Sunday, 2
representing Monday, and so on until 7 representing Saturday.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
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Example
You can use DateDiff to create a metric or attribute form that lists the length of time between
two dates. One common way to do this is to compare the date information you have stored to
the current date. Consider a report or dashboard that includes a Day attribute with a single
ID form. You can create a metric with the following definition:
DateDiff(Day@ID, CurrentDate(), “d”)
For each element of the Day attribute, the metric displays the number of days between that
date and the current date when running the report or dashboard.
When using the DateDiff function in MicroStrategy Web, you will need to replace the Day@ID
attribute in the function definition. Create the metric [MAX(Day@ID)] and define the function
as follows: DateDiff([MAX(Day@ID)], CurrentDate(), “d”)
DayofMonth
Returns a number corresponding to the day of the month of the date provided. The value
returned is an integer between 1 and 31.
Syntax
DayofMonth(Argument)
Where:
Argument is a metric representing a list of dates.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 7/11/2002; output is the integer 11.
Input is the date 2003/10/10; output is the integer 10.
DayofWeek
Returns the number of the day in the week corresponding to the input date. The return value
is an integer between 1 and 7. The value of 1 represents Sunday and the value of 7
represents Saturday.
Syntax
DayofWeek(Argument)
Where:
Argument is a metric representing a list of dates.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
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Input is the date 5/16/2003; output is the integer 6, which represents Friday.
Input is the date 2003/9/9; output is the integer 3, which represents Tuesday.
DayofYear
Returns the number of the day in the year of the input date. The return value is an integer
between 1 and 365.
Syntax
DayofYear(Argument)
Where:
Argument is a metric representing a list of dates.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 1/16/2003; output is the integer 16.
Input is the date 2/17/2003; output is the integer 48.
DaysBetween
Returns the difference in days between two given dates as an integer value. The calculation
of the difference is based on the number of day boundaries crossed, not the number of
twenty-four hours periods elapsed. If the first date argument is later than the second date
argument, the result is a negative number. The result does not display the time stamps, as
they are truncated before performing the calculation.
Syntax
DaysBetween(Date/Time1, Date/Time2)
Where:
•
Date/Time1 is the start date.
•
Date/Time2 is the end date.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Examples
Inputs are the dates 2004-07-29 and 2004-07-25; output is the integer -4.
Inputs are the dates 2004-07-29 02:00:00 and 2004-07-31 01:00:00; output is
the integer 2.
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FiscalMonth
Returns the numeric position of a month within a fiscal year, for a given input date. This
function is useful in financial reporting when the start of the fiscal year is different than the
start of the calendar year.
Syntax
FiscalMonth<firstMonth>(Date/Time)
Where:
•
Date/Time is the input date or timestamp.
•
firstMonth (default value is 1) is a parameter that determines which month is
considered as the start of the fiscal year. You can type an integer value from 1 to 12, with
1 representing January, 2 representing February, and so on until 12 representing
December.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Consider a report or dashboard that includes a Day attribute with a single ID form. You can
create a metric with the following definition:
FiscalMonth<firstMonth=4>(Day)
For each element of the Day attribute, the metric displays the numeric position of the month
within the fiscal year for that date. For this example, since the fiscal year starts in April, a date
of July 4, 2014 would return 4. This is because July is the fourth month in the fiscal year.
FiscalQuarter
Returns the numeric position of a quarter within a fiscal year, for a given input date. This
function is useful in financial reporting when the start of the fiscal year is different than the
start of the calendar year.
Syntax
FiscalQuarter<firstMonth>(Date/Time)
Where:
•
Date/Time is the input date or timestamp.
•
firstMonth (default value is 1) is a parameter that determines which month is
considered as the start of the fiscal year. You can type an integer value from 1 to 12, with
1 representing January, 2 representing February, and so on until 12 representing
December.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
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Consider a report or dashboard that includes a Day attribute with a single ID form. You can
create a metric with the following definition:
FiscalQuarter<firstMonth=7>(Day)
For each element of the Day attribute, the metric displays the numeric position of the quarter
within the fiscal year for that date. For this example, since the fiscal year starts in July, a date
of October 13, 2014 would return 2. This is because October is in the second quarter of the
fiscal year.
FiscalWeek
Returns the numeric position of a week within a fiscal year, for a given input date. This
function is useful in financial reporting when the start of the fiscal year is different than the
start of the calendar year.
Syntax
FiscalWeek<firstWeekDay, firstMonth>(Date/Time)
Where:
•
Date/Time is the input date or timestamp.
•
firstWeekDay (default value is 1) is a parameter that determines which
day of the week is considered as the first day of the week. You can type an
integer value from 1 to 7, with 1 representing Sunday, 2 representing
Monday, and so on until 7 representing Saturday.
•
firstMonth (default value is 1) is a parameter that determines which month is
considered as the start of the fiscal year. You can type an integer value from 1 to 12, with
1 representing January, 2 representing February, and so on until 12 representing
December.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Consider a report or dashboard that includes a Day attribute with a single ID form. You can
create a metric with the following definition:
FiscalWeek<firstWeekDay=1, firstMonth=7>(Day)
For each element of the Day attribute, the metric displays the numeric position of the week
within the fiscal year for that date. For this example, since the fiscal year starts in July, a date
of July 8, 2014 would return 2. This is because the first fiscal week runs from July 1st through
July 5th . Then on July 6th , the first Sunday of the fiscal year, the second fiscal week starts.
This week includes July 8th , and so 2 is returned.
FiscalYear
Returns the fiscal year of the input date. This function is useful in financial reporting when the
start of the fiscal year is different than the start of the calendar year.
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When determining the fiscal year, the year returned is the year in which the fiscal year ends.
For example, if a fiscal year runs from March 1, 2014 through April 30, 2015, the fiscal year is
2015.
Syntax
FiscalYear<firstMonth>(Date/Time)
Where:
•
Date/Time is the input date or timestamp.
•
firstMonth (default value is 1) is a parameter that determines which month is
considered as the start of the fiscal year. You can type an integer value from 1 to 12, with
1 representing January, 2 representing February, and so on until 12 representing
December.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Consider a report or dashboard that includes a Day attribute with a single ID form. You can
create a metric with the following definition:
FiscalYear<firstMonth=2>(Day)
For each element of the Day attribute, the metric displays the fiscal year for that date. A date
of July 4, 2013 would have a fiscal year of 2014.
Hour
Returns the integer value for the hour of the input time. The return value is an integer
between 0 and 23.
Syntax
Hour(Argument)
Where:
Argument is a metric representing a list of dates and times.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the time 9:45 am; output is the integer 9.
Input is the time 11:10 pm; output is the integer 23.
Millisecond
Returns the integer value for the millisecond of the input time.
Syntax
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Millisecond(Argument)
Where:
Argument is a metric representing a list of dates and times.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Minute
Returns the integer value for the minute of the input time. The return value is an integer
between 0 and 59.
Syntax
Minute(Argument)
Where:
Argument is a metric representing a list of dates and times.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the time 9:45 am; output is the integer 45.
Input is the time 11:10 pm; output is the integer 10.
Month
Returns the number of the month in the year of the input date. The return value is an integer
between 1 and 12.
Syntax
Month(Argument)
Where:
Argument is a metric representing a list of dates.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 5/16/2003; output is the integer 5.
Input is the date 2003/9/3; output is the integer 9.
MonthEndDate
Returns the date of the last day of the month in which a date or timestamp occurs.
Syntax
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MonthEndDate(Date/Time)
Where:
Date/Time is the input date or timestamp.
For information on whether your database supports various date and time functions,see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 2004-07-29; output is the date 2004-07-31.
MonthStartDate
Returns the date of the first day of the month in which a date or timestamp occurs.
Syntax
MonthStartDate(Date/Time)
Where:
Date/Time is the input date or timestamp.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 2004-07-29; output is the date 2004-07-01.
MonthsBetween
Returns the difference in months between two dates as an integer value. The difference is
calculated by the number of months elapsed and not by the number of month boundaries
crossed. If the first date argument is later than the second date argument, the result is a
negative number. The result does not display the timestamps, as they are truncated before
performing the calculation.
Syntax
MonthsBetween(Date/Time1, Date/Time2)
Where:
•
Date/Time1 is the start date.
•
Date/Time2 is the end date.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Inputs are the dates 2004-07-29 and 2004-02-28; output is the integer -5.
Inputs are the dates 2004-07-29 and 2004-09-29; output is the integer 2.
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Quarter
Returns the number of the quarter in the year of the input date. The return value is an integer
between 1 and 4.
Syntax
Quarter(Argument)
Where:
Argument is a metric representing a list of dates.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 5/16/2003; output is the integer 2.
Input is the date 2003/9/3; output is the integer 3.
QuarterStartDate
Returns the date of the first day of the quarter in which a date or timestamp occurs.
Syntax
QuarterStartDate<firstMonth>(Date/Time)
Where:
•
Date/Time is the input date or timestamp.
•
firstMonth (default value is 1) is a parameter that determines which months are
considered as the first months of each quarter. You can type an integer value from 1 to
12, with 1 representing January, 2 representing February, and so on until 12
representing December. Defining one month automatically defines the first month for the
other quarters as well. For example, if you specify January as the first month, April, July,
and October are also considered as the first month of quarters.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Consider a report or dashboard that includes a Day attribute with a single ID form. You can
create an attribute form with the following definition:
QuarterStartDate<firstMonth=1>(Day)
For each element of the Day attribute, the new attribute form displays the start date of the
quarter.
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Second
Returns the integer value for the second of the input time. The return value is an integer
between 0 and 59.
Syntax
Second(Argument)
Where:
Argument is a metric representing a list of dates and times.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the time 9:45:13 am; output is the integer 13.
Input is the time 11:10:47 pm; output is the integer 47.
ToDateTime (convert string or number to a date or
timestamp)
The ToDateTime function converts a string of characters or a number into a date or
timestamp.
Syntax
ToDateTime<Pattern=null>(Argument)
Where:
•
Argument is a fact, metric, column, or constant value that provides the strings that are
converted to numeric values.
•
Pattern is a parameter that determines the date or timestamp format used for the
resulting date or timestamp. By default, dates are expected to be of the format
mm/dd/yyyy. When providing a pattern, enclose the pattern in double quotes (""). For
example:
ToDateTime<pattern="mm/dd/yyyy">(Day@ID)
To specify a pattern, you can use custom formatting symbols that conform to the
standards of the International Components for Unicode. For information about the
custom formatting standards and syntax, see http://userguide.icuproject.org/formatparse/datetime.
Example
ToDateTime<pattern="mm/dd/yyyy">(Day@ID)
This example would return the data stored for the ID form of Day in a date format that
includes month, day, and year information.
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Week
Returns the number of the week in the year of the input date. The return value is an integer
between 1 and 54.
Syntax
Week(Argument)
Where:
Argument is a metric representing a list of dates.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 5/16/2003; output is the integer 20.
Input is the date 2003/9/9; output is the integer 37.
WeekStartDate
Returns the date of the first day of the week in which a date or timestamp occurs.
Syntax
WeekStartDate<firstDay>(Date/Time)
Where:
•
Date/Time is the input date or timestamp.
•
firstDay (default value is 1) is a parameter that determines which day of the week is
considered as the first day of the week. You can type an integer value from 1 to 7, with 1
representing Sunday, 2 representing Monday, and so on until 7 representing Saturday.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Consider a report or dashboard that includes a Day attribute with a single ID form. You can
create an attribute form with the following definition:
WeekStartDate<firstDay=1>(Day)
For each element of the Day attribute, the new attribute form displays the start date of the
week.
Year
Returns the year of the input date. The return value is an integer between 1900 and 9999.
Syntax
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Year(Argument)
Where:
Argument is a metric representing a list of dates.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 5/16/2003; output is the integer 2003.
Input is the date 2002/9/9; output is the integer 2002.
YearEndDate
Returns the date of the last day of the year in which a date or timestamp occurs.
Syntax
YearEndDate(Date/Time)
Where:
Date/Time is the input date or timestamp.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Example
Input is the date 2004-07-29; output is the date 2004-12-31.
YearStartDate
Returns the date of the first day of the year in which a date or timestamp occurs.
Syntax
YearStartDate(Date/Time)
Where:
Date/Time is the input date or timestamp.
For information on whether your database supports various date and time functions, see
Appendix A, MicroStrategy and Database Support for Functions.
Examples
Input is the date 2004-07-29; output is the date 2004-01-01.
Internal functions
The following are internal function types:
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•
•
•
MicroStrategy Apply, or Pass-through, functions provide access to functionality
that is not standard in MicroStrategy but is available in many Relational Database
Management Systems (RDBMS). These MicroStrategy functions act as containers for
non-standard SQL expressions passed to your warehouse database. The
MicroStrategy Apply functions are listed below:
▫
ApplyAgg defines simple metrics or facts using group-value aggregation functions.
▫
ApplyLogicdefines custom filters using comparison operators.
▫
ApplyLogic defines custom filters using logical operators.
▫
ApplyOLAP defines compound functions using database-specific OLAP functions
such as Rank and RunningSlope.
▫
ApplySimple uses simple operators and functions like +, -, and * to perform singlevalue operations at the database level.
Banding functions are used to differentiate displayed data on a report. You can
divide data into bands in the following ways:
▫
Banding distributes the values into bands of equal size.
▫
BandingC (banding count) distributes values into a specified number of bands.
▫
BandingP (banding points) distributes values into bands based on specific intervals
of values.
Case functions return specified data based on the evaluation of user-defined
conditions.
▫
Case evaluates multiple expressions until a condition is determined to be true, then
returns a corresponding value.
▫
CaseV (case vector) evaluates a single metric and returns different values
according to the results.
▫
Coalesce returns the value of the first non-null argument.
Apply (Pass-Through) functions
MicroStrategy Apply functions provide access to functions or syntactic constructs that are not
standard in MicroStrategy but are provided by various Relational Database Management
System (RDBMS) platforms.
MicroStrategy strongly advises against using Apply functions when standard MicroStrategy
functions can be used to achieve the same goal, because using RDBMS functions effectively
bypasses the validations and other benefits of MicroStrategy products. Using Apply functions
is recommended only when corresponding functionality does not exist in MicroStrategy.
When you need to use an Apply function, MicroStrategy encourages you to submit an
enhancement request for inclusion of the desired feature in a future product release.
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Syntax common to Apply functions
While each Apply function has its own syntax, the Apply functions share several syntactic
features:
•
The “#n” code in Apply function syntax serves as placeholders for the MicroStrategy
objects being passed to your database. The index for referencing these objects begins
with 0 and increases by 1 for each successive object passed. For example,
ApplySimple("#0 * #1",[col1],[col2]) indicates that two items, col1 and
col2, referenced as #0 and #1, respectively, are being passed to your database to be
multiplied together (by the database). If the number of references in an Apply function
(e.g., #0, #1, and so forth) exceeds the number of objects passed in that function,
MicroStrategy passes the last available object in place of the extra reference(s). For
example, ApplySimple("#0 * #1 * #2 * #3",[col1],[col2]) uses two
more references than there are arguments to pass, so MicroStrategy passes #2 and
#3, the extra references, as col2, the last available object in the list.
•
To use # as a character rather than a placeholder, use four # characters in a row. See
the syntax below for an example.
ApplyComparison(UPPER(#0) like
‘Z####%’, Country@DESC)
The SQL for the function is:
Select a.11[COUNTRY_ID] AS COUNTRY_ID
from [LU_COUNTRY] a11
where upper(a11.[COUNTRY_NAME])
like ‘Z#%’
•
Do not use form groups for the attribute form expression when using Apply functions,
because form groups are ignored by the Analytical Engine. Instead, use a single form.
For example, instead of using Customer@Name, where Name is defined to contain
Customer’s first name, middle name, and last name, use ID or any other single form.
For more general information on Apply functions as well as an example, see Apply (Passthrough) functions, page 26.
ApplyAgg
The ApplyAgg function is used to define simple metrics or facts by using database-specific,
group-value functions. The ApplyAgg function itself is a group-value function and accepts
facts, attributes, and metrics as input.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
ApplyAgg()
Accepts facts, attributes, and metrics as input.
All placeholders must begin with #0 and increase in increments of 1.
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Example
ApplyAgg(“Regrsxx(#0,#1)”, [Argument 1], [Argument 2] {~+}
ApplyComparison
ApplyComparison is used to define a filter by populating the WHERE clause in the SQL
passed to your RDBMS, and can take facts, attributes, and metrics as input.
The ApplyComparison function is used with RDBMS comparison operators such as >,
like, and In.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
ApplyComparison()
Example
ApplyComparison ("#0>#1", Store@ID,2)
For another example of the ApplyComparison function, see Example: ApplyComparison
used to check a prompted date, page 27.
ApplyLogic
The ApplyLogic function is used to define custom filters. It is used with logical operators
such as AND and OR. ApplyLogic is a logical function.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
ApplyLogic()
Accepts logic (Boolean) values as input.
Example
ApplyLogic(“#0 and #1”, Year@ID>2003, Month@ID>200301)
ApplyOLAP
OLAP functions are group-value functions that take a set of data as input and generate a set
of data as output, usually reordering the set according to some criteria.
ApplyOLAP is the MicroStrategy Apply function tool used for OLAP functionality when you
wish to use the native capabilities of your RDBMS. It is used to define compound metrics via
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database-specific functions such as Rank(). ApplyOLAP, like ApplySimple, is used to
define metrics but differs in that it only accepts metrics as input.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
ApplyOLAP()
Accepts only metrics as input.
Example
ApplyOLAP(“RunningSlope(#0,#1)”, [Metric 1], [Metric 2])
ApplySimple
The ApplySimple function is a single-value function. It is used to insert any single-value,
database-specific functions and simple operators directly into SQL.
In general, ApplySimple can be used to create the following objects:
•
Attribute form
For any Apply function, the attribute form in the arguments should be a single form—not a
form group. The engine ignores any definitions based on attribute forms.
•
Consolidation
•
Custom group
•
Fact
•
Metric
•
Subtotal
•
Transformation
For information about consolidations, custom groups, metrics, and subtotals, see the
MicroStrategy Advanced Reporting Guide.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
ApplySimple()
Accepts facts, attributes, and metrics as input.
Examples of object creation
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Expression type
Attribute
Examples
ApplySimple(“Datediff(YY,#0,getdate())”, [BIRTH_DATE]), in which [BIRTH_DATE]
is an attribute
ApplySimple(“Months_between(sysdate,#0)”,[CURRENT_DT]), in which
[CURRENT_DT] is an attribute
Compound Metric
ApplySimple(“Greatest(#0,#1,#2)”, Metric_1, Metric_2,Metric_3)
ApplySimple(" CASE WHEN #0 between 0 and 100000 THEN 'Low' WHEN #0
between 100001 and 200000 THEN 'Med' ELSE 'High' END ", Sum(Revenue){~})
Examples in custom expressions
•
ApplySimple("Datediff(YY,#0,getdate())", [BIRTH_DATE])
•
ApplySimple("Months_between(sysdate,#0)", [CURRENT_DT])
Examples: Incorrect usage
•
ApplySimple("Sum(#0)",[Column 1])
•
ApplySimple("Count(#0)",[Column 2])
The two examples immediately above are incorrect and should not be used in your
application because of the following two reasons:
•
ApplySimple is a single-value function and therefore can only be used with single-value
functions. Sum and Count are both group-value functions and therefore should not be
used with ApplySimple.
•
Sum and Count are both MicroStrategy functions and are not database-specific;
therefore, they should not be used with ApplySimple or any other Apply functions.
Banding functions
Banding functions are used to group data on a report so that it is both more comprehensible
and aesthetically pleasing than when it is displayed as one contiguous list. MicroStrategy
provides different banding options for you to use, depending on how you want to divide your
data. The banding functions, their syntaxes, and examples are listed below.
For information on banding functions in custom groups, see the Advanced Reporting Guide.
Banding
This function maps metric values that fall within a certain range to a particular integer band
value. The range and band values are determined by the parameter input to the function. For
example, if 5,000 is the specified range, the dollar sales are shown in bands of 0 - 5,000,
5,001 - 10,000, 10,001 - 15,000, and so on. Banding is a single-value function.
Syntax
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Banding <HasResidue>(Argument, StartAt, StopAt, Size)
Where:
•
Argument is a metric.
•
StartAt and StopAt are real numbers specifying the full range of the values to be
placed in bands.
•
Size is a real number indicating the width of each band.
•
HasResidue is a TRUE/FALSE parameter that defines whether an extra band is
created for all values that do not fall within the StartAt and StopAt values. Defining
HasResidue as TRUE allows the function to create an additional band which is used to
identify all values outside of the defined band range.
Usage notes
Values in the list that fall outside of the start and end values set in the function syntax are
assigned a band of 0 (zero) in the report interface.
Example
The following example shows how the Banding function acts on report data. In this case the
total revenue for each city is used to divide the report data into bands:
•
The highest total revenue is 405,367 (New York).
•
The lowest revenue is 668 (Cleveland).
The metric function syntax is as follows:
Banding([Total Revenue], 1, 410000, 20000)
•
Total Revenue is the metric, defined as Sum(Revenue), representing the list of values
acted on by the Banding function.
•
1 is the value at which banding begins.
•
410,000 is the value at which banding ends.
•
20,000 is the size of the range of values included in each band.
Only part of the resulting report is displayed here.
The result is that 21 bands are created, each encompassing a range of 20,000 dollars of total
revenue. To determine the number of bands, 410,000 is divided by 20,000 resulting in 20.5
(rounded to nearest integer, 21). Each band is given an integer value of between 1 and 21.
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BandingC (banding count)
BandingC returns metric data grouped into a specified number of bands and assigns
integer values to the resulting bands (for example, a total of 25,000 in dollar sales shown in
five equal bands). This is a single-value function.
Syntax
BandingC <HasResidue>(Argument, StartAt, StopAt,
BandCount)
Where:
•
Argument is a metric.
•
BandCount is a positive integer indicating the number of bands into which the total is
divided.
•
StartAt and StopAt are real numbers specifying the full range of the values to be
placed in bands.
•
HasResidue is a TRUE/FALSE parameter that defines whether an extra band is
created for all values that do not fall within the StartAt and StopAt values. Defining
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HasResidue as True allows the function to create an additional band which is used to
identify all values outside of the defined band range.
Usage notes
Values that fall outside of the range indicated by the Start and Stop values are assigned a
band of 0 (zero).
Example
The following example shows how the BandingC function acts on report data. This example
uses the same set of values as the example for the Banding function. Using the BandingC
function, you can designate the number of bands created for the range of values.
The metric function syntax is as follows:
BandingC([Total Revenue], 1, 410000, 25)
Where:
•
Total Revenue is the metric, defined as Sum(Revenue), representing the list of values
acted on by the BandingC function.
•
1 is the value at which banding begins.
•
410,000 is the value at which banding ends.
•
25 is the number of bands into which you want the values divided.
Only part of the resulting report is displayed here.
The result is that 20 bands of equal sizes are created. To determine the range of each band,
410,000 is divided by 25 resulting in bands of 16,400. Each band is given an integer value of
between 1 and 25.
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BandingP (banding points)
Returns metric data grouped into bands identified by user-specified boundary point values
(for example, 0 - 5,000, 5,000 - 20,000, 20,000 - 30,000). This function assigns integer
values to the resulting metric and allows you to create band intervals of varying widths. This
is a single-value function.
Syntax
BandingP <HasResidue>(Argument, Boundary1, Boundary2,...,
BoundaryN)
Where:
•
Argument is a metric.
•
Boundary1 through BoundaryN are real numbers indicating the cut-off value for each
band. Boundary1 is less than Boundary2, Boundary2 is less than Boundary3,
and so on.
•
HasResidue is a TRUE/FALSE parameter that defines whether an extra band is
created for all values that do not fall within the Boundary1 and BoundaryN values.
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Defining HasResidue as TRUE allows the function to create an additional band which
is used to identify all values outside of the defined band range.
Example
The following example shows how the BandingP function acts on report data. This example
again uses the same data set as in the previous examples (Banding and BandingC). Using
the BandingP function, you can designate the boundaries for each interval.
The metric function syntax is as follows:
BandingP([Total Revenue],
1,20000,40000,60000,80000,100000,410000)
Where:
•
Total Revenue is the metric, defined as Sum(Revenue), representing the list of values
acted on by the BandingP function.
•
All other values specify the boundaries of an interval. There are six intervals defined as
follows:
▫
Band 1: 1 ≤ [Total Revenue] < 20000
▫
Band 2: 20,000 ≤ [Total Revenue] < 40,000
▫
Band 3: 40,000 ≤ [Total Revenue] < 60,000
▫
Band 4: 60,000 ≤ [Total Revenue] < 80,000
▫
Band 5: 80,000 ≤ [Total Revenue] < 100,000
▫
Band 6: 100,000 ≤ [Total Revenue] ≤ 410,000
Only part of the resulting report is displayed here.
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Case functions
Case functions return specified data in a SQL query based on the evaluation of user-defined
conditions. In general, a user specifies a list of conditions and corresponding return values.
When MicroStrategy queries the data warehouse, the software determines which condition
evaluates as true and then returns the value that the user has specified that corresponds to
that condition. The case functions, their syntaxes, and examples are listed below.
Case
This function evaluates multiple expressions until a condition is determined to be true, then
returns a corresponding value. If all conditions are false, a default value is returned. Case
can be used for categorizing data based on multiple conditions. This is a single-value
function.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
Case (Condition1, ReturnValue1,
Condition2,ReturnValue2,..., DefaultValue)
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Where:
•
Condition1 is the first condition to evaluate. The condition can contain metrics,
comparison and logical operators, and constants.
•
ReturnValue1 is a constant or metric value to return if the Condition1 condition is
TRUE.
•
Condition2 is the second condition to evaluate. The condition can contain metrics,
comparison and logical operators, and constants.
•
ReturnValue2 is a constant or metric value to return if the Condition2 condition is
TRUE.
•
... represents any number of Conditions and Return Values that can be passed through
this function.
•
DefaultValue is the information to return if none of the other conditions are TRUE.
Example
This example generates a report where if the revenue, represented by the Total Revenue
metric, is less than 300,000, the function returns a 0; if the revenue is less than 600,000, the
function returns a 1; if revenue is any other value, the function returns a 2. The case metric is
defined as follows:
Case(([Total Revenue] < 300000), 0, ([Total Revenue] <
600000), 1, 2)
Difference between Case and If
The If function is very similar to the Case function. Each function takes a condition as an
argument and returns a value depending on whether the condition is true or not. The Case
function can evaluate multiple conditional arguments, while the If function can only evaluate
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one condition. However, the If function can be evaluated by either the SQL Engine or the
Analytical Engine, while the Case function is evaluated only by the Analytical Engine.
CaseV (case vector)
CaseV evaluates a single metric and returns different values according to the results. It can
be used to perform transformations on a metric. For example, if provided a list of values
ranging from 1 to 12, CaseV might return January for a value of 1, February for a value of 2,
etc. This is a single-value function.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
CaseV (Argument, Value1, Result1, Value2, Result2, ...,
DefaultResult)
Where:
•
Argument is the metric against which all values are compared.
•
Value1 is the first value (constant or metric) to be evaluated.
•
Result1 is the information to return (constant or metric) if the Value1 value is equal to
the value for the Metric.
•
Value2 is the second value (constant or metric) to be evaluated.
•
Result2 is the information to return (constant or metric) if the Value2 value is equal to
the value for the Metric.
•
... represents any number of Conditions and Return Values that can be passed
through this function.
•
DefaultResult is the information to return (constant or metric) if none of the other
values are Equal to the Metric.
Usage notes
The metric or argument in the CaseV expression is always held as a float. This means that
even if the value is 2, it is held as 2.00000; and therefore 2 and 2.00000 are never considered
equal. For this reason, it is best if you wrap the metric or argument in the integer function, for
example, CaseV(int(M1), 2, A,...).
Example
This simple example generates a report where if the Unit Profit for the item is 2, the function
returns a 200; if the Unit Profit for the item is 3, the function returns a 300; if Unit Profit is any
other value, the function returns a 1000000. Notice how a single metric, Unit Profit, is
evaluated against multiple numeric values. The report contains the attribute Item and the
metrics Unit Profit and CaseV. A report filter limits the items displayed to those in the
subcategory Action. The CaseV metric is defined as follows:
CaseV ([Unit Profit], 2, 200, 3, 300, 1000000)
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Coalesce
Returns the value of the first non-null argument. Coalesce can be used to identify data in
tables that may not be fully populated or in metric definitions.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
Coalesce (Argument1, Argument2,..., ArgumentN)
Where:
The arguments for the Coalesce function can be any expression that can be evaluated as
null or not null.
Usage Notes
You can use the Coalesce function in defining a metric, but more often it is used with the
Query Builder feature to support the inclusion of the Coalesce function in SQL queries. See
the example below for more detailed information.
Example
Your database has two tables T1 and T2 that include the column MONTH_ID with the format
yyyymm. You want to filter on a specific month, but you are not sure which table has been
populated with month data. In the Query Builder Editor, you can filter your SQL query by
creating the condition Coalesce(T1.MONTH_ID, T2.MONTH_ID) = 200410. The
WHERE clause of the SQL query checks for the first non-null MONTH_ID column and
compares it to the value 200410.
For more information on Query Builder, see the Advanced Reporting Guide.
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NULL/Zero functions
The NULL/Zero functions are often used to determine how NULLs and zeros are displayed
on a report. They can also be used as a form of data validation to ensure meaningful results.
For example, an otherwise invalid mathematical expression such as 5 + NULL can be
changed to 5 + 0.
IsNotNull
Returns TRUE if value input is not NULL; otherwise, returns FALSE. This is a comparison
function.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
IsNotNull(Argument)
Where:
Argument is a fact or metric.
Usage notes
The IsNull and IsNotNull functions work only with the data returned from the database. For
example, if the database data is NULL, the IsNotNull function returns a FALSE. However,
you may see NULL data when you manipulate a report after its original generation and that
manipulation does not regenerate the data from the database. This can happen when you
page by, for example, and the result includes a NULL.
IsNull
Returns TRUE if the value is NULL; otherwise, returns FALSE. This is a comparison function.
Depending on your MicroStrategy product and licensing, this function may not be available.
Syntax
IsNull(Argument)
Where:
Argument is a fact or metric.
Usage notes
The IsNull and IsNotNull functions work only with the data returned from the database. For
example, if the database data is NULL, the IsNotNull function returns a FALSE. However,
you may see NULL data when you manipulate a report after its original generation and that
manipulation does not regenerate the data from the database. This can happen when you
page by, for example, and the result includes a NULL.
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NullToEmpty
Converts a value of NULL to an empty string. If the value is not NULL, the original value is
kept.
Syntax
NullToEmpty(Argument)
Where:
Argument is metric or attribute form.
Example
You can use NullToEmpty when creating derived attributes to convert NULL values for
attribute elements to empty strings. For example, you can convert NULL values to empty
strings so that attribute element values can be successfully concatenated with functions such
as Concat (see Concat (concatenate), page 222) or ConcatBlank (see ConcatBlank
(concatenate plus blank space), page 223).
For steps to create derived attributes, see the MicroStrategy Web Help.
NullToZero
Converts a value of NULL to 0. If the value is not NULL, the original value is kept.
Syntax
NullToZero(Argument)
Where:
Argument is a fact or metric.
Example
In this simple example, the function NullToZero is applied to a list of values (12, NULL, 97,
43, NULL). The resulting list is (12, 0, 97, 43, 0). This function could be applied to a value list
before it is acted on by another function such as Average so that NULL values in a list are
included as list elements and factored into the average as zeros.
ZeroToNull
Converts a value of 0 to NULL. If the value is not 0, the original value is kept.
Syntax
ZeroToNull(Argument)
Where
Argument is a fact or metric.
Example
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In this simple example, the function ZeroToNull is applied to a list of values (12, 0, 97, 43, 0).
The resulting list is (12, NULL, 97, 43, NULL). This function could be applied to a value list
before it is acted on by another function such as Average so that zero values in a list are not
included as list elements and therefore not factored into the average.
OLAP functions
OLAP functions are also known as relative functions. They take multiple elements from a list
and return a new list of elements. The following applies to all OLAP functions:
•
SortBy is applied before the engine performs the calculation of an OLAP function.
•
In OLAP functions, the SortBy parameter can be either a metric or an attribute.
Many OLAP functions calculate measures useful for analyzing a set of values such as the
sum, average, and standard deviation. These functions fall into three groups depending on
how they select the window of values on which they base their calculations. These groups
are as follows:
•
Running: Functions with a running window include the current value and all preceding
values. For example, given the list (1, 2, 3, 4), RunningSum returns the sums 1, 3, 6,
and 10. This example is illustrated in the table below. These functions include the word
Running in their name, such as RunningAvg and RunningStDev.
•
Moving: Functions with a moving window include the current value and a fixed number
of preceding values. For example, given the list (1,2,3,4) and a window size of 2,
MovingSum returns 1, 3, 5, and 7. This example is illustrated in the table below. These
functions include the word Moving in their name, such as MovingAvg and
MovingStDev.
•
OLAP: Functions with flexible windows allow you to set where windows begin and end
in relation to the current value. This feature allows you to include both preceding and
succeeding values in your calculations. For example, you can use OLAPSum to include
one value above and below the current row. This example is illustrated in the table
below. These functions include the word OLAP, such as OLAPSum and OLAPAvg.
The following table lists a comparison of the example scenarios described above.
Values
RunningSum
MovingSum
OLAPSum
1
1 (1)
1 (1)
3 (1+2)
2
3 (1+2)
3 (1+2)
6 (1+2+3)
3
6 (1+2+3)
5 (2+3)
9 (2+3+4)
4
10 (1+2+3+4)
7 (3+4)
7 (3+4)
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ExpWghMovingAvg (exponential weighted moving
average)
ExpWghMovingAvg allows you to place more or less emphasis on recent data than on past
data within a specified number of rows. It is calculated within the specified window size and
can restart based on an attribute specified in the function parameters.
Syntax
ExpWghMovingAvg <BreakBy, SortBy> (Argument, WindowSize,
Rate)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
•
WindowSize indicates the number of values to use in each calculation.
•
Rate is a positive real number specifying the base weight applied to each argument
value. In the calculation, exponents are sequentially applied to the rate value. Assign a
rate of less than one to give more emphasis to more recent data; assign a rate of greater
than 1 to give greater emphasis to past data.
Expression
Where:
•
k = row number
•
yi = metric value at the ith row
•
m = window size or the row number, whichever is smaller
•
n = number of rows
•
w = the base weight applied to each value, which is determined by the Rate value in the
function, as described in the function syntax details above
Rows with null values are excluded from the calculation.
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Example
This example uses small numbers to demonstrate the calculation for the exponential
weighted moving average (ExpWghMovingAvg) function. For example, you have a list of
values (32, 8, 5), with 5 being the most recent value, and you assign a rate of .5 and a
window size of 2.
Values
EWM
average
Calculation
32
32
32(.5)0 / (.5)0 = 32(1)/1 = 32
8
16
8(.5)0 + 32(.5)1 / (.5)0 + (.5)1 =
8(1)+ 32(.5) / 1+.5 = 8+16 / 1.5 = 16
5
6
5(.5)0 + 8(.5)1 / (.5)0 + (.5)1 =
5(1)+ 8(.5) / 1+.5 = 5+4 / 1.5 = 6
As an additional example, the Human Resources Analysis Module project includes the
Division Breakdown report shown below.
A description of how the MovingAvg metric is used on the report is provided in MovingAvg
(moving average), page 160. You can also add an exponential weighted moving average
metric to this report to apply more or less emphasis to older data. For example, you can
create another derived metric named ExpWghMovingAvg with the following expression:
ExpWghMovingAvg<BreakBy={Division}, SortBy=(Quarter)>
(Employees, 4.0, 0.5)
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The rate of 0.5 changes the moving average to apply more significance, or weight, to more
recent data. This means that the number of Sales employees during the first quarter (13) has
less weight than the number of Sales employees in the fourth quarter (18). When comparing
MovingAvg and ExpWghMovingAvg for the Sales division, you can see that the
ExpWghMovingAvg is larger. This is because more significance is given to more recent
data, and the recent trend is that employment is on the rise. This is shown in the report
below.
Conversely, you can modify the same ExpWghMovingAvg metric to use the following
expression:
ExpWghMovingAvg<BreakBy={Division}, SortBy= (Quarter) >
(Employees, 4.0, 2)
The rate of 2 changes the moving average to apply more significance, or weight, to older
data. This means that the number of Sales employees during the first quarter (13) has more
weight than the number of Sales employees in the fourth quarter (18). When comparing
MovingAvg and ExpWghMovingAvg for the Sales division, you can see that the
ExpWghMovingAvg is smaller. This is because more significance is given to older data, and
employment was lower earlier in the year. This is shown in the report below.
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ExpWghRunningAvg (exponential weighted running
average)
ExpWghRunningAvg allows you compute a running average while placing more or less
emphasis on recent data than on past data. The calculation can restart based on an attribute
specified in the function parameters.
Syntax
ExpWghRunningAvg <BreakBy, SortBy> (Argument, Rate)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
•
Rate is a positive real number specifying the base weight applied to each argument
value. In the calculation, exponents are sequentially applied to the rate value. Assign a
rate of less than one to give more emphasis to more recent data; assign a rate of greater
than 1 to give greater emphasis to past data.
Expression
Where:
•
k = row number
•
yi = metric value at the ith row
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•
m = window size or the row number, whichever is smaller
•
n = number of rows
•
w = the base weight applied to each value, which is determined by the Rate value in the
function, as described in the function syntax details above
Rows with null values are excluded from the calculation.
Example
This example uses small numbers to demonstrate the calculation for the exponential
weighted running average (ExpWghRunningAvg) function. For example, you have a list of
values (32, 8, 5), with 5 being the most recent value, and you assign a rate of .5.
values
EWR
average
calculation
32
32
32(.5)0 / (.5)0 = 32(1)/1 = 32
8
16
8(.5)0 + 32(.5)1 / (.5)0 + (.5)1 =
8(1)+ 32(.5) / 1+.5 = 8+16 / 1.5 = 16
5
10
5(.5)0 + 8(.5)1 + 32(.5)2 / (.5)0 + (.5)1 +(.5)2 =
5(1)+ 8(.5)+ 32(.25) / 1+ .5 + .25 =
5 + 4 + 8 / 1.75 = 10 (rounded from 9.71)
FirstInRange
Returns the first value in a range of values. FirstInRange can be used to examine data
such as inventory at the beginning of each month.
Calculations can be restarted based on attributes set in the function parameters. This restart
capability differentiates FirstInRange from First. While First is a group-value
function and takes both facts and metrics as argument input, FirstInRange is an OLAP
function and takes only metrics as argument input.
Syntax
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FirstInRange <BreakBy, SortBy> (Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
Expression
Where:
•
k = row number
•
y1 = first non-NULL value of the metric after applying the BreakBy and SortBy
parameters
Example
This example shows the results of using the FirstInRange function. The report includes the
attributes Customer Region and Customer State and the metrics Profit and FirstInRange. A
report filter limits the regions displayed to Northwest, Southeast, and Southwest. The
FirstInRange metric is defined as follows:
FirstInRange<BreakBy={[Customer Region]}, SortBy=
([Customer State]) >(Profit)
In the following report, FirstInRange returns the first profit value in the list of Customer States
for each Customer Region.
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Lag
The Lag function is useful to display a set of metric values in an order determined by another
metric or attribute on the report. This allows you to compare metric values side-by-side. The
easiest way to understand the Lag function is with an example, provided below.
Syntax
Lag <BreakBy, SortBy> (Argument, Offset, DefaultValue)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is an attribute or metric representing a list of values. It is common to use a
metric for Argument.
•
Offset specifies the offset from the current row to display information for. This offset
trails behind the current row (you can use the Lead function to use an offset that
precedes ahead of the current row, see Lead, page 159). For example, with an offset of
two, row three displays data for the row two behind it, which would be row one.
•
DefaultValue is the value displayed for any entries that do not have any data due to
the offset. For example, when using an offset of 2, the first two entries use the default
value.
Some common default values to display include:
▫
0: To display a value of 0 for any entries that do not have any data due to the offset,
type 0.
▫
No data: To display no data for any entries that do not have any data due to the
offset, type ZeroToNull(0).
Example
On a report with Item, Cost, and Profit, you can use the Lag function to create a Cost (Lag
on Profit) metric. This metric displays Cost values based on the descending sort order of the
Profit metric, and is defined with the following expression:
Lag<SortBy= (Profit Desc) >(Cost, 3.0, 0.0)
Notice that the offset is set to three, meaning that the display of cost values lags behind by
three entries. This is displayed in the report results shown below.
Only the top and the bottom of the report is shown. To view the entire report results, you can
create this report in the MicroStrategy Tutorial project.
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The Cost (Lag on Profit) values are displayed three behind the Cost metric, and are
displayed based on the descending sort order of Profit. Notice that with an offset of three, the
first three entries for Cost (Lag on Profit) use the default value of zero. Also, the last three
values of Cost are not included in the Cost (Lag on Profit) metric.
The report has been sorted based on the Profit metric to make the Cost (Lag on Profit)
values easier to analyze.
Transformation-style analysis using the Lag and Lead functions
Transformation-style analysis can 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 creating
transformations, see the Project Design Guide.
Note the following:
•
The examples shown below were created in the MicroStrategy Tutorial project.
•
The examples below use the Lag function. The Lead function can be used in the
same way. The difference in using Lead rather than Lag is that the Lead function
would display information by going forward in time rather than backward. For
example, while the Lag function can return historical data from one year ago, the
same formula used for the Lead function would return historical data from one year
ahead.
The Lead function does not predict future values. For functions that can predict
future values based on historical data, see ForecastV (forecast, vector input),
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page 271, GrowthV (growth, vector input), page 272, and TrendV (trend, vector
input), page 276. Additionally, you can use data mining functions from
MicroStrategy Data Mining Services to predict future trends, as described in
Data mining functions, page 255.
For example, a Last Year’s Monthly Revenue metric can be created using the following
function:
Lag<SortBy=(Month@ID)>(Revenue,12,ZeroToNull(0))
This metric can then be included on a report with the Month attribute and the Revenue
metric, as shown below.
This allows you to perform a side-by-side comparison of monthly revenue for different years.
In this report, the 2007 monthly revenue is displayed next to the 2008 monthly revenue. The
element Jan 2009 is included to show that the Last Year’s Monthly Revenue is displaying the
monthly revenue from the previous year.
By modifying the offset of the Lag function, you can change the time periods that can be
compared side by side. For example, the function listed above uses an offset of 12, which
displays monthly data from one year ago. Using an offset of 3 would display monthly data
from three months ago, while using an offset of 24 would display monthly data from two
years ago. These slight modifications could be used to create separate metrics that could all
be included on the same report. The report below shows an example of including three
different metrics that use the same Lag formula with a different offset.
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The report shown above allows side-by-side comparison of monthly revenue for three
different time periods. In this report, the Monthly Revenue 2 Years Ago metric displays no
data for months in the year 2008 because no revenue data exists for the year 2006.
To use the Lag or Lead functions for transformation-style analysis, the metric formulas must
be created to support the required reporting scenario. For example, the report with the
Month attribute and the Revenue metric has the Category attribute added to it. To support
this reporting scenario, you can modify the Lag formula described above to include the
Category attribute, as shown below.
Lag<BreakBy={Category},SortBy=(Month@ID)>
(Revenue,12,ZeroToNull(0))
Using the formula shown above, the calculation is restarted for each category, which allows
the side-by-side comparison of monthly revenue over time for each category, as shown in
the report below:
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Only the Month attribute elements Jan 2007, Jan 2008, and Jan 2009 are displayed to show
that the Last Year’s Monthly Revenue (Category) metric is displaying the monthly revenue
from the previous year.
LastInRange
Returns the last value in a range of values. LastInRange can be used to examine data
such as inventory at the end of each month.
Calculations can be restarted based on attributes set in the function parameters. This restart
capability differentiates LastInRange from Last. While Last is a group-value function
and takes both facts and metrics as argument input, LastInRange is an OLAP function
and takes only metrics as argument input.
Syntax
LastInRange <BreakBy={}, SortBy=()> (Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
Expression
Where:
•
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k = row number
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•
yk = last non-NULL value of the metric after applying the BreakBy and SortBy
parameters
Lead
The Lead function is useful to display a set of metric values in an order determined by
another metric or attribute on the report. This allows you to compare metric values side-byside. The easiest way to understand the Lead function is with an example, provided below.
Syntax
Lead <BreakBy, SortBy> (Argument, Offset, DefaultValue)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is an attribute or metric representing a list of values. It is common to use a
metric for Argument.
•
Offset specifies the offset from the current row to display information for. This offset
precedes ahead of the current row (you can use the Lag function to use an offset that
trails behind the current row, see Lag, page 154). For example, with an offset of two, row
one displays data for the row two ahead of it, which would be row three.
•
DefaultValue is the value displayed for any entries that do not have any data due to
the offset. For example, when using an offset of 2, the last two entries use the default
value.
Some common default values to display include:
▫
0: To display a value of 0 for any entries that do not have any data due to the offset,
type 0.
▫
No data: To display no data for any entries that do not have any data due to the
offset, type ZeroToNull(0).
Example
For an example of using Lag and Lead functions to perform transformation-style analysis,
see Transformation-style analysis using the Lag and Lead functions, page 155.
On a report with Item, Cost, and Profit, you can use the Lead function to create a Cost (Lead
on Profit) metric. This metric displays Cost values based on the descending sort order of the
Profit metric, and is defined with the following expression:
Lead<SortBy= (Profit Desc) >(Cost, 3.0, 0.0)
Notice that the offset is set to three, meaning that the display of cost values is displayed three
ahead of the current value. This is displayed in the report results shown below.
Only the top and the bottom of the report is shown. To view the entire report results, you can
create this report in the MicroStrategy Tutorial project.
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The Cost (Lead on Profit) values are displayed three ahead of the Cost metric, and are
displayed based on the descending sort order of Profit. Notice that with an offset of three, the
last three entries for Cost (Lead on Profit) use the default value of zero. Also, the first three
values of Cost are not included in the Cost (Lead on Profit) metric.
The report has been sorted based on the Profit metric to make the Cost (Lead on Profit)
values easier to analyze.
MovingAvg (moving average)
Returns the moving average of the current value and preceding values, as defined by the
WindowSize parameter. The calculations can be restarted based on attributes set in the
function parameters. This is an OLAP function.
Syntax
MovingAvg <BreakBy={}, SortBy=()> (Argument, WindowSize)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
•
WindowSize is a positive integer indicating the number of values to use in each
calculation.
Expression
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Where:
•
yi = metric value at the ith row
•
m = window size
•
n = number of rows/metric values
Example
This simple example illustrates how the MovingAvg function calculates a list of values and
returns the average of a specified number of values. In this case, the window size is set to 3,
meaning that the value in the MovingAverage column represents the average of the current
value of the two values that precede it in the value list. The calculation is shown in the
following table.
Values
MovingAverage
10
10 (10/1)
20
15 ((20+10) / 2)
30
20 ((30+20+10) / 3)
15
51.67 ((15+30+20) / 3)
5
16.67 ((5+15+30) / 3)
20
13.34 ((20+5+15) / 3)
40
21.67 ((40+20+5) / 3)
As an additional example, the Human Resources Analysis Module project includes the
Division Breakdown report shown below.
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This report displays details about employee headcounts for each division, over the various
quarters of 2010. Included in this report is the metric MovingAvg. It is defined as a derived
metric, using the following expression:
MovingAvg<BreakBy={Division}, SortBy=(Quarter)>
(Employees, 4.0)
This expression calculates the moving average of employee headcount for a given division,
during the four quarters of 2010. The window size of 4.0 specifies that the average is
calculated across the four quarters, and BreakBy={Divison} ensures that the moving
average calculation is specific to each division.
Using this MovingAvg metric, you can determine that the Sales division had between 13.0
and 16.3 employees on average during the 2010 year, with slight increases throughout the
year.
For an extension of this example on how you can also use a weighted moving average, see
ExpWghMovingAvg (exponential weighted moving average), page 148.
MovingCount
Returns the moving count of a list of values. The calculation can be restarted based on
attributes set in the function parameters. This is an OLAP function.
Syntax
MovingCount <BreakBy={}, SortBy=()> (Argument,
WindowSize)
Where:
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•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
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•
Argument is a metric representing a list of values.
•
WindowSize is a positive integer indicating the highest number to use in the count.
Expression
Where:
•
yi = metric value at the ith row
•
m = window size
•
n = number of rows/metric values
•
1i = 1 if the value at the ith row is not NULL
1i = 0 otherwise
Usage Notes
If there are more entries in a section than the integer in the window size parameter, then all
the remaining entries are given the highest number in the count. For example, if the window
size is 4 and there are 6 entries in the list section, they are counted as follows: 1,2,3,4,4,4.
Example
This simple example demonstrates how the MovingCount function counts rows of data. This
report uses the attributes Region and Employee, and the metrics Revenue and Moving
Count. A filter is applied so the only Regions displayed are South, Northwest, and
Southwest. The Moving Count metric is defined as follows:
MovingCount<BreakBy={Region}, SortBy= (Value) >(Revenue,
3.0)
•
The count restarts for every Region.
•
The entries are counted based on the value of the metric Revenue in ascending order
(the lowest value is counted as 1, next lowest is 2, and so on).
•
The highest number in the count is 3 as designated in the WindowSize parameter.
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MovingDifference
Returns the moving difference between current and preceding values. The position of the
preceding row used in the calculation is defined in the function arguments. The calculations
can be restarted based on attributes set in the function parameters. This is an OLAP
function.
Syntax
MovingDifference <BreakBy, SortBy> (Argument, WindowSize)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
•
WindowSize is a positive integer indicating the range of values used to compute each
difference.
Expression
Where:
•
yi = metric value at the ith row
•
m = window size
•
n = number of rows/metric values
Example
This example illustrates how the MovingDifference function subtracts the value of a
preceding row from the value of the current row and returns the difference. In this case, the
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window size is set to 3, meaning that the value two rows previous to the current row is
subtracted from the current row. In other words, there are 3 rows in the window, the current
row is 3, the row immediately preceding it is 2, and the row preceding that is 1; therefore, the
calculation for the moving difference of row 3 is (row 3 - row 1).
The value in the MovingDifference column represents the result of the calculation for every
window of three values. The calculation is also shown.
Values
MovingDifference
400
500
700
300 (700-400)
300
-200 (300-500)
600
-100 (600-700)
800
500 (800-300)
200
-400 (200-600)
MovingMax (moving maximum)
Returns the moving maximum value by comparing current and preceding rows as defined in
the function arguments. The calculation can be restarted based on attributes set in the
function parameters. This is an OLAP function.
Syntax
MovingMax <BreakBy, SortBy> (Argument, WindowSize)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
•
WindowSize is a positive integer indicating the number of values to compare in each
calculation.
Expression
Where:
•
yi = metric value at the ith row
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•
m = window size
•
n = number of rows/metric values
Example
This simple example illustrates how the MovingMaximum function moves through a list of
values, subtracting a value from the user-defined number of preceding rows. In this case, the
window size is set to 3, meaning that the value in the MovingMaximum column represents
the highest value among the current and preceding two values in the value list. The
calculation is shown in the following table.
Values
MovingMaximum
550
550
30
550 (550>30)
40
550 (550>40 and 550>30)
70
70 (70>40 and 70>30)
50
70 (70>50 and 70>40)
MovingMin (moving minimum)
Compares the current value and preceding values as defined in the function arguments to
calculate a moving minimum value. The calculation can be restarted based on attributes set
in the function parameters. This is an OLAP function.
Syntax
MovingMin <BreakBy,SortBy>(Argument, WindowSize)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
•
WindowSize is a positive integer indicating the number of values to use in each
calculation.
Expression
Where:
•
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yi = metric value at the ith row
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•
m = window size
•
n = number of rows/metric values
Example
This simple example illustrates how the MovingMin function moves through a list of values
returning the lowest value within a specified number of values. In this case, the window size
is set to 3, meaning that the value in the MovingMinimum column represents the lowest value
among the current and preceding two values in the value list. The calculation is shown in the
following table.
Values
MovingMinimum
550
550
30
30 (30<550)
40
30 (30<40 and 30<550)
70
30 (30<70 and 30<40)
50
40 (40<70 and 40<50)
MovingStDevP (moving standard deviation of a
population)
Returns the moving standard deviation of a population based on a list of values that
encompasses the whole population. The calculation can be restarted based on attributes set
in the function parameters. This is an OLAP function.
Syntax
MovingStDevP <BreakBy, SortBy> (Argument, WindowSize)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
•
WindowSize is a positive integer indicating the number of values to use in each
calculation.
Expression
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Where:
•
yi = metric value at the ith row
•
y = average of metric
•
m = window size
•
n = number of rows/metric values
Example
This example shows a report where the moving standard deviation of the revenue is
calculated. This calculation is based on the assumption that the list of values supplied in the
metric represents the entire population of the data for which you want to obtain the standard
deviation. The calculation starts over for each region, the information is sorted within the
region by state in ascending order, and each calculation is based upon a window size of 3.
Compare this example report to the example for MovingStDev to see the different values
returned when calculating for a population as opposed to a sample.
The report contains the attributes Customer Region and Customer State, and the metrics
Total Revenue, MovingStDevP, RunningStDevP, and StDevP. A report filter limits data to
the South, Southwest, and Northwest regions. The definition of the MovingStDevP metric is
as follows:
MovingStDevP<BreakBy={[Customer Region]}, SortBy=<
[Customer State])>([Total Revenue],3)
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MovingStDev (moving standard deviation)
Returns the moving standard deviation based on a list of values that is a sample of the
population. The calculation can be restarted based on attributes set in the function
parameters. This is an OLAP function.
Syntax
MovingStDev <BreakBy, SortBy> (Argument, WindowSize)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of number.
•
WindowSize is a positive integer indicating the number of values to use in each
calculation.
Expression
Where:
•
yi = metric value at the ith row
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•
y = average of metric
•
m = window size
•
n = number of rows/metric values
Example
This example shows a report where the moving standard deviation of the revenue is
calculated. This calculation is based on the assumption that the list of values supplied in the
metric represents a sample of the data for which you want to obtain the standard deviation.
The calculation starts over for each region, the information is sorted within the region by state
in ascending order, and each calculation is based upon a window size of 3.
Compare this example report to the example for MovingStDevP to see the different values
returned when calculating for a population as opposed to a sample.
The report contains the attributes Customer Region and Customer State, and the metrics
Total Revenue, MovingStDev, RunningStDev, and StDev. A report filter limits data to the
Southwest, Southeast, and Northwest regions. The definition of the MovingStDev metric is
as follows:
MovingStDev<BreakBy={[Customer Region]}, SortBy=<
[Customer State])>([Total Revenue], 3)
MovingSum
Returns the moving sum of the current value and preceding values as defined in the function
arguments. The calculations can be restarted based on attributes set in the function
parameters. This is an OLAP function.
Syntax
MovingSum <BreakBy,SortBy>(Argument, WindowSize)
Where:
•
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Argument is a metric representing a list of numbers.
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•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
WindowSize is a positive integer indicating the number of values to sum in each
calculation.
Expression
Where:
•
yi = metric value at the ith row
•
m = window size
•
n = number of rows/metric values
Example
This simple example illustrates how the MovingSum function moves through a list of values
calculating and returning the sum of a specified number of values. In this case the window
size is set to 2 meaning that the sum in the MovingSum column represents the current value
added to the value from the value list that precedes it. The calculation is shown in the
following table.
Values
MovingSum
10
10
20
30 (20+10)
30
50 (30+20)
15
45 (15+30)
5
20 (5+15)
OLAPAvg (OLAP average)
Returns the average of the current value, preceding values, and succeeding values as
defined in the function arguments. Unlike RunningAvg and MovingAvg, which can only
include values above the current row in the calculation, you can use OLAPAvg to include
values both above and below the current row in the calculation.
The calculations can be restarted based on attributes defined in the function parameters.
Syntax
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OLAPAvg<Distinct, OLAPWinStType, OLAPWinStOffset,
OLAPWinEndType, OLAPWinEndOffset, BreakBy, SortBy>
(Argument)
Where:
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•
Distinct is a TRUE/FALSE parameter that allows you to use all values in the
calculation or to calculate using only the unique values. If you define Distinct to be
true, then the parameters OLAPWinStType, OLAPWinStOffset,
OLAPWinEndType, OLAPWinEndOffset, and SortBy are ignored.
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
OLAPWinStType defines the window type for the starting location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinStType value in parentheses:
▫
Top of data set (0): The calculation starts at the top value as determined by the
BreakBy and SortBy values.
▫
Current row (2): The calculation starts at the current row.
▫
N rows before current row (3): The calculation starts a number of rows before the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
▫
N rows after current row (4): The calculation starts a number of rows after the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
•
OLAPWinStOffset defines the offset of rows from the current row to start the
calculation. You can define this offset if the OLAPWinStType parameter is defined as N
rows before current row (3) or N rows after current row (4).
•
OLAPWinEndType defines the window type for the ending location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinEndType value in parentheses:
▫
Bottom of data set (1): The calculation stops at the bottom value as determined by
the BreakBy and SortBy values.
▫
Current row (2): The calculation stops at the current row.
▫
N rows before current row (3): The calculation stops a number of rows before the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
▫
N rows after current row (4): The calculation stops a number of rows after the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
•
OLAPWinEndOffset defines the offset of rows from the current row to stop the
calculation. You can define this offset if the OLAPWinEndType parameter is defined as
N rows before current row (3) or N rows after current row (4).
•
Argument is a metric representing a list of numbers.
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The function is not valid if your starting point is at a lower point than your ending point.
Example
An OLAPAvg Unit Cost metric is created using the OLAPAvg function based on the Unit
Cost metric, as defined below:
OLAPAvg<OLAPWinStType=3, OLAPWinStOffset=3,
OLAPWinEndType=4, OLAPWinEndOffset=2, BreakBy={Category}
SortBy={Item}>([Unit Cost])
The starting point for the average is defined as three rows before the current row
(OLAPWinStType=3, OLAPWinStOffset=3). The stopping point for the average is
defined as two rows after the current row (OLAPWinEndType=4,
OLAPWinEndOffset=2).
This metric is displayed on a report along with Category, Item, and Unit Cost, as shown
below.
There are a few facts about this data to take note of.
The first value of OLAPAvg Unit Cost is $15. This is calculated by adding $32, $8, and $5 to
get a total of $45. This total of $45 is then divided by three (the number of rows included in
the calculation) to get the final result of $15. These rows are included because the calculation
ends two rows after the current row. Even though the calculation starts three rows before the
current row, there is no data above the current row to include in the calculation.
The fourth value for OLAPAvg Unit Cost is the first value that can include data from all three
rows above the current row to two rows below the current row in the calculation (($32 +
$8 + $5 + $25 + $19 + $20) / 6 = $18.17). Notice that with this calculation a
value of six is used to calculate the average because six rows were included in the average.
While it is not shown on this report, the calculation would restart for the first Item of the next
Category because the function is defined to break by the Category attribute.
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OLAPCount
Returns the count of the current value, preceding values, and succeeding values as defined
in the function arguments. Unlike RunningCount and MovingCount, which can only
include values above the current row in the calculation, you can use OLAPCount to include
values both above and below the current row in the calculation.
The calculations can be restarted based on attributes defined in the function parameters.
Syntax
OLAPCount<Distinct, Null, OLAPWinStType, OLAPWinStOffset,
OLAPWinEndType, OLAPWinEndOffset, BreakBy, SortBy>
(Argument)
Where:
•
Distinct is a TRUE/FALSE parameter that allows you to use all values in the
calculation or to calculate using only the unique values. If you define Distinct to be
true, then the parameters Null, OLAPWinStType, OLAPWinStOffset,
OLAPWinEndType, OLAPWinEndOffset, and SortBy are ignored.
•
Null is a TRUE/FALSE parameter that defines whether null values are included in the
total count.
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
OLAPWinStType defines the window type for the starting location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinStType value in parentheses:
▫
Top of data set (0): The calculation starts at the top value as determined by the
BreakBy and SortBy values.
▫
Current row (2): The calculation starts at the current row.
▫
N rows before current row (3): The calculation starts a number of rows before the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
▫
N rows after current row (4): The calculation starts a number of rows after the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
•
OLAPWinStOffset defines the offset of rows from the current row to start the
calculation. You can define this offset if the OLAPWinStType parameter is defined as N
rows before current row (3) or N rows after current row (4).
•
OLAPWinEndType defines the window type for the ending location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinEndType value in parentheses:
▫
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Bottom of data set (1): The calculation stops at the bottom value. The top value is
determined by the BreakBy and SortBy values.
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▫
Current row (2): The calculation stops at the current row.
▫
N rows before current row (3): The calculation starts a number of rows before the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
▫
N rows after current row (4): The calculation starts a number of rows after the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
•
OLAPWinEndOffset defines the offset of rows from the current row to stop the
calculation. You can define this offset if the OLAPWinEndType parameter is defined as
N rows before current row (3) or N rows after current row (4).
•
Argument is a metric representing a list of numbers.
The function is not valid if your starting point is at a lower point than your ending point.
Example
An OLAPCount Unit Cost metric is created using the OLAPCount function based on the
Unit Cost metric, as defined below:
OLAPCount<OLAPWinStType=3, OLAPWinStOffset=3,
OLAPWinEndType=4, OLAPWinEndOffset=2, BreakBy={Category}
SortBy={Subcategory}>([Unit Cost])
The starting point for the count is defined as three rows before the current row
(OLAPWinStType=3, OLAPWinStOffset=3). The stopping point for the count is
defined as two rows after the current row (OLAPWinEndType=4,
OLAPWinEndOffset=2).
This metric is displayed on a report along with Category, Subcategory, and Unit Cost, as
shown below.
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There are a few facts about this data to take note of.
The first value of OLAPCount Unit Cost is three. This is calculated by counting the current
row and two rows after the current row. Even though the calculation starts three rows before
the current row, there is no data above the current row to include in the calculation.
The fourth entry for OLAPCount Unit Cost is the first entry that can count all three rows
above the current row to two rows below the current row in the calculation. This entry is able
to reach the maximum value of six. Notice that after this entry the count begins to decrease
because there are no longer two rows below the current row to include in the calculation.
The calculation restarts for the first Subcategory of the next Category because the function is
defined to break by the Category attribute.
OLAPMax (OLAP maximum)
Returns the maximum value based on comparing the current value, preceding values, and
succeeding values as defined in the function arguments. Unlike RunningMax and
MovingMax, which can only include values above the current row in the calculation, you can
use OLAPMax to include values both above and below the current row in the calculation.
The calculations can be restarted based on attributes defined in the function parameters.
Syntax
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OLAPMax<OLAPWinStType, OLAPWinStOffset, OLAPWinEndType,
OLAPWinEndOffset, BreakBy, SortBy>(Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
OLAPWinStType defines the window type for the starting location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinStType value in parentheses:
▫
Top of data set (0): The calculation starts at the top value as determined by the
BreakBy and SortBy values.
▫
Current row (2): The calculation starts at the current row.
▫
N rows before current row (3): The calculation starts a number of rows before the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
▫
N rows after current row (4): The calculation starts a number of rows after the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
•
OLAPWinStOffset defines the offset of rows from the current row to start the
calculation. You can define this offset if the OLAPWinStType parameter is defined as N
rows before current row (3) or N rows after current row (4).
•
OLAPWinEndType defines the window type for the ending location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinEndType value in parentheses:
▫
Bottom of data set (1): The calculation stops at the bottom value. The top value is
determined by the BreakBy and SortBy values.
▫
Current row (2): The calculation stops at the current row.
▫
N rows before current row (3): The calculation starts a number of rows before the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
▫
N rows after current row (4): The calculation starts a number of rows after the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
•
OLAPWinEndOffset defines the offset of rows from the current row to stop the
calculation. You can define this offset if the OLAPWinEndType parameter is defined as
N rows before current row (3) or N rows after current row (4).
•
Argument is a metric representing a list of numbers.
The function is not valid if your starting point is at a lower point than your ending point.
Example
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An OLAPMax Unit Cost metric is created using the OLAPMax function based on the Unit
Cost metric, as defined below:
OLAPMax<OLAPWinStType=3, OLAPWinStOffset=3,
OLAPWinEndType=4, OLAPWinEndOffset=2, BreakBy={Category}
SortBy={Subcategory}>([Unit Cost])
The starting point for the calculation is defined as three rows before the current row
(OLAPWinStType=3, OLAPWinStOffset=3). The stopping point for the calculation is
defined as two rows after the current row (OLAPWinEndType=4,
OLAPWinEndOffset=2).
This metric is displayed on a report along with Category, Subcategory, and Unit Cost, as
shown below.
There are a few facts about this data to take note of.
The first value of OLAPMax Unit Cost is $14. This is calculated by returning the maximum
value of $14, $11, and $6. These are included because the calculation ends two rows after
the current row. Even though the calculation starts three rows before the current row, there
is no data above the current row to include in the calculation.
The second value also returns $14 as it returns the maximum value of $14, $11, $6, and $7.
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The third value for OLAPMax Unit Cost is the first entry that includes $26 in its maximum
calculation. The rest of the OLAPMax Unit Cost values for the Books Category return $26
because this is the maximum value, and it is within the window of three rows above the
current row to two rows below the current row.
The calculation restarts for the first Subcategory of the next Category because the function is
defined to break by the Category attribute.
OLAPMin (OLAP minimum)
Returns the minimum value based on comparing the current value, preceding values, and
succeeding values as defined in the function arguments. Unlike RunningMin and
MovingMin, which can only include values above the current row in the calculation, you can
use OLAPMin to include values both above and below the current row in the calculation.
The calculations can be restarted based on attributes defined in the function parameters.
Syntax
OLAPMin<OLAPWinStType, OLAPWinStOffset, OLAPWinEndType,
OLAPWinEndOffset, BreakBy, SortBy>(Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
OLAPWinStType defines the window type for the starting location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinStType value in parentheses:
▫
Top of data set (0): The calculation starts at the top value as determined by the
BreakBy and SortBy values.
▫
Current row (2): The calculation starts at the current row.
▫
N rows before current row (3): The calculation starts a number of rows before the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
▫
N rows after current row (4): The calculation starts a number of rows after the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
•
OLAPWinStOffset defines the offset of rows from the current row to start the
calculation. You can define this offset if the OLAPWinStType parameter is defined as N
rows before current row (3) or N rows after current row (4).
•
OLAPWinEndType defines the window type for the ending location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinEndType value in parentheses:
▫
Bottom of data set (1): The calculation stops at the bottom value. The top value is
determined by the BreakBy and SortBy values.
▫
Current row (2): The calculation stops at the current row.
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▫
N rows before current row (3): The calculation starts a number of rows before the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
▫
N rows after current row (4): The calculation starts a number of rows after the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
•
OLAPWinEndOffset defines the offset of rows from the current row to stop the
calculation. You can define this offset if the OLAPWinEndType parameter is defined as
N rows before current row (3) or N rows after current row (4).
•
Argument is a metric representing a list of numbers.
The function is not valid if your starting point is at a lower point than your ending point.
Example
An OLAPMin Unit Cost metric is created using the OLAPMin function based on the Unit Cost
metric, as defined below:
OLAPMin<OLAPWinStType=3, OLAPWinStOffset=3,
OLAPWinEndType=4, OLAPWinEndOffset=2, BreakBy={Category}
SortBy={Subcategory}>([Unit Cost])
The starting point for the calculation is defined as three rows before the current row
(OLAPWinStType=3, OLAPWinStOffset=3). The stopping point for the calculation is
defined as two rows after the current row (OLAPWinEndType=4,
OLAPWinEndOffset=2).
This metric is displayed on a report along with Category, Subcategory, and Unit Cost, as
shown below.
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There are a few facts about this data to take note of.
The first value of OLAPMinUnit Cost is $6. This is calculated by returning the minimum value
of $14, $11, and $6. These are included because the calculation ends two rows after the
current row. Even though the calculation starts three rows before the current row, there is no
data above the current row to include in the calculation.
All of the OLAPMin Unit Cost values for the Books Category return $6 because this is the
minimum value, and it is within the window of three rows above the current row to two rows
below the current row.
The calculation restarts for the first Subcategory of the next Category because the function is
defined to break by the Category attribute.
OLAPRank
Returns the rank of the current value based on the other values defined by the sorting
criteria. The ranking can be restarted based on attributes defined in the function parameters.
The OLAPRank function allows the ranking to be calculated in the database, rather than
calculating the ranking using the MicroStrategy Analytical Engine.
Be aware of the following:
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•
Since OLAPRank is calculated in the database, you can only use this function if your
database supports the OLAPRank function.
•
Any metrics that use the OLAPRank function must not be defined as smart metrics.
If metrics that use OLAPRank are defined as smart metrics, the calculation is
performed in the MicroStrategy Analytical Engine and an error is returned.
•
You must include at least one metric in the SortBy parameter.
Syntax
OLAPRank<BreakBy, SortBy>()
Where:
•
BreakBy is the attribute indicating where the ranking restarts.
•
SortBy is the attribute or metric by which the data is sorted. For OLAPRank, the
SortBy parameter is also where you can include the metric to perform the calculation
on. You must include at least one metric in the SortBy parameter.
Example
The MicroStrategy Tutorial project includes an Avg Revenue per Customer metric. This
metric can be placed on a report along with the attributes Quarter and Region to display the
quarterly average revenue per customer for each region.
To extend this analysis, you can create an OLAPRank Avg Rev per Customer metric as
defined below:
OLAPRank<BreakBy={Quarter}, SortBy= ([Avg Revenue per
Customer], Region@ID) >()
When this metric is included on a report with Quarter, Region, and Avg Revenue per
Customer, it displays the regional rank of the quarterly average revenue per customer for
each region. This is shown in the report below.
The report has been sorted by Quarter, and then by the OLAPRank Avg Rev per Customer
values.
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The report shown above displays regions based on their average revenue per customer for
each quarter, sorted from the lowest average revenue per customer to the highest. This
analysis allows you to quickly see how regions are performing based on average revenue
per customer over different quarters. In the first three quarters of data shown above, Web
has the lowest average revenue per customer. However, there is some variation in the
performance of the other regions. Performing this analysis over extended periods of time can
help to show trends in revenue and regional performance.
OLAPSum
Returns the summation of the current value, preceding values, and succeeding values as
defined in the function arguments. Unlike RunningSum and MovingSum, which can only
include values above the current row in the calculation, you can use OLAPSum to include
values both above and below the current row in the calculation.
The calculations can be restarted based on attributes defined in the function parameters.
Syntax
OLAPSum<Distinct, OLAPWinStType, OLAPWinStOffset,
OLAPWinEndType, OLAPWinEndOffset, BreakBy, SortBy>
(Argument)
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Where:
•
Distinct is a TRUE/FALSE parameter that allows you to use all values in the
calculation or to calculate using only the unique values. If you define Distinct to be
true, then the parameters OLAPWinStType, OLAPWinStOffset,
OLAPWinEndType, OLAPWinEndOffset, and SortBy are ignored.
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
OLAPWinStType defines the window type for the starting location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinStType value in parentheses:
▫
Top of data set (0): The calculation starts at the top value as determined by the
BreakBy and SortBy values.
▫
Current row (2): The calculation starts at the current row.
▫
N rows before current row (3): The calculation starts a number of rows before the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
▫
N rows after current row (4): The calculation starts a number of rows after the
current row. You define this offset of rows with the OLAPWinStOffset parameter
described below.
•
OLAPWinStOffset defines the offset of rows from the current row to start the
calculation. You can define this offset if the OLAPWinStType parameter is defined as N
rows before current row (3) or N rows after current row (4).
•
OLAPWinEndType defines the window type for the ending location of the calculation.
Select one of the following options, listed by the name of the setting and its
corresponding OLAPWinEndType value in parentheses:
▫
Bottom of data set (1): The calculation stops at the bottom value. The top value is
determined by the BreakBy and SortBy values.
▫
Current row (2): The calculation stops at the current row.
▫
N rows before current row (3): The calculation stops a number of rows before the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
▫
N rows after current row (4): The calculation stops a number of rows after the
current row. You define this offset of rows with the OLAPWinEndOffset
parameter described below.
•
OLAPWinEndOffset defines the offset of rows from the current row to stop the
calculation. You can define this offset if the OLAPWinEndType parameter is defined as
N rows before current row (3) or N rows after current row (4).
•
Argument is a metric representing a list of numbers.
The function is not valid if your starting point is at a lower point than your ending point.
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Example
An OLAPSum Unit Cost metric is created using the OLAPSum function based on the Unit
Cost metric, as defined below:
OLAPSum<OLAPWinStType=3, OLAPWinStOffset=3,
OLAPWinEndType=4, OLAPWinEndOffset=2, BreakBy={Category}
SortBy={Subcategory}>([Unit Cost])
The starting point for the summation is defined as three rows before the current row
(OLAPWinStType=3, OLAPWinStOffset=3). The stopping point for the summation is
defined as two rows after the current row (OLAPWinEndType=4,
OLAPWinEndOffset=2).
This metric is displayed on a report along with Category, Subcategory, and Unit Cost, as
shown below.
There are a few facts about this data to take note of.
The first value of OLAPSum Unit Cost is $30.82. This is calculated by adding $13.93,
$10.75, and $6.13. These are included because the calculation ends two rows after the
current row. Even though the calculation starts three rows before the current row, there is no
data above the current row to include in the calculation.
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The data displayed on the report is rounded to the nearest cent, which can give the
impression that some calculations are slightly incorrect. For example, adding $13.93, $10.75,
and $6.13 actually totals $30.81 rather than $30.82 as displayed on the report. This difference
is because the data is rounded up for display on the report. You can display more decimal
values for the Unit Cost and OLAPSum Unit Cost metrics to see the exact values.
The fourth value for OLAPSum Unit Cost is the only value that can include data from all three
rows above the current row to two rows below the current row in the calculation ($13.93 +
$10.75 + $6.13 + $7.20 + $25.93 + $9.43 = $73.38).
The final OLAPSum Unit Cost value for the Books Category can only include the Unit Cost
value for the current row and the three rows above it ($6.13 + $7.20 + $25.93 +
$9.43 = $48.70). It cannot include two rows below the current row because the
calculation restarts for the first Subcategory of the next Category. The calculation restarts
because the function is defined to break by the Category attribute.
RunningAvg (running average)
Moves through the values in a list and returns the running average, adding the current value
to the sum of the preceding values and dividing by the current count of values. The
calculation can restart based on attribute groupings identified in the parameter settings. This
is an OLAP function.
Syntax
RunningAvg <BreakBy,SortBy>(Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing a list of numbers.
Expression
Where:
•
yi = metric value at the ith row
•
m = window size
•
n = number of rows/metric values
Example
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This simple example illustrates how the RunningAverage function walks through a list of
values calculating and returning the new average with the addition of each value. The
calculation is shown in the following table.
Values
RunningAverage
10
10 (10/1)
20
15 (30/2)
30
20 (60/3)
40
25 (100/4)
50
30 (150/5)
RunningCount
Returns the running count for each value in a list of values, returning the current count after
each value. The count can be restarted based upon attributes identified in the parameter
settings. This is an OLAP function.
Syntax
RunningCount<BreakBy,SortBy>(Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing the list of values.
Expression
Where:
•
1i = 0 if the ith row of argument is NULL
1i = 1 otherwise
•
n = number of rows/metric values
Example
This simple example demonstrates how the RunningCount function counts rows of data.
This report uses the attributes Region and Employee, and the metrics Revenue and
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Running count. A filter is applied so the only Regions displayed are South, Northwest, and
Southwest. The Running count metric is defined as follows:
RunningCount<BreakBy={Region}, SortBy= (Value) >(Revenue)
•
The count restarts for every Region.
•
The entries are counted based on the value of the metric Revenue in ascending order
(the lowest value is counted as 1, next lowest is 2, and so on).
RunningMax (running maximum)
Returns the running maximum value in a list of values by comparing the current and
preceding values. The evaluation can restart based on attributes identified in the parameter
settings. This is an OLAP function.
Syntax
RunningMax <BreakBy, SortBy>(Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing the list of numbers.
Expression
Where:
188
•
yi = metric value at the ith row
•
n = number of rows/metric values
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Example
This simple example illustrates how the RunningMax function moves through a list of values
comparing each value to the highest value identified so far and returning the maximum value
as it progresses. The calculation is shown in the following table.
Values
RunningMaximum
8
6
8 (8>6)
10
10 (10>8)
9
10 (10>9)
5
10 (10>5)
RunningMin (running minimum)
Returns the running minimum value in a list of values by comparing current and preceding
values. The evaluation can restart based on attributes identified in the parameter settings.
This is an OLAP function.
Syntax
RunningMin <BreakBy, SortBy> (Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing the list of numbers.
Expression
Where:
•
yi = metric value at the ith row
•
n = number of rows/metric values
Example
This simple example illustrates how the RunningMinimum function walks through a list of
values comparing each value to the lowest value identified so far and returning the minimum
value as it progresses. The calculation is shown in the following table.
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Values
RunningMinimum
8
6
6 (6<8)
10
6 (6<10)
9
6 (6<9)
5
5 (5<6)
RunningStDevP (running standard deviation of a
population)
Returns the running standard deviation of a population for a value expression. The list of
values supplied is the population. The calculation can restart based on attributes identified in
the function parameter settings. This is an OLAP function.
Syntax
RunningStDevP <BreakBy, SortBy> (Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing the list of numbers.
Expression
Where:
•
yi = metric value at the ith row
•
n = number of rows/metric values
Example
This example shows a report where the running standard deviation of the revenue is
calculated. This calculation is based on the assumption that the list of values supplied in the
metric represents the entire population of the data for which you want to obtain the standard
deviation. The calculation starts over for each region, and the information is sorted within the
region by state in ascending order.
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Compare this example report to the example for RunningStDev to see the different values
returned when calculating for a population as opposed to a sample.
The report contains the attributes Customer Region and Customer State, and the metrics
Total Revenue, RunningStDevP, and StDevP. A report filter limits data to the Southwest,
Southeast, and Northwest regions. The definition of the RunningStDevP metric is as follows:
RunningStDevP<BreakBy={[Customer Region]}, SortBy=<
[Customer State])>([Total Revenue])
RunningStDev (running standard deviation)
Returns the running standard deviation of a sample for a value expression. The list of values
supplied is the sample. The calculation can restart based on attributes identified in the
function parameters. This is an OLAP function.
Syntax
RunningStDev<BreakBy,SortBy>(Argument)
Where:
•
BreakBy is the attribute indicating where the calculation restarts.
•
SortBy is the attribute or metric by which the data is sorted.
•
Argument is a metric representing the list of numbers.
Expression
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Example
This example shows a report where the running standard deviation of the revenue is
calculated. This calculation is based on the assumption that the list of values supplied in the
metric represents a sample of the data for which you want to obtain the standard deviation.
The calculation starts over for each region, and the information is sorted within the region by
state in ascending order.
Compare this example report to the example for RunningStDevP to see the different values
returned when calculating for a population as opposed to a sample.
The report contains the attributes Customer Region and Customer State, and the metrics
Total Revenue, RunningStDev, and StDev. A report filter is used to limit the data to the
Southwest, Southeast and Northwest regions. The definition of the RunningStDev metric is
as follows:
RunningStDev<BreakBy={[Customer Region]}, SortBy=<
[Customer State])>([Total Revenue])
RunningSum
This function returns the running sum of the values in a list, adding the current value to the
preceding values. It can be used to maintain running totals of values in specific categories as
well as an entire set. The calculation can restart based on attributes identified in the
parameter settings. This is an OLAP function.
Syntax
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RunningSum <BreakBy,SortBy>(Argument)
Where:
•
BreakBy is the parameter that sets the attribute designating where the calculation
restarts.
•
SortBy is the parameter that sets the attribute or metric by which the data is sorted.
•
Argument is a metric representing the list of numbers.
Expression
Example
Example 1: A running sum displays the results of each calculation as it works towards the
total of a set of values. Using the value set (1, 2, 3, 4, 5), the following table illustrates the
running sum and its calculations.
Values
RunningSum
1
1 (0+1)
2
3 (1+2)
3
6 (3+3)
4
10 (6+4)
5
15 (10+5)
Example 2: This example shows a report where the running sum of the revenue is
calculated. The calculation starts over for each region, and the information is sorted within
the region by state in ascending order. The report contains the attributes Customer Region
and Customer State, and the metrics Total Revenue and Running Sum. A report filter limits
data to the Southwest, Southeast, and Northwest regions. The definition of the Running
Sum metric is as follows:
RunningSum<BreakBy={[Customer Region]}, SortBy=<[Customer
State])>([Total Revenue])
Note that the subtotaled Total Revenue is equal to the last value in the Running Sum column
for each region.
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WeightedCorr (weighted correlation)
A correlation of two values describes the degree to which the values are related or
associated. Values that are closely related with either a positive or negative correlation will
have a correlation close to 1 or -1 respectively, while values that are not correlated at all will
have a correlation close to 0.
A weighted correlation allows you to apply a weight, or relative significance to each value
comparison. Correlation comparisons with a higher value for their weight are considered as
more significant when compared to the other value comparisons.
To determine the correlation of two values without applying a weight to the comparisons, see
Correlation, page 271.
Syntax
WeightedCorr <FactID> (Argument1, Argument2, Weight)
Where:
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
Argument1 and Argument2 are attributes, facts, or metrics representing lists of
numbers for comparison. A correlation is calculated on these values to determine the
level of association between the two values.
•
Weight is an attribute, fact, or metric representing a list of numbers to define the weight
of each comparison.
Expression
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Where:
•
x and y are the values being compared.
•
w is the weight applied to each comparison.
Usage notes
•
The correlation coefficient is measured on a scale that varies from 1 to - 1. So only a
value between -1 and 1 is returned.
•
Complete correlation between two variables is expressed by either 1 or -1. When one
variable increases as the other increases, the correlation is positive. When one
decreases as the other increases, the correlation is negative. Complete absence of
correlation is represented by 0.
•
If an array or reference argument contains text, logical values, or empty cells, those
values are ignored; however, cells with the value zero are included.
•
If Argument1 and Argument2 have a different number of data points, an error is
returned.
•
If either Argument1 or Argument2 is empty, or if the standard deviation of their values
equals zero, an error is returned.
Example
Your company keeps employee statistics including their overall satisfaction with their job and
role at the company, as well as their performance score. While these are both important
statistics, it can also be beneficial to know how closely related these two statistics are.
Knowing this information can help determine if an employee’s satisfaction with their job is
related to their performance.
A report including both the correlation and weighted correlation of these statistics, displayed
as percentages, is shown below for employees at the executive level:
The expressions for these calculations are as follows:
•
Correlation:
Correlation([Avg. Performance Score], [Avg.
Satisfaction Score]) {Level , ~ }
•
Weighted Correlation:
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WeightedCorr([Avg. Performance Score], [Avg.
Satisfaction Score], Tenure) {Level, ~}
Both the correlation and weighted correlation indicate that employee satisfaction and
performance are positively correlated, meaning that when one increases so does the other.
The weighted correlation includes an employee’s tenure into the correlation calculation. This
means that more significance is given to correlation comparisons for employees that have
been with the company longer. For the executive level employees, factoring in tenure results
in a smaller correlation between satisfaction and performance. This type of analysis can be
crucial in determining how performance can be improved or maintained both for new hires
and long tenured employees.
WeightedCov (weighted covariance)
Covariance is used to examine the relationship between two data sets. For instance, the
covariance can be used to examine whether an increase in income is related to higher
education levels. A covariance greater or less than zero indicates a relationship, while a
value of zero indicates no relationship.
A weighted covariance allows you to apply a weight, or relative significance to each value
comparison. Covariance comparisons with a higher value for their weight are considered as
more significant when compared to the other value comparisons.
To determine the covariance of two values without applying a weight to the comparisons, see
Covariance, page 271.
Syntax
WeightedCov <FactID> (Argument1, Argument2, Weight)
Where:
•
Argument1 and Argument2 are attributes, facts, or metrics representing lists of
numbers for comparison. A covariance is calculated on these values to determine the
level of association between the two values.
•
Weight is an attribute, fact, or metric representing a list of numbers to define the weight
of each comparison.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
Expression
•
x and y are the Argument1 and Argument2 values being compared.
•
w is the weight applied to each comparison.
•
= average value of x
•
= average value of y
Usage notes
The following are invalid conditions:
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•
If an array or reference argument contains text, logical values, or empty cells, those
values are ignored; however, cells with the value zero are included.
•
If Argument1 and Argument2 have a different number of data points, an error is
returned.
•
If either Argument1 or Argument2 is empty, an error is returned.
Example
Your company keeps employee statistics including their overall satisfaction with their job and
role at the company, as well as their performance score. While these are both important
statistics, it can also be beneficial to know how closely related these two statistics are.
Knowing this information can help determine if an employee’s satisfaction with their job is
related to their performance.
A report including both the covariance and weighted covariance of these statistics, is shown
below for employees at the executive level:
The expressions for these calculations are as follows:
•
Covariance:
Covariance([Avg. Performance Score], [Avg. Satisfaction
Score]) {Level , ~ }
•
Weighted Covariance:
WeightedCov([Avg. Performance Score], [Avg.
Satisfaction Score], Tenure) {Level, ~}
Both the covariance and weighted covariance indicate that employee satisfaction and
performance are positively related, meaning that when one increases so does the other. The
weighted covariance includes an employee’s tenure into the covariance calculation. This
means that more significance is given to covariance comparisons for employees that have
been with the company longer. For the executive level employees, factoring in tenure results
in a smaller relationship between satisfaction and performance. This type of analysis can be
crucial in determining how performance can be improved or maintained both for new hires
and long tenured employees.
WeightedMean
An average, also known as an arithmetic mean, is the sum of a set of values divided by the
number of values in the set.
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A weighted mean allows you to apply a weight, or relative significance to each value when
determining an average. Values with a higher value for their weight are considered as more
significant when compared to the other values.
To calculate an average without applying a weight to the values, see Avg (average), page 93.
Syntax
WeightedMean<FactID>(Argument1, Weight)
Where:
•
Argument1 is an attribute, fact, or metric representing a list of numbers that are used to
calculate an average.
•
Weight is an attribute, fact, or metric representing a list of numbers to define the weight
of each value.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
Expression
Example
You can calculate a weighted mean to apply greater significance to certain values used to
determine an average. For example, you can use the percent growth of revenue as a way to
define revenue values as more or less significant for an average. The report shown below
contains the attribute Customer Region and the metrics Revenue, Percent Growth, Average
Revenue, and Weighted Mean Revenue. The report also includes the Year attribute in the
page-by to show results by year.
The expressions for the average and weighted mean calculations are as follows:
•
Average Revenue:
Avg(Revenue){Year}
•
Weighted Mean Revenue:
WeightedMean(Revenue, [Percent Growth]){Year}
The weighted mean takes into account the weights of each revenue value. For example,
since Central’s percent growth is greater than Northwest’s percent growth, and the weight in
this example is the percent growth, Central’s revenue is given more significance in the
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weighted mean calculation. This results in $2,229,195 for the Weighted Mean Revenue as
compared to $2,122,695 for the Average Revenue for all customer regions for the given
year.
Notice that while the Average Revenue and Weighted Mean Revenue are applicable to a
year in this example, the values are displayed for every Customer Region row. One way to
simplify the display of this information is to include this data on a dashboard. You can include
the Average Revenue and Weighted Mean Revenue results in text fields along with
additional visualizations of the data, as shown below.
WeightedStDev (weighted standard deviation of a
sample)
The standard deviation is an indicator of how widely values in a group differ from the mean
(see StDev (standard deviation of a sample), page 111). It is useful for comparing different
sets of values with a similar mean.
A weighted standard deviation allows you to apply a weight, or relative significance to each
value in a set of values. Values with a higher value for their weight are considered as more
significant to a sample as compared to the other values in a sample.
WeightedStDev returns the weighted standard deviation of a population based on a
sample. This is a group-value function.
Syntax
WeightedStDev <Distinct, FactID>(Argument, Weight)
Where:
•
Argument is an attribute, fact, or metric representing a list of numbers.
•
Weight is an attribute, fact, or metric representing a list of numbers to
define the weight of each value.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
Expression
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Where:
•
wi: The weight of the ith value. Values with a higher value for their weight are considered
as more significant to a sample as compared to the other values in a sample.
•
N’: The number of weights that are not equal to zero.
•
xw: The weighted mean of the values.
Usage notes
•
In this function, arguments correspond to a population sample as opposed to the entire
population. For entire populations, see StDevP (standard deviation of a population),
page 110.
Example
This example shows a report where the standard deviation and a weighted standard
deviation of the revenue are calculated. This calculation is based on the assumption that the
list of values supplied in the metric represents a sample of the data for which you want to
obtain the standard deviation. The calculation is based on the revenue values for each state
within a region and calculated at the region level.
The report contains the attributes Customer Region and Customer State, and the metrics
Revenue, Standard Deviation, and Weighted Standard Deviation.
The definition of the standard deviation metrics are as follows:
•
Standard Deviation:
StDev(Revenue){[Customer Region], ~}
•
Weighted Standard Deviation:
WeightedStDev(Revenue, Revenue) {[Customer Region] , ~
}
For this example, the revenue values are also used as the weights given to each
revenue value included in the standard deviation.
The report is shown below:
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The weighted standard deviation also takes into account the weights of each revenue value.
For example, since Connecticut’s revenue is greater than Maine’s revenue, and the weight in
this example is the revenue value, Connecticut’s revenue is given more significance in the
weighted standard deviation calculation. This results in $957,689 for the standard deviation
as compared to $1,141,237 for the weighted standard deviation of Northeast revenue.
Notice that while the standard deviation and weighted standard deviation are applicable to a
Customer Region in this example, the values are displayed for every Customer State row.
One way to simplify the display of this information is to include this data on a dashboard. You
can include the standard deviation and weighted standard deviation results in text fields
along with additional visualizations of the data, as shown below.
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Rank and NTile functions
Rank and NTile functions are used to qualify a list of values relative to the other values. For
example, out of the four quarters in a year, you want to rank total revenue from one to four,
with one being the top revenue for the year.
Functions including Rank, Percentile, PercentRank, and PercentRankRelative
allow you to view the ranking of values such as a simple integer list or various types of
percentages.
NTile functions are used to group the values in an ordered list into one of several buckets or
NTiles. Each element in the list is assigned an integer corresponding to the bucket to which it
belongs. The various NTile functions differ in how the buckets are defined. Some functions
allow you to define the number of buckets, others allow you to define the size of the buckets,
and so on.
NTile
NTile functions are used to group the values in an ordered list into one of several buckets or
NTiles. For the NTile function, the buckets are calculated so that each bucket has exactly
the same number of rows assigned to it or at most one row more than the others (the
exception is that identical value rows are placed in the same bucket). For example, if you
have 100 rows in a partition and define an NTile function with four buckets, 25 rows will be
assigned a value of 1, 25 rows will have value 2, and so on. These buckets are referred to as
equiheight buckets.
If the number of rows in the partition does not divide evenly into the number of buckets, then,
barring identical value rows, the number of rows assigned per bucket will differ by one at
most. The extra rows are added to buckets using the calculations ceiling(1*
(buckets/remainder)),...,
ceiling(remainder*(buckets/remainder)).
For example, if there are 103 distinct value rows in a partition which has an
NTile<Tiles=5>() function, the first 20 rows will be in the first bucket, the next 21 in the
second bucket, the next 20 in the third bucket, the next 21 in the fourth bucket, and the final
21 in the fifth bucket. The calculations ceiling(1*(5/3))=2, ceiling(2*(5/3))=4,
and ceiling(3*(5/3))=5 include one of the extra three rows each in the second,
fourth, and fifth buckets.
NTile distributes the values in the specified metric, sorted in either ascending or
descending order, over a user-defined number of buckets. Each bucket has an equal
number of elements (if possible). This is an OLAP function.
Syntax
NTile <Ascending, Tiles, BreakBy> (Argument)
Where:
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•
Ascending is a TRUE/FALSE parameter that designates the organization of data
within the NTiles.
•
BreakBy is the parameter that sets the attribute determining where the calculation
restarts.
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•
Tiles is a positive integer that designates the number of buckets or NTiles.
•
Argument is a metric representing a list of values to be distributed equally (if possible)
into n buckets.
Example
Example 1: If you define Tiles=4 for a metric that contains 20 values, the function
distributes the numbers as follows:
•
Values 1 through 5 in bucket 1
•
Values 6 through 10 in bucket 2
•
Values 11 through 15 in bucket 3
•
Values 16 through 20 in bucket 4
Example 2: This example shows a report where the customer states were sorted based on
revenue and then divided among a specified number of buckets. The number of buckets is
defined as 8. The report includes the attribute Customer State and the metrics Total
Revenue and NTile. The NTile metric is a derived one, the syntax for which is as follows:
NTile<Tiles=8>([Total Revenue])
The resulting report divides the 48 states into 8 NTiles, each containing 6 elements (states).
Within each band the data is sorted in ascending order by the attribute Customer State. A
portion of the report is displayed as follows.
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NTileSize
NTileSize distributes the values in the specified metric, sorted in either ascending or
descending order, with the same number of elements in each bucket. The number of
elements in each bucket is user-defined. This is an OLAP function.
Syntax
NTileSize <Ascending, BreakBy> (Argument, Size)
Where:
204
•
Ascending is a TRUE/FALSE parameter that designates the organization of data
within the NTiles.
•
BreakBy is the parameter that sets the attribute determining where the calculation
restarts.
•
Argument is a metric representing a list of values to be distributed in buckets.
•
Size is a positive integer that designates the number of elements per bucket.
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Example
Example 1: If you define the Size as 2, the function returns buckets containing two values
each. If there are six values, values one and two go in bucket 1, values three and four go in
bucket 2, and values five and six go in bucket 3.
Example 2: This example shows a report where the customer states are sorted by revenue
and then placed in buckets based on the number of elements that can fit in a bucket. The
number of elements per bucket size is defined as 8. The report includes the attribute
Customer State and the metrics Total Revenue and NTileSize. The syntax for the NTileSize
metric is as follows:
NTileSize ([Total Revenue],8)
The resulting report divides the 48 states into 6 buckets each containing 8 elements (states).
A portion of the resulting report displays as follows.
NTileValue
NTileValue distributes the values in the metric by value ranges over a user-defined
number of buckets, sorted in either ascending or descending order. Each bucket is the same
size in terms of the range of values contained in the bucket, but the number of elements per
bucket is not equal. This is an OLAP function.
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Syntax
NTileValue <Ascending, Segments, BreakBy> (Argument)
Where:
•
Ascending is a TRUE/FALSE parameter that designates the organization of data
within the NTiles.
•
Segments is a positive integer designating the number of buckets in which the values
are distributed.
•
BreakBy is the parameter that sets the attribute determining where the calculation
restarts.
•
Argument is a metric representing a list of values to be distributed into buckets.
Example
Example 1: If you define the Segments=4, and the minimum value in the range is 5 and the
maximum is 105, the bucket distribution is as follows, where x is the value:
•
5 ≤ x < 30 in bucket 1
•
30 ≤ x < 55 in bucket 2
•
55 ≤ x < 80 in bucket 3
•
80 ≤ x ≤ 105 in bucket 4
Example 2: This example shows a report where the customer states are sorted based on
revenue and then placed in buckets based on the value range to which they belong. The
number of buckets (segments) is defined as 4. The range of values is divided by the number
of buckets and the result is used to define four value ranges into which all the values fall. The
report includes the attribute Customer State and the metrics Total Revenue and NTileValue.
The syntax for the NTileValue metric is as follows:
NTileValue<Segments=4>([Total Revenue])
The resulting report displays the 48 states distributed across 4 buckets. Notice that the
number of elements in each bucket is not equal as more values fall into one range than
another. A portion of the resulting report is displayed as follows.
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Example 3: Histogram
The reports and components used in this example are available in the MicroStrategy Tutorial
under the following folder:
MicroStrategy Tutorial\Public Objects\Reports\MicroStrategy Platfo
rm Capabilities\Advanced Analytics\Statistics and Forecasting\Hist
ogram
How are my customers distributed (classified) based on sales data?
You are interested in finding out how your customers are distributed based on their
contributions to sales data. This example segments customers into 10 separate groups, with
each group representing the customers within a 10% increment of sales. For example, the
first segment includes the customers who spend in the lowest 10% of the sales, while the
final, tenth segment includes the customers who spend in the top 10% of the sales.
This example uses the following concepts:
•
Analytical functions: NTileValue
•
Custom group
NTileValue distributes values into buckets based on minimum and maximum values. These
tiles are assigned an integer and the contents of each bucket denoted accordingly. For
example, all customers that fall in the first bucket are assigned a 1, all customers in the
second bucket are assigned a 2, and so on. This function is computed by Intelligence Server.
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The metric using NTileValue is then used to create a dynamic classification of Customers
using a custom group.
To create a general solution that can be used by different users, use object prompts (see
instructions below).
Create the following object prompts:
•
Choose a base fact. This object prompt asks the user to select a fact: Revenue, Profit, or
Units Sold.
•
Choose a sample set level. This object prompt asks the user to select an attribute:
Customer, Item, or Day.
Once you have identified the basic fact and attribute to analyze, you can start building other
objects. Take the following steps:
1
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Create a metric defined as follows:
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Sample Set Metric = Sum(?[Choose a base fact]){~, ?
[Choose a sample set level]}
2
To put the value in the proper buckets (tiles), create another metric defined as:
Decile By Value = NTileValue<Segments=10> ([Sample Set
Metric])
3
Create a dynamic classification of the subject attribute (Customers, Item, or Day) by
building a custom group using banding. Use the metric Decile By Value, the banding
type “band count” and set the band count to 10, starting at 1 and stopping at 10.
4
Choose to show only individual items within this element since the objective is to build a
graph (histogram) for this custom group.
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5
Count the number of elements in the new classification to display a histogram. To do
this, create a dummy metric defined as follows:
Count of Samples = Count(1){~}
6
Since there is no column to perform the count, define the metric so that the database can
calculate how many attribute elements are in each custom group element.
7
Add the Decile by Value custom group to the row axis, and the Count of Samples metric
to the column axis.
8
Execute the report with attribute Customer and fact Revenue, and the Decile
Histogram, in Grid mode, displays as follows:
The same report, in graph mode, appears as follows:
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The SQL generated for the Decile by Histogram is as follows:
Pass0 - Duration: 0:00:00.25
select a11.[CUSTOMER_ID] AS CUSTOMER_ID,
a11.[TOT_DOLLAR_SALES] AS WJXBFS1
from [CUSTOMER_SLS] a11
Pass1 - Duration: 0:00:00.09
create table ZZT1Y02011CMQ000 (
CUSTOMER_ID SHORT,
DA56 LONG)
Pass2 - Duration: 0:00:00.00
[An Analytical SQL]
Pass3 - Duration: 0:01:13.64
insert into ZZT1Y02011CMQ000 values (1499, 1)
Pass4 - Duration: 0:00:00.32
select a11.[DA56] AS DA56,
count(1.0) AS WJXBFS1
from [ZZT1Y02011CMQ000] a11
group by a11.[DA56]
Pass5 - Duration: 0:00:00.03
drop table ZZT1Y02011CMQ000
•
Pass0 computes the revenue for each Customer using the Sample Set Metric.
•
The next three passes (Pass1, Pass2, and Pass3) prepare the dynamic classification of
Customer for the custom group. This series of SQL passes essentially builds a dynamic
lookup table for the classification that is requested. The column DA stands for Dynamic
Attribute. The Intelligence Server computes the NTileValue function. This is indicated by
[An Analytical SQL] in Pass3. The value is then inserted, with the new
classification, into the dynamic lookup table for the custom group.
•
Pass4 calculates the number of customers that belong to each custom group element.
•
Pass5 drops the temporary table.
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NTileValueSize
NTileValueSize distributes the values in the metric across buckets based on a userspecified value range, sorted in either ascending or descending order. This is an OLAP
function.
Syntax
NTileValueSize <Ascending, BreakBy> (Argument, Size)
Where:
•
Ascending is a TRUE/FALSE parameter that designates the organization of data
within the NTiles.
•
BreakBy is the parameter that sets the attribute determining where the calculation
restarts.
•
Argument is a metric representing the list of values to be distributed across buckets.
•
Size is a real number designating the size of the range of values for each bucket.
Example
Example 1: If you define size as 1000 and your range of values begins at 1200, the first
bucket contains values 1200 through 2199, the second bucket contains values 2200 through
3199, and so on until every value is in a bucket. It does not matter how many values are in
each bucket; it only matters that their value is within the bucket range.
Example 2: This example shows a report where the customer states were sorted based
upon revenue and then placed in buckets based upon the value range to which they belong.
The value range is defined as 100,000. Beginning at the lowest metric value, in this case
18,654, the first bucket contains values from 18,654 to 118,653, the next bucket ranges from
118,654 to 218,653, and so on until all values are in buckets. The highest value in the report
is 1,839,238, which falls into bucket 19 and is the only value in that range.
The report includes the attribute Customer State and the metrics Total Revenue and
NTileValueSize. The syntax for the NTileValueSize metric is as follows:
NTileValueSize ([Total Revenue], 100000)
A portion of the resulting report is displayed as follows.
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Percentile
Returns the nth percentile of values in a given range. This function can be used to establish
thresholds indicating, for example, which states have revenue above the 75th percentile for
their region. This is a group-value function.
Syntax
Percentile <Ascending, FactID> (Argument, Percent)
Where:
•
Ascending is a TRUE/FALSE parameter that indicates the organization of the data.
•
Argument is an attribute, fact or metric representing a list of numbers.
•
Percent is the decimal value of the percent for which you want to use as a threshold.
Example
Example 1: If you have an argument containing the values 1, 2, 3, and 4 and you want to find
the threshold of the 3oth percentile, the syntax is as follows:
Percentile ({1, 2, 3, 4}, 0.3) = 1.9
1.9 is the level of the 30th percentile. All values above 1.9 are greater than the 30th
percentile.
Example 2: This example shows a report where only states with Total Revenue greater than
the threshold for the 75th percentile in its region are displayed. The report contains the
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attributes Customer Region and Customer State, and the metrics Total Revenue and
Percentile. A view filter is then applied to the report so that it only displays states where Total
Revenue is greater than Percentile.
The syntax for the Percentile metric is as follows:
Percentile([Total Revenue], .75){[Customer Region]}
PercentRank
This function is used to display the ranking of values as a percentage. For example you can
rank profit for a particular region based on the profit for all regions.
The calculation can restart based on attributes identified in the parameter settings. This is an
OLAP function.
You can also rank values in the following ways:
•
You use the Rank function (see Rank , page 217) to rank values as an integer value. For
example you can rank profit for five categories from 1 to 5.
•
You use the PercentRankRelative function (see PercentRankRelative, page 215)
to rank values as a percentage, with the ranking based on another data set. For example
you can rank profit for the current year based on last year’s profit.
Syntax
PercentRank <inclusive, BreakBy> (Argument)
Where:
•
Argument is a fact or metric representing a list of numbers that are to be ranked.
•
inclusive is a TRUE/FALSE parameter that indicates whether the rank is inclusive or
exclusive:
▫
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TRUE (default): The percent rank is in a range from 0 to 1 inclusive. This means that
0% and 100% are included.
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▫
•
FALSE: The percent ranks is in a range from 0 to 1 exclusive. This means that 0%
and 100% are excluded.
BreakBy is the parameter that designates where the calculation should restart.
Example
The example report shown below displays revenue information across regions for a given
quarter. You can use PercentRank to return the rank of revenue for each region during a
given quarter.
The definition for the Percent Rank Revenue metric in this example is:
PercentRank<BreakBy={Quarter}>(Revenue)
The Percent Rank Revenue metric in the report shown above provides the rank of each
revenue value. You can see that the highest value in the Northeast region is displayed as
100% while the lowest value in the Northwest region is displayed as 0%.
This report also includes a metric that uses the PercentRankRelative function, ranking
the revenue based on last quarter’s revenue. For information on PercentRankRelative
and an explanation of these results, see PercentRankRelative, page 215.
PercentRankRelative
This function is used to display the ranking of values as a percentage, with the ranking based
on a secondary data set. For example you can rank the current year’s profit for a particular
region based on (relative to) the profit from the previous year.
The calculation can restart based on attributes identified in the parameter settings. This is an
OLAP function.
You can also rank values in the following ways:
•
The Rank function (see Rank , page 217) ranks values as an integer value. For
example, you can rank profit for five categories from 1 to 5.
•
The PercentRank function (see PercentRank , page 214) ranks values as a
percentage based on those values as the data set. For example, you can rank profit for a
particular region based on the profit for all regions.
Syntax
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PercentRankRelative <inclusive, rankOutliers,
significance, truncate, BreakBy> (Argument, Rank)
Where:
•
Argument is a fact or metric representing a list of numbers that are to be ranked.
•
Rank is a fact or metric representing a list of numbers that are used to determine the
rank of the values provided with Argument. The values for Rank should be within the
same range as the values of Argument to provide a relevant ranking. For example,
using profit values to rank revenue would rarely result in relevant results, as profit is likely
to have a different range of values than Revenue. However, using last year’s profit to
rank this year’s profit can provide relevant results as the profits between last year and
this year are more likely to be in the same range of values.
•
inclusive is a TRUE/FALSE parameter that indicates whether the rank is inclusive or
exclusive:
•
▫
TRUE (default): The percent rank is in a range from 0 to 1 inclusive. This means that
0% and 100% are included.
▫
FALSE: The percent rank is in a range from 0 to 1 exclusive. This means that 0%
and 100% are excluded.
rankOutliers is a TRUE/FALSE parameter that indicates whether outlier values are
included in the calculation:
▫
TRUE: Values from Rank that are outside of the range of values from Argument
are included in the ranking. This often results in showing values such as 100% or
0%.
▫
FALSE (default): Values from Rank that are outside of the range of values from
Argument are not included in the ranking. The results are left blank rather than
showing a percentage.
•
significance (default is 3) determines the number of digits that are used to perform
each calculation. You can provide any integer value from 1 to 9 for this parameter.
•
truncate is a TRUE/FALSE parameter that indicates whether the final result is
rounded or truncated:
•
▫
TRUE (default): The final result is truncated, based on the significance applied to the
calculation. For example, using the default of significance=3, the fourth digit is
dropped and is not used to round the third digit.
▫
FALSE: The final result is rounded, based on the significance applied to the
calculation. For example, using the default of significance=3, the fourth digit is
used to round the third digit.
BreakBy is the parameter that designates where the calculation should restart.
Example
The example report shown below displays revenue information across regions for a given
quarter. You can use PercentRankRelative to return the rank of revenue for each
region during a given quarter, relative to last quarter’s revenue.
The definition for the Percent Rank Revenue metric in this example is:
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PercentRankRelative<BreakBy={Quarter}>(Revenue, [Last
Quarter’s Revenue])
The Percent Rank Relative Revenue metric in the report shown above provides the rank of
each revenue value. The highest value in the Northeast region displays no data because last
quarter’s revenue is $797,627, which is greater than any revenue value for this quarter and
thus outside of the range of values. Since the default for the function is to not rank outlier
values, no data is displayed. If you modify the rankOutlier parameter to be
rankOutlier=TRUE, then data is returned for this ranking as shown in the report below.
This report also includes a metric that uses the PercentRank function, ranking the revenue
based on its own values. For information on PercentRank and an explanation of these
results, see PercentRank , page 214.
Rank
This function is used to display the ranking of values in a list relative to the other values. The
calculation can restart based on attributes identified in the parameter settings. This is an
OLAP function.
Unless the defaults are changed, the function ranks the values in ascending order by the
value of the metric, and the rank is an integer.
Syntax
Rank <ASC, ByValue, BreakBy, NullInclude> (Argument)
Where:
•
Argument is a fact or metric representing a list numbers.
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•
ASC is a TRUE/FALSE parameter that indicates the order of ranking (1 is the lowest or
highest value).
•
ByValue is a TRUE/FALSE parameter that indicates whether the ranking is done by
integer values (1, 2, 3, 4) or by percentage (10%, 50%, 75%, 100%).
•
BreakBy is the parameter that designates where the calculation should restart.
•
NullInclude is a parameter that determines how NULL values are included in the
rank calculation.
The NullInclude parameter only affects the rank of NULL values if the Rank function
is performed by the MicroStrategy Analytical Engine. The Rank function is performed by
the Analytical Engine for smart metrics, derived metrics, and other metric scenarios. To
determine whether the Rank function for a metric is performed by the Analytical Engine,
view the SQL statement for the report. If the metric is listed in the Analytical Engine
calculation steps, this verifies that the Rank function is performed by the Analytical
Engine.
If the Rank function is performed on a database, the NullInclude parameter is
ignored and NULL values are included in the rank calculation based on the database
standards.
For Rank functions that are performed by the Analytical Engine, you have the following
options for this parameter:
▫
1: If you define NullInclude=1, NULL values are given a rank value equal to the
number of other rank values, plus one. For example, the rank of the Profit metric in
the report below ranks four separate profit values.
There is one NULL value, which is given the rank of 4. The Rank (Profit) metric in
this example uses an ascending rank. If you define the metric with a descending
rank, the other rank values change but the rank value for any NULL values remains
the same. This is shown in the report below.
▫
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-1: If you define NullInclude=-1, NULL values are given the rank value of one.
For example, the reports shown below both define the Rank (Profit) metric with
NullInclude=-1. The report on the left uses an ascending rank, while the report
on the right uses a descending rank.
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As shown in the reports above, the NULL values for both reports are ranked with the
value of one.
▫
0 (default): If you define NullInclude=0, NULL values are included in the rank
calculation based on the NULL value handling defined using the Null checking for
Analytical Engine VLDB property. For information on VLDB properties, including
steps to access and modify them for various MicroStrategy objects, see the System
Administration Guide :
— If you define the Null checking for Analytical Engine property as True, NULL
values are treated as zero values in the rank calculation. For example, the
report shown below ranks the NULL values with a rank of two, because zero is
greater than -10 and less than 40.
— If you define the Null checking for Analytical Engine property as False, NULL
values are treated as NULL values, which means NULL values are also
displayed for the rank values. For example, the report shown below displays the
NULL values as NULL in the rank.
Example
This example report displays customer states ranked by revenue within their regions. There
are two metrics, one that ranks by value (default) and the other that ranks using a
percentage. In order to have the state with the highest revenue in each region ranked as 1 in
the Rank by Value metric, the Rank by Value ranking is descending. In order to have the
largest number ranked at 100% in the Rank by Percent metric, the ranking is ascending
(default).
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The report includes the attributes Customer Region, Customer State, the metric Total
Revenue (defined as Sum(Revenue)), and the two ranking metrics. The syntax for the
metrics is as follows:
Rank by Value:
Rank<ASC=False,ByValue=True, BreakBy={[Customer Region]}>
([Total Revenue])
Rank by Percent:
Rank<ASC=True,ByValue=False BreakBy={[Customer Region]}>
([Total Revenue])
The resulting report is displayed as follows.
String functions
String functions perform various actions that modify the characters returned for a string of
characters. While string functions can be used to create metrics, a more common use case
for these functions is in the creation of attribute forms. For example, these functions can aid
in the creation of attribute forms by combining multiple columns of information, capitalizing
the first letter of a column, removing or returning select characters of a column, and so on.
For information on creating attributes and attribute forms, see the Project Design Guide.
The MicroStrategy Analytical Engine does not calculate string functions; they are processed
by the database. For information on which string functions are supported for your specific
database, see Appendix A, MicroStrategy and Database Support for Functions, and search
in the section corresponding to your database. String functions for which your database does
not include SQL syntax support cannot be calculated in your environment.
This section of the document includes information and examples on the data returned by the
function.
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BeginsWith
Returns 1 if a text string begins with a specified text pattern. If the text string does not begin
with the pattern, the function returns 0.
For the definition and syntax of the Begins with comparison operator, see Begins with, page
243.
Syntax
BeginsWith(String, Pattern)
Where:
•
String is the string that is being searched. You can use facts, metrics, columns, or
string values.
•
Pattern is the string that is being searched for at the beginning of the values from
String. You can use facts, metrics, columns, or string values.
The text pattern comparison may or may not be case sensitive depending on the database
implementation.
Example
BeginsWith(Region@DESC, "North")
Returns 1 for Regions that start with North, such as Northeast and Northwest.
Char (convert ASCII code to a character)
The Char function converts a decimal ASCII code into its associated character. This
function supports returning any of the standard 128 characters assigned an ASCII code.
Syntax
Char(Argument)
Where:
•
Argument is a fact, metric, column, or constant value that provides an ASCII code in
ASCII decimal format. Any value provided outside the range of 0 to 127 causes the
Char function to return a single question mark (?) character. Any other invalid input,
such as character strings, causes the Char function to return null.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
Char(65)
This example returns A.
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Concat (concatenate)
The Concat function combines two or more input strings into one continuous string and
returns the result. For example, concatenating the two strings “Micro” and “Strategy” would
result in the single string “MicroStrategy”. Concat is often used to combine related values,
such as a first and last name.
Syntax
Concat(Argument1, Argument2,..., ArgumentN)
Where:
•
Argument1,..., ArgumentN are facts, metrics, columns, or string values.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
The Concat function can help to create attribute forms that are a combination of multiple
columns in database tables. For example, an LU_CUSTOMER table includes two columns:
CUST_FIRST_NAME and CUST_LAST_NAME. You can create a single attribute form that
combines these two columns as described below.
Concat([CUST_FIRST_NAME], [CUST_LAST_NAME])
The input from the first column is concatenated with the input from the second column, to
display information such as JohnDoe and JaneDoe for the attribute form.
For scenarios such as the one described above, you can use the ConcatBlank function
(see ConcatBlank (concatenate plus blank space), page 223 below) to concatenate the
strings and include a space between the two strings. This can result in attribute forms that are
easier to read. For example, rather than displaying JohnDoe, the attribute form would display
John Doe.
For an additional example of using the Concat function, see the example section for the
function InitCap (initial capitalization), page 224.
ConcatAgg (concatenate plus delimiter)
The ConcatAgg function takes all of the content from a single input and concatenates the
content as a single string. By default, each string of characters that is concatenated is
separated by a comma. You can define the delimiter.
Syntax
ConcatAgg<Delimiter, FactID, UseLookupForAttributes>
(Argument)
Where:
•
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Argument is facts, metrics, columns, or string values.
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•
Delimiter is a parameter that determines the characters used to separate each
concatenated value. By default, a comma is used.
•
FactID is a parameter that forces a calculation to take place on a fact table that
contains the selected fact.
•
UseLookupForAttributes is a TRUE/FALSE parameter that can be used when
performing an aggregation of an attribute. The Count function is most commonly used
to aggregate attributes. For information on this parameter, including an example of using
it with the Count function, see Count , page 95.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
The ConcatAggBlank function can help to create attribute forms that are a combination of
multiple rows in database tables. For example, an LU_CUSTOMER table includes a column
CUST_LAST_NAME. You can create a single attribute form that combines all the values
(rows) for CUST_LAST_NAME into a single attribute form:
ConcatAgg([CUST_LAST_NAME])
Each last name is concatenated, separating each last name with a comma by default.
ConcatBlank (concatenate plus blank space)
The ConcatBlank function concatenates two or more input strings into one continuous
string, inserting a blank space between each string. This function can be used to combine
related values that are logically separated by spaces. For example, concatenating the two
strings “Business” and “Intelligence” would result in the single string “Business Intelligence”.
Syntax
ConcatBlank (Argument1, Argument2,..., ArgumentN)
Where:
•
Argument1,..., ArgumentN are facts, metrics, columns, or string values.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
The ConcatBlank function can help to create attribute forms that are a combination of
multiple columns in database tables. For example, an LU_CUSTOMER table includes two
columns: CUST_FIRST_NAME and CUST_LAST_NAME. You can create a single attribute
form that combines these two columns as described below.
ConcatBlank([CUST_FIRST_NAME], [CUST_LAST_NAME])
The input from the first column is concatenated with the input from the second column, and
an additional space is included between the two inputs. This displays information such as
John Doe and Jane Doe for the attribute form.
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For an additional example of using the ConcatBlank function, see the example section for
the function InitCap (initial capitalization), page 224.
EndsWith
Returns 1 if a text string ends with a specified text pattern. If the text string does not end with
the pattern, the function returns 0.
For the definition and syntax of the Ends With comparison operator, see Ends with, page 244.
Syntax
EndsWith(String, Pattern)
Where:
•
String is the string that is being searched. You can use facts, metrics, columns, or
string values.
•
Pattern is the string that is being searched for at the end of the values from String.
You can use facts, metrics, columns, or string values.
Example
EndsWith(Employee@[Last Name], "son")
Returns 1 for Employees with a last name that ends with son, such as Wilson, Johnson, and
so on.
InitCap (initial capitalization)
The InitCap function returns a string in which the first letter of the input string is capitalized.
All other letters appear in lower case. This can help to fix capitalization errors in information
that is displayed for attribute forms, metrics, and other objects.
To capitalize the first letter of every word in a string, see TitleCap (title capitalization), page
234.
Syntax
InitCap(Argument)
Where:
•
Argument is a metric, column, or string value representing the text string.
For information on the syntax used in your specific database,see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
The InitCap function can be used to fix capitalization errors for information that is
displayed for attribute forms. For example, an LU_CUSTOMER table includes two columns:
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CUST_FIRST_NAME and CUST_LAST_NAME. To ensure that the first letter of the last name
is capitalized you create an attribute form with the following definition.
InitCap([CUST_LAST_NAME])
The input from the column is modified so that the first character is capitalized and all other
characters are lowercase. For example, if the column included information such as jackson,
sMITh, and Hughes, these would be displayed as Jackson, Smith, and Hughes respectively.
Be aware that this function could potentially return undesired results in certain scenarios.
Using the scenario described above, consider the name McCoy. Using the InitCap
function, this would be displayed as Mccoy.
Another way to use this function would be to create an attribute form that combined the first
letter of someone’s first name with the person’s full last name. The InitCap function could
be used to ensure that the first letter of the first name was capitalized. An attribute form of
this type would require the use of the functions InitCap, ConcatBlank, Concat, and
LeftStr. The definition of such an attribute form is shown below:
ConcatBlank(Concat(LeftStr(InitCap([CUST_FIRST_NAME]),
1), "."), [CUST_LAST_NAME])
An attribute form using a definition such as the one listed above would display names such
as J. Doe, M. Smith, and L. Martinez. The InitCap function ensures that the first letter of
the first name is capitalized.
LastPosition (last position of substring)
The LastPosition function returns the starting position of the last occurrence of a series
of characters in the input string. For example, using the LastPosition function to search
for the string “Strategy” within the string “MicroStrategy Inc. MicroStrategy” would return the
value of 25.
To find the first occurrence of a series of characters in a string, see Position (position of
substring), page 229.
Syntax
LastPosition (Argument1, Argument2)
Where:
•
Argument1 is the string in which to search for Argument2.
•
Argument2 is the substring to search for.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
LeftStr (left string selection)
The LeftStr function returns a substring taken as a specified number of characters from
the left of the input string. For example, if the specified length is five, LeftStr would return
the string “Micro” from the original string “MicroStrategy”. LeftStr is useful for
abbreviations or length reduction when the entire input string is not required.
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Syntax
LeftStr (Argument, Length)
Where:
•
Argument is a metric, fact, column, or string value representing the text string(s).
•
Length is an integer indicating the number of characters, starting from the far left
position of the string, to be returned.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
The LeftStr function can be used to create attribute forms that use abbreviations. This can
help reduce the length for attribute forms that may have long descriptions.
For example, an LU_CUSTOMER table includes two columns: CUST_FIRST_NAME and
CUST_LAST_NAME. You create attribute forms for the customer first and last names. You
also create an attribute form that displays only the first letter of the first name. This attribute
form can be used when the full first name does not need to be displayed. The definition of
such an attribute form is shown below:
LeftStr([CUST_FIRST_NAME], 1)
The integer value of 1 causes the LeftStr function to only display the first character at the
far left position for all inputs from the CUST_FIRST_NAME column.
For an additional example of using the LeftStr function, see the example section for the
function InitCap (initial capitalization), page 224.
Length (length of string)
The Length function returns the number of characters in an input string. For example, using
the Length function on the string “MicroStrategy” would return a value of 13. Length is
often used to manipulate strings with the help of other string functions.
Syntax
Length(Argument)
Where:
•
Argument is a metric, fact, column, or string representing the text string(s).
For information on the syntax used in your specific database, w33 Appendix A,
MicroStrategy and Database Support for Functions and see the section that corresponds to
your database.
Example
The Length function can be used in conjunction with other string functions to manipulate
strings in various ways. For example, rather than abbreviating information to a specific
number of characters, you can abbreviate each string to only display half of the available
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characters. The definition of an attribute form that displays only half of the characters
available for the first names of customers is shown below:
LeftStr([CUST_FIRST_NAME], Int(Length([CUST_FIRST_NAME])
/ 2))
For first names such as Alan, Frederick, and Jennifer, the function shown above would
display Al, Fred, and Jenn, respectively. This provides an abbreviated version of customers’
first names while providing additional characters to possibly distinguish each abbreviated
name.
In the function used in this example, the Int function is used to return only the integer part of
the calculation Length([CUST_FIRST_NAME]) / 2. Depending on your database’s
support for functions, you could also use functions such as Round, Ceiling, and Floor.
For information on these types of functions, see Mathematical functions, page 267.
Lower (lower case)
The Lower function returns a string in which all alphabetic characters in an input string are
displayed as lower case. For example, using the Lower function on the string
“MicroStrategy” would return the string “microstrategy”.
Lower may be used to help standardize the display of information and make the information
more readable.
Syntax
Lower(Argument)
Where:
•
Argument is a metric, column, or string value representing the text string(s).
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
The Lower function can be used to display information in all lower case for attribute forms.
For example, an LU_CUSTOMER table includes an ADDRESS column. You can create an
attribute form to display all characters for a customer’s address in lower case. The definition
of such an attribute form is shown below:
Lower([ADDRESS])
An attribute form using a definition such as the one listed above would take input addresses
such as 10 Main Street, 350 West Elm Avenue, and 4400 Spring Road and display them as
10 main street, 350 west elm avenue, and 4400 spring road, respectively.
LTrim (left trim)
The LTrim function returns a string in which any leading blank spaces on the left side of the
input string have been removed. For example, using the LTrim function on the string “
MicroStrategy” would return the string “MicroStrategy”.
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The LTrim function helps to remove blank spaces that may have been caused by errors in
data entry. Removing these spaces helps standardize the display of information and makes
the information more readable.
Syntax
LTrim(Argument)
Where:
•
Argument is a metric, fact, column, or string representing the text string(s).
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
An LU_CUSTOMER table includes an ADDRESS column. You can create an attribute form to
remove any leading blank spaces from the addresses. The definition of such an attribute
form is shown below:
LTrim([ADDRESS])
Any leading blank spaces included for addresses are removed from the display for the
attribute form.
To remove both leading and trailing blank spaces, use the Trim function (Trim, page 236).
Match
The Match function uses regular expressions to search a string for a pattern of characters
and returns any matches that are found.
Syntax
Match <Group=0,Instance=1>(Argument, Find)
Where:
228
•
Argument is a metric, fact, column, or string representing the text strings that are
searched for matches.
•
Find is a metric, fact, column, or string that provides a regular expression used to
searched the strings returned by Argument. The regular expressions supported by this
function conform to the standards of the International Components for Unicode. For
information about these regular expression standards and syntax, see
http://userguide.icu-project.org/strings/regexp.
•
Group is a parameter that determines which group within the regular expression is
returned. The groups in a regular expression are each set of characters enclosed by
parentheses () and are ordered left to right. By default, the Group parameter is defined
as 0 and the entire string that is matched is returned. If you define Group as 1, only the
first group from the left of the regular expression is returned. If you define Group to a
value greater than the number of groups in the regular expression, no results are
returned. For example if there is one group but Group is defined as 2, no results are
returned.
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•
Instance is a parameter that determines which instance of the matching results are
returned. By default, Instance is defined as 1 and the first match is returned. If you
define Instance to a value greater than the number of matching results, no results are
returned. For example if there are two matches but Instance is defined as 3, no
results are returned.
For information about the syntax used in your specific database, the Appendix A,
MicroStrategy and Database Support for Functions and see the section that corresponds to
your database.
Example
Consider the string of text "Telephone: 703-555-1234, Fax: 704-555-6789". You can search
for and return various parts of the telephone numbers in this string using regular
expressions. The regular expression (\d+)[-\b](\d+)[-\b](\d+) breaks up the
three sets of digits in a phone number into separate groups, which can be returned using the
Match function.
See http://userguide.icu-project.org/strings/regexp for information on the regular expression
standards and syntax.
For example:
•
Match("Telephone: 703-555-1234, Fax: 704-555-6789", "
(\d+)[-\b](\d+)[-\b](\d+)")
By default this returns the entire string that is the first match, which is 703-555-1234.
•
Match<Group=1>("Telephone: 703-555-1234, Fax: 704-5556789", "(\d+)[-\b](\d+)[-\b](\d+)")
This returns the first group (the first search criteria in parentheses) from the first
occurrence, which is 703.
•
Match<Instance=2, Group=3>("Telephone: 703-555-1234,
Fax: 704-555-6789", "(\d+)[-\b](\d+)[-\b](\d+)")
This returns the third group (the third search criteria in parentheses) from the second
occurrence, which is 6789.
Position (position of substring)
The Position function returns the starting position of the first occurrence of a series of
characters in the input string. For example, using the Position function to search for the
string “Strategy” within the string “MicroStrategy” would return the value of 6.
To find the last occurrence of a series of characters in a string, see LastPosition (last position
of substring), page 225.
In addition to locating the position of a substring, Position also can be used to test
whether the substring is present. If a given substring is not found, 0 will be returned.
Syntax
Position (Argument1, Argument2)
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Where:
•
Argument1 is the substring to search for.
•
Argument2 is the string in which to search for Argument1.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
The Position function can be used in conjunction with other functions to perform various
manipulations on strings. For an example of how this function works, consider an LU_
CUSTOMER table that includes an ADDRESS column. You use the Position function to find
the word Street in the addresses as defined below.
Position("Street", [ADDRESS])
For an address of 10 Main Street, the function listed above returns the value of 9. For
addresses that do not include the word Street, a value of 0 would be returned.
RepeatStr (repeat string)
The RepeatStr function returns a character or string of characters the specified number of
times.
Syntax
RepeatStr(String, Times)
Where:
•
String is a metric, fact, column, or string of characters representing the characters to
be repeated.
•
Times is a metric, fact, column, or constant value that specifies how many times to
repeat the string of characters. A common practice is to type a constant value.
Example
RepeatStr("Hello ", 3)
This would return the string of characters "Hello Hello Hello ".
Replace
The Replace function searches a string for a pattern of characters and replaces each
instance of those characters with the new characters you specify. The resulting string with all
instances replaced is returned.
Syntax
Replace(Argument, Find, Replace)
Where:
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•
Argument is a metric, fact, column, or string representing the text strings that are
searched.
•
Find is a metric, fact, column, or string representing the text strings that are searched
for within the strings returned by Argument.
•
Replace is a metric, fact, column, or string representing the text strings that are
used to replace any instances of the string from Find that are included in the strings
returned by Argument.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
RightStr (right string selection)
The RightStr function returns a substring taken as a specified number of characters from
the right of the input string. For example, the first eight characters from the right of the string
“MicroStrategy” would be “Strategy”.
RightStr can be used to create attribute forms that display only part of the information
available. This can be helpful when some information needs to be hidden for security
purposes, as in the credit card example below.
Syntax
RightStr (Argument, Length)
Where:
•
Argument is a metric, fact, column, or string representing the text string(s).
•
Length is an integer indicating the number of characters to be returned.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
An LU_CUSTOMER table includes a CREDIT_CARD_NUMBER column that contains credit
card numbers for online customers. You create attribute forms to display only the last four
digits of the customers’ credit card numbers. The definition of such an attribute form is shown
below:
RightStr([CREDIT_CARD_NUMBER], 4)
The integer value of 4 causes the RightStr function to only display the last four characters
from the CREDIT_CARD_NUMBER column.
You could also combine this functionality with the Concat function to display Xs or other
characters to represent the digits for the credit card that are not displayed.
Concat(“XXXX-XXXX-XXXX-”, RightStr([CREDIT_CARD_NUMBER],
4))
This definition would modify the display of a credit card number from 1111-2222-3333-4444
to be displayed as XXXX-XXXX-XXXX-4444.
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RTrim (right trim)
The RTrim function returns a string in which blank spaces on the right side of the input string
have been removed. For example, using the RTrim function on the string “MicroStrategy ”
would return the string “MicroStrategy”.
RTrim helps to remove trailing blank spaces, which may have been caused by errors in data
entry. Removing these spaces helps standardize the display of information and makes the
information more readable.
Syntax
RTrim(Argument)
Where:
•
Argument is a metric, fact, column, or string representing the text string(s).
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
An LU_CUSTOMER table includes an ADDRESS column. You can create an attribute form to
remove any trailing blank spaces from the addresses. The definition of such an attribute form
is shown below:
RTrim([ADDRESS])
Any trailing blank spaces included in the addresses are removed from the display for the
attribute form.
To remove both leading and trailing blank spaces, use the Trim function (Trim, page 236).
Split
The Split function searches a string, separates the contents into groups of characters
based on a delimiter, and returns the string of characters requested. For example, you can
search a string of characters that separates values with commas, and return the characters
after the first comma. Searching the string "red, yellow, green" and selecting to return the
second set of characters based on a comma as the delimiter returns "yellow ".
Syntax
Split<SeparatorRegex=False>(String, Delimiter, Index)
Where:
232
•
String is a metric, fact, column, or string representing the text strings that are
searched.
•
Delimiter is a metric, fact, column, or string that determines the characters used to
separate each concatenated value. A common practice is to provide a constant text
value for the delimiter. Typing “,” uses a comma as the delimiter.
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You can also choose to use regular expressions to define the delimiter characters. For
information on supporting regular expressions, see the SeparatorRegex parameter
described below.
If the delimiter character is not found in the string of characters, one of two resolutions
can occur:
▫
If you define Index as 1, then the entire string of characters is returned.
▫
If you define Index as any value other than 1, a NULL value is returned.
•
Index is a metric, fact, column, or constant value that determines which group of
characters are returned. A common practice is to provide a constant numerical value.
For example, using a value of 1 returns the first group of characters before the first
delimiter characters are found in the string.
•
SeparatatorRegex is a parameter that determines if a regular expression is used to
provide the delimiter characters. The regular expressions supported by this function
conform to the standards of the Intern a ion al Components for Unicode. For information
about these regular expression standards and syntax, see http://userguide.icuproject.org/strings/regexp.
Example
•
Split<SeparatorRegex=True>("red yellow green", "\s", 2)
This returns the string of characters "yellow". By using the regular expression \s, any
white space character is considered a delimiter. This splits the string into the strings
"red", "yellow", and "green".
•
Split("red; yellow; green", ";" 1)
This returns the string of characters "red".
•
Split("red; yellow; green", ";" 3)
This returns the string of characters " green".
•
Split("red; yellow; green", ";" 4)
This returns a NULL value, since the string is only split into three separate strings.
SubStr (substring selection)
The SubStr function returns a substring taken as a specified sequence of characters from
the input string. SubStr is useful for isolating a specific section of a string that contains
relevant information.
Syntax
SubStr(Argument, Position, Length)
Where:
•
Argument is a metric, fact, column, or string representing the text string(s).
•
Position is an integer indicating the starting position inside the string.
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•
Length is an integer indicating the number of characters to be returned.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
An LU_ITEM table includes an ITEM_ID column. This column stores a ten-digit number,
which has been created to define various facts about the item. This includes two digits which
identify the location in which the item is sold. These two digits always start at the fourth digit
from the left side of the ten-digit item ID number. To retrieve these digits and determine the
location of sale for an item, you can create an attribute form to return these two digits. The
definition of such an attribute form is shown below:
SubStr(ITEM_ID, 4, 2)
The substring retrieved starts at the fourth digit and retrieves two characters. For an item ID
of 2334560897, this function returns 45.
TitleCap (title capitalization)
The TitleCap function returns a string in which the first letter of every word in the input
string is capitalized. All other letters that are not the first letter in a word appear in lower case.
This can help to fix capitalization errors in information that is displayed for attribute forms,
metrics, and other objects.
A word in a string is any string of alphabetic characters that are separated by a nonalphabetic character such as a space, comma, or number.
To capitalize only the first letter of the first word in a string, see InitCap (initial capitalization),
page 224.
Syntax
TitleCap(Argument)
Where:
•
Argument is a metric, column, or string value representing the text string.
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
TitleCap(“john smith”)
This example would return John Smith.
TitleCap(“john SMITH”)
This example would also return John Smith.
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ToNumber (convert string to a number)
The ToNumber function converts a string of characters to its applicable numeric value.
Converting data that has been processed as a string to a numeric value allows you to
perform various calculations and analysis that can be done only with numeric values.
Syntax
ToNumber(Argument)
Where:
•
Argument is a fact, metric, column, or constant value that provides the strings that are
converted to numeric values. The function can recognize E notation and return the
appropriate number.
Example
ToNumber("1001")
This example returns the numeric value 1001.
ToNumber("1.48e12")
This example returns the numeric value 1,480,000,000,000.
ToString (convert number, date, or timestamp to a
string)
The ToString function converts a number, date, or timestamp to a string of characters.
Converting data that has been processed as a numeric, date, or timestamp value to a string
allows you to view and display the data in different ways.
Syntax
ToString<Pattern=null>(Argument)
Where:
•
Argument is a fact, metric, column, or constant value that provides the values that are
converted to a string of characters.
•
Pattern is a parameter that determines the formatting for the resulting string of
characters. When providing a pattern, enclose the pattern in double quotes (""). For
example:
ToString<pattern="0,000.00">(Revenue)
To specify a pattern, you can use the custom numeric, date, and time formatting symbols
that are described in the Advanced Reporting Guide.
Example
ToString<pattern="0,000.00">(1001)
This example returns the string "1,001.00".
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Trim
The Trim function returns a string in which blank spaces on either side of the input string
have been removed. For example, using the Trim function on the string “ MicroStrategy”
would return the string “MicroStrategy”.
The Trim function helps to remove leading and trailing blank spaces, which may have been
caused by errors in data entry. Removing these spaces helps standardize the display of
information and makes the information more readable.
Syntax
Trim(Argument)
Where:
•
Argument is a metric, fact, column, or string representing the text string(s).
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
An LU_CUSTOMER table includes an ADDRESS column. You can create an attribute form to
remove any leading and trailing blank spaces from the addresses. The definition of such an
attribute form is shown below:
Trim([ADDRESS])
Any leading or trailing blank spaces included in the addresses are removed from the display
for the attribute form.
Upper (upper case)
The Upper function returns a string in which all alphabetic characters in an input string are
displayed as upper case. For example, using the Upper function on the string
“MicroStrategy” would return the string “MICROSTRATEGY”. The Upper function can be
used to display information in all upper case for attribute forms.
Syntax
Upper(Argument)
Where:
•
Argument is a metric, column, or string representing the text string(s).
For information on the syntax used in your specific database, see Appendix A, MicroStrategy
and Database Support for Functions and see the section that corresponds to your database.
Example
An LU_CUSTOMER table includes an ADDRESS column. You can create an attribute form to
display all characters for a customer’s address in upper case. The definition of such an
attribute form is shown below:
Upper([ADDRESS])
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An attribute form using a definition such as the one listed above would take input addresses
such as 10 Main Street, 350 West Elm Avenue, and 4400 Spring Road and display them as
10 MAIN STREET, 350 WEST ELM AVENUE, and 4400 SPRING ROAD, respectively.
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OPERATORS
The operators described in this chapter are building block functions. They provide the means
to perform simple mathematical operations, compare values, search strings, and evaluate
logical conditions.
Operators may not be available for direct selection when creating MicroStrategy objects such
as metrics. These operators are employed to support the logic of metrics, filters, thresholds,
and so on.
The following categories of operators are covered in this chapter:
•
Arithmetic operators, page 238
•
Comparison operators, page 240
•
Comparison for rank operators, page 248
•
Logical operators, page 252
Arithmetic operators
The arithmetic operators are basic mathematical functions, such as Minus, Times, Divide,
Plus, and Unary Minus, which are among the most commonly used operators. A brief
description of each operator follows.
Minus (-)
Returns the difference between two values.
Syntax
Arg1 - Arg2
Where:
Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers, big decimals,
date/time, or functions that return numbers or big decimals. Both Arg1 and Arg2 must be of
the same data type, with the exception if Arg1 is a Date data type. In this case, Arg2 must
be a number.
Example
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A metric is defined as:
Revenue - Freight
This metric returns the difference between the revenue and freight charges.
Times (*)
Returns the product of two values.
Syntax
Arg1 * Arg2
Where:
Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers or big decimals,
or functions that return numbers or big decimals.
Example
A metric is defined as:
([Unit Profit] * [Units Sold])
This metric returns the product of the unit profit and the units sold values.
Divide (/)
Returns the quotient when one value is divided by another.
Syntax
Arg1 / Arg2
Where:
Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers or big decimals,
or functions that return numbers or big decimals. In addition, Arg2 must not be zero.
Example
A metric is defined as:
(Profit / [Units Sold])
This metric returns the quotient of the profit and number of units sold.
Plus (+)
Returns the sum of two values.
Syntax
Arg1 + Arg2
Where:
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Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers or big decimals,
or functions that return numbers and big decimals. In addition, if either of the two arguments
is date/time, the other argument must be a number.
Example
A metric is defined as:
Cost + Freight
This metric returns the sum of the cost and the freight charges.
Unary minus (U-)
Returns the absolute value of a negative value or the negative value of a positive value.
Syntax
U-(Arg)
Where:
Arg is an attribute, fact, metric representing a list of numbers, or a function that returns
numbers and big decimals.
Example
A metric is defined as:
U-(Profit)
This metric changes the sign of the profit value. If the profit value is negative, it returns a
positive value. If the profit value is positive, it returns a negative value.
Comparison operators
These operators are used to compare values, which can be numbers, text strings, date and
time, expressions, or operators that return any values of the mentioned data types. A brief
description of each operator follows. The comparison patterns of text strings may or may not
be case-sensitive depending on the database implementation.
Less than (<)
Returns TRUE if the first value is less than the second value.
Syntax
Arg1 < Arg2
Where:
Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers, text strings,
date/time, or functions that return literal values. Arg1 and Arg2 must be of the same data
type.
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Example
A condition is defined as:
Profit < Cost
This condition returns TRUE only if the profit is less than the cost.
Less than or equal (<=)
Returns TRUE if the first value is less than or equal to the second value.
Syntax
Arg1 <= Arg2
Where:
Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers, text strings,
date/time, or functions that return literal values. Arg1 and Arg2 must be of the same data
type.
Example
A condition is defined as:
Profit <= Cost
This condition returns TRUE only if the profit is less than or equal to the cost.
Not equal (<>)
Returns TRUE if two given values are not equal to each other.
Syntax
Arg1 <> Arg2
Where:
Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers, text strings,
date/time, or functions that return literal values. Arg1 and Arg2 must be of the same data
type.
Example
A condition is defined as:
Profit <> Cost
This condition returns TRUE only if the profit is not equal to the cost.
Equal (=)
Returns TRUE if the two given values are equal to each other.
Syntax
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Arg1 = Arg2
Where:
Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers, text strings,
date/time, or functions that return literal values. Arg1 and Arg2 must be of the same data
type.
Example
A condition is defined as:
Profit = Cost
This condition returns TRUE if the profit is equal to the cost.
Greater (>)
Returns TRUE if the first value is greater than the second value.
Syntax
Arg1 > Arg2
Where:
Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers, text strings,
date/time, or functions that return literal values. Arg1 and Arg2 must be of the same data
type.
Example
A condition is defined as:
Profit > Cost
This condition returns TRUE only if the profit is greater than the cost.
Greater than or equal (>=)
Returns TRUE if the first value is greater than or equal to the second value.
Syntax
Arg1 >= Arg2
Where:
Arg1 and Arg2 are attributes, facts, metrics representing a list of numbers, text strings,
date/time, or functions that return literal values. Arg1 and Arg2 must be of the same data
type.
Example
A condition is defined as:
Profit >= Cost
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This condition returns TRUE only if the profit is greater than or equal to the cost.
Begins with
Returns TRUE if a text string begins with a specified text pattern.
For the definition and syntax of the BeginsWith string function, which can be used to create
attribute forms, see BeginsWith, page 221.
Syntax
Arg1 Begins With Arg2
Where:
Arg1 and Arg2 are text strings.
The text pattern comparison may or may not be case sensitive depending on the database
implementation.
Example
Region@DESC Begins with "North"
Returns TRUE for Regions that start with North, such as Northeast and Northwest.
Between
Returns TRUE if the specified value is between the inclusive range of two boundaries.
Between can be used to test if a value is within a valid data range.
Syntax
Value Between Boundary1 and Boundary2
Where:
•
Value, Boundary1, and Boundary2 can be numbers, date/time, text, or functions
that return the mentioned data types. Value, Boundary1 and Boundary2 must be of
the same data type.
Example
•
M1 Between 1 and 1000
•
region@DESC Between "A" and "Z"
•
date@DESC Between '1999-2-12' and '2005-2-23'
Contains
Returns TRUE if a text string contains a specified text pattern.
Syntax
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Arg1 Contains Arg2
Where:
Arg1 and Arg2 must be of data type Text.
Example
Employee@[Last Name] Contains "Smith"
Returns TRUE for Employees with a last name such as “Smith”, “Smithson”, and so on.
Ends with
Returns TRUE if a text string ends with a specified text pattern.
For the definition and syntax of the EndsWith string function, which can be used to create
attribute forms, see EndsWith, page 224.
Syntax
Arg1 Ends With Arg2
Where:
Arg1 and Arg2 must be of data type Text.
Example
Employee@[Last Name] Ends with "son"
Returns TRUE for Employees with a last name that ends with son, such as Wilson, Johnson,
and so on.
In
Returns TRUE if a value is contained in a specified list of values.
Syntax
Arg1 In Arg2
Where:
Arg2 is a list of the literals with the same data type as Arg1. It can be of the data type
numbers, big decimals, text, date/time, or functions that return these data types.
Examples
•
Employee@[Last Name] in {"Smith", "Cooper", "Michael"}
•
Year in {2000, 2001, 2002}
Like
Returns TRUE if a text string matches a specified text pattern; otherwise, returns FALSE.
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Depending on whether you use wildcards and how they are used in the text in the pattern,
Like can be used in place of Begins With, Ends With, Contains, or =. This operator
is used to search for related strings.
Syntax
Arg1 Like Arg2
Where:
Arg1 and Arg2 must be of data type Text.
Usage notes
Using wildcards with the Like operator allows you to search for more than just a static set of
text. For example, rather than searching for the exact text pattern South, you can use
wildcards to search for any text pattern that includes South, such as Mid Southern,
SouthEast, and South.
The Like operator can be processed by the MicroStrategy Analytical Engine, or it can be
passed to the database to be processed using the database’s own comparison support. You
can determine if the Like operator was processed by the MicroStrategy Analytical Engine
or passed to the database by viewing the SQL view of a report. If the Like operator is
included in the SQL of the report, the database performed the comparison. By contrast, if the
Like operator is not included in the SQL of the report, or the Like operator is part of
retrieving results from an Intelligent Cube (see the In-memory Analytics Guide for reporting
on Intelligent Cubes), then the MicroStrategy Analytical Engine processed the Like
operator. Wildcard support depends on how the Like operator is processed:
•
•
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If the MicroStrategy Analytical Engine processes the Like operator, the following
wildcard characters are supported:
▫
The % character can be used to represent any number of characters. For example,
using the comparison Like 'Sout%' returns TRUE for South, SouthEast, and
Southern.
▫
The _ character can be used to represent a single character. For example, using the
comparison Like 'Sout_' returns TRUE for South but returns FALSE for
SouthEast and Southern.
▫
The / character can be used as an escape character for the %, _, or / characters,
which means it can be used prior to these wildcard characters to search for the
character rather than to use it as a wildcard. To search for the characters %, _, or /,
you must include a single / character before the character you are searching for.
For example, to search for the exact text User_ID, you would need to use the
comparison Like 'User/_ID'.
▫
The * character can be used to represent any number of characters. For example,
using the comparison Like ‘Sout*’ returns TRUE for South, SouthEast, and
Southern. The * character also acts as its own escape character, which means it
can be used prior to another * to search for asterisks in text patterns. For example,
to search for the exact text User*, you would need to use the comparison Like
'User**'.
If the database processes the Like operator, the database determines how wildcards
are supported. In general, many databases support the same wildcard characters and
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escape characters that are listed above as supported by the MicroStrategy Analytical
Engine. However, some databases do not support / as an escape character and instead
use an alternative such as enclosing the wildcard character in brackets. For example, a
database may support using [%] to search for the % character. Therefore, if the Like
operator is being processed by the database, see your third-party database
documentation to verify which wildcards can be used as part of a comparison.
Example
•
Region@DESC like 'South'
Returns TRUE if region is “South”.
•
Region@DESC like 'South%'
Returns TRUE if region is “South”, “SouthEast”, and so on.
•
Region@DESC like '%South%'
Returns TRUE if region is “Mid Southern”, “SouthEast”, “South”, and so on.
•
Region@DESC like 'D_g'
Returns TRUE if region is “Dog”, “Dig”, “Dug”, and so on.
•
Region@DESC like 'D*g'
Returns TRUE if region is “Dog”, “Drag”, “Drug”, and so on.
Not begins with
Returns TRUE if a text string does not begin with a specified text pattern.
Syntax
Arg1 Not Begins With Arg2
Where:
Arg1 and Arg2 must be of data type Text.
Example
Region@DESC Not Begins with "South"
Returns TRUE if region is “Northeast”, “North”, and so on.
Not between
Returns TRUE if a specified value does not lie in between two given boundaries.
Syntax
Value Not Between Boundary1 and Boundary2
Where:
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Value, Boundary1, and Boundary2 must be of the same data type. They can be of any
data type that MicroStrategy supports.
Example
Year not between 2000 and 2005
Returns TRUE if Year = 1999
Not contains
Returns TRUE if a text string does not contain the specified text pattern.
Syntax
Arg1 Not Contains Arg2
Where:
Arg1 and Arg2 must be of Text data type.
Example
Region@DESC Not Contains "South"
Returns TRUE if region is “North”, “West”, and so on.
Not ends with
Returns TRUE if a text string does not end with the specified text pattern.
Syntax
Arg1 Not Ends With Arg2
Where:
Arg1 and Arg2 must be of Text data type.
Example
Region@DESC Not Ends With "East"
Returns TRUE if region is “Northwest”, “West”, and so on.
Not in
Returns TRUE if a given value is not in the specified list of values.
Syntax
Arg1 not in Arg2
Where:
Arg2 must be a list with one or more elements. Arg1 must be of the same data type as the
elements in Arg2. Arg1 and Arg2 can be of any data type that MicroStrategy supports.
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Example
Year not in (2002, 2003)
Returns TRUE if year is 2000, 2001, and so on.
Not like
Returns TRUE if a text string does not match the specified text pattern; otherwise, returns
FALSE.
Syntax
Arg1 Not Like Arg2
Where:
Arg1 and Arg2 must be of data type Text.
Example
Region@DESC not like “South%”
Returns TRUE if region is “Northeast”, “North”, “Mid South”, and so on.
Comparison for rank operators
These operators compare rank values. The Comparison for rank operators are:
•
Less than or equal enhanced (*<=), page 248
•
Not equal enhanced (*<>), page 249
•
Equal enhanced (*=), page 249
•
Greater than or equal enhanced (*>=), page 250
•
Between enhanced (*Between), page 250
•
Not between enhanced (Not *Between), page 251
Less than or equal enhanced (*<=)
Returns TRUE for values in a list which are less than or equal to a specified condition. The
list values are generated from the Rank function.
Syntax
ValueList *<= Condition
Where:
•
ValueList is the list of the rank values of a metric.
•
Condition is the value to be compared.
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Example
Build a Set Qualification filter with
•
Metric: M1
•
Function: Rank
•
Operator: *<=
•
Value: 2
Returns TRUE for the values of M1 whose rank values are less than or equal to 2.
Not equal enhanced (*<>)
Returns TRUE for values in a list which are not equal to a specified condition. The list values
are generated from the Rank function.
Syntax
ValueList *<> Condition
Where:
•
ValueList is the list of the rank values of a metric.
•
Condition is the value to be compared.
Example
Build a Set Qualification filter with
•
Metric: M1
•
Function: Rank
•
Operator: *<>
•
Value: 2
Returns TRUE for the values of M1 whose rank values are not equal to 2.
Equal enhanced (*=)
Returns TRUE for values in a list which are equal to a specified condition. The list values are
generated from the Rank function.
Syntax
ValueList *= Condition
Where:
•
ValueList is the list of the rank values of a metric.
•
Condition is the value to be compared.
Example
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Build a Set Qualification filter with
•
Metric: M1
•
Function: Rank
•
Operator: *=
•
Value: 2
Returns TRUE for the values of M1 whose rank values are equal to 2.
Greater than or equal enhanced (*>=)
Returns TRUE for values in a list which are greater than or equal to a specified condition. The
list values are generated from the Rank function.
Syntax
ValueList *>= Condition
Where:
•
ValueList is the list of the rank values of a metric.
•
Condition is the value to be compared.
Example
Build a Set Qualification filter with
•
Metric: M1
•
Function: Rank
•
Operator: *>=
•
Value: 2
Return TRUE for the values of M1 whose rank values are greater than or equal to 2.
Between enhanced (*Between)
Returns TRUE for the values in a list of values that are in a specified range. The values are
generated from the Rank function.
Syntax
ValueList *Between Condition1 and Condition2
Where:
•
ValueList is the list of the rank values of a metric.
•
Condition1 and Condition2 set the range of values to be compared.
Usage notes
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*Between is inclusive which means any value greater than or equal to Condition1 and less
than or equal to Condition2 will return TRUE.
Example
Build a Set Qualification filter with
•
Metric: M1
•
Function: Rank
•
Operator: *between
•
Value: 2 and
•
Value: 8
Returns TRUE for the values of M1 whose rank values are between 2 and 8.
Not between enhanced (Not *Between)
Returns TRUE if a list of rank values is not within a specified range. The values are generated
from the Rank function.
Syntax
ValueList Not *Between Condition1 and Condition2
Where:
•
ValueList is the list of the rank values of a metric.
•
Condition1 and Condition2 set the range of values to be compared.
Usage notes
Values equal to Condition1 and Condition2 are not satisfied with the condition.
Example
Build a Set Qualification filter with
•
Metric: M1
•
Function: Rank
•
Operator: *Not between
•
Value: 2 and
•
Value: 8
Return TRUE for the values of M1 whose rank values are less than 2 or greater than 8.
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Logical operators
The logical operators allow certain conditions to be applied to two sets of filter expressions
simultaneously.
And
Returns TRUE if both the specified conditions are TRUE; otherwise, returns FALSE.
Syntax
Arg1 And Arg2
Where:
Arg1 and Arg2 are conditional expressions. The condition can contain metrics, comparison
and logical operations, functions, and constants. The condition must be evaluated as TRUE
or FALSE.
Example
A condition is defined as:
(Cost < 1000) And (Freight < 500)
This condition will return TRUE only if both, the Cost is less than 1000, and the Freight is less
than 500.
IF
Returns a value if the specified condition is TRUE; otherwise, a default value is returned. This
is a single value function.
Syntax
IF (Condition, TrueBranch, FalseBranch)
Where:
•
Condition is the conditional expression. The condition can contain metrics,
comparison and logical operations, and constants. The condition must be evaluated to
be TRUE or FALSE.
•
TrueBranch is a constant or metric value to return if the condition is TRUE.
•
FalseBranch is a constant or metric value to return if the condition is FALSE.
Usage notes
FalseBranch must be provided; otherwise the return value is undefined.
Example
A metric is defined as:
IF ((Total Revenue < 300000), 0, 1)
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This metric returns 0 if the Total Revenue is less than 300,000; otherwise, it returns 1.
Not
Returns TRUE if the specified condition is FALSE, and FALSE if the condition is TRUE.
Syntax
Not(Arg1)
Where:
Arg1 is the conditional expression. The condition can contain metrics, comparison and
logical operations, and constants. The condition must be evaluated as TRUE or FALSE.
Example
A condition is defined as:
Not ((Profit <= 0))
This condition returns TRUE only if the profit is greater than zero.
Or
Returns FALSE if both the specified conditions are FALSE; else returns TRUE.
Syntax
Arg1 Or Arg2
Where:
Arg1 and Arg2 are conditional expressions. The condition can contain metrics, comparison
and logical operations, and constants. The condition must be evaluated as TRUE or FALSE.
Example
A condition is defined as:
(Cost <= 1000) Or (Freight <= 500)
This condition returns FALSE only if the Cost is greater than 1000, and the Freight is greater
than 500.
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PLUG-IN PACKAGE
FUNCTIONS
The functions in this chapter represent the more advanced functions available in
MicroStrategy, including data mining, financial, mathematical, and statistical functions. For
more information on the plug-in functions and how to install the Function Plug-in Wizard, see
Using custom plug-in functions, page 67.
Each section briefly describes the category of function and then lists each function along with
information designed to provide data necessary for understanding and implementing an
individual function. The information provided for each function includes:
•
An explanation of the data returned by the function
•
The syntax of the function including function name, the available parameters, the
parameter setting defaults, and the types of data possible for use with the function
•
The mathematical expression illustrating exactly how the calculation is defined in
MicroStrategy (if applicable)
•
Usage notes describing any error conditions, invalid data types, or key items to know
before using the function (if applicable)
•
An example of the function in use; this can be either a report example or a simple text
description of the data returned based on the specified input
For a list of databases and the functions they support, see Appendix A, MicroStrategy and
Database Support for Functions.
The following categories of functions are covered:
•
Data mining functions, page 255
•
Financial functions, page 257
•
Mathematical functions, page 267
•
Statistical functions, page 270
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Data mining functions
Data mining generally refers to examining a large amount of data to extract valuable
information. The data mining process uses predictive models based on existing and historical
data to project potential outcome for business activities and transactions. MicroStrategy
Data Mining Services facilitates the development and deployment of these predictive
models.
Data mining is covered in the Data Mining Services chapter of the Advanced Reporting
Guide. The Data Mining Services chapter introduces MicroStrategy Data Mining Services,
which includes these features:
•
Using MicroStrategy and the Training Metric Wizard to create multi-variable regression
predictive models
•
Support for importing third-party predictive models using the PMML industry standard
•
A Predictive Model Viewer that visualizes the predictive model
•
A set of sample predictive metrics and reports incorporated into Customer Analysis
Module (CAM)
In addition, the Data Mining Services chapter of the Advanced Reporting Guide describes
the process of how to create and use predictive models with MicroStrategy and provides a
business case for illustration.
The data mining functions that are available within MicroStrategy are employed when using
standard MicroStrategy Data Mining Services interfaces and techniques, which includes the
Training Metric Wizard and importing third-party predictive models. To ensure proper
functionality, it is recommended to use these MicroStrategy data mining functions within the
Data Mining Services interfaces and techniques, rather than manually defining the values
and parameters for these functions.
Functions for R integration
MicroStrategy supports the integration and deployment of analytics from the R statistical
environment to Analytics Desktop. Customers interested in deploying analytics from the R
programming language into MicroStrategy can do so using the R Integration Pack, available
on the MicroStrategy GitHub site.
The third-party R environment is freely available, as a separate download, from CRAN-R.
Once you have downloaded and configured the R Integration Pack, there are several
functions available in MicroStrategy Desktop that allow you to deploy your R analytics.
These functions include:
For the functions listed below, scalars are variables with a single value while vectors are
variables with one or more values.
255
•
RScript: Supports R scripts that use vectors for the inputs and output. Sorting is also
supported.
•
RScriptAgg: Supports R scripts that use vectors for the inputs and a scalar output.
Sorting is also supported.
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•
RScriptAggU: Supports R scripts that use vectors for the inputs and a scalar output.
This is a version of the RScriptAgg function that does not include sorting.
•
RScriptSimple: Supports R scripts that use scalar for the inputs and output. Sorting is
not supported.
•
RScriptU: Supports R scripts that use vectors for the inputs and output. This is a
version of the RScript function that does not include sorting.
The easiest way to create a metric expression that utilizes these functions
A metric expression that can then be used in a derived metric to include the statistical
analysis of an R script on a dashboard in Desktop. The R Integration Pack User Guide
provides steps to use the deployR utility to create metric expressions for R scripts.
When defining these functions in MicroStrategy Desktop, you have the following options:
•
Value1, ..., ValueN: The MicroStrategy metrics that act as inputs for the R script.
•
BooleanParam1, ..., BooleanParam9: A set of boolean parameters that allow
you to pass boolean values into R scalar values that do not change from execution to
execution. If you use any boolean parameters in your R script, you can define the default
value for the parameter when using the function in Analytics Desktop.
•
NumericParam1, ..., NumericParam9: A set of numeric parameters that allow
you to pass numeric values into R scalar values that do not change from execution to
execution. If you use any numeric parameters in your R script, you can define the default
value for the parameter when using the function in Analytics Desktop.
•
StringParam1, ..., StringParam9: A set of string parameters that allow you
to pass numeric values into R scalar values that do not change from execution to
execution. If you use any string parameters in your R script, you can define the default
value for the parameter when using the function in Analytics Desktop.
•
_WorkingDir: The R scripts working directory, which is used to store various
supporting files.
•
_OutputVar: The R variable that is used as the output for the metric. If there is more
than one output, the first output is considered the default output unless otherwise
specified here.
•
_NullsAllowed: Controls whether records containing null values are to be passed in
as inputs to your analytic:
•
▫
True (default): Null values are included in the analysis.
▫
False: All records containing null values are eliminated from the analysis.
_CheckInputCount: Controls whether MicroStrategy ensures that the number of
inputs to the metric exactly matches exactly the number of inputs specified in the
function’s signature:
▫
True (default): If the number of inputs is different, a warning message is returned
when using the R script in MicroStrategy.
▫
False: If the number of inputs is different, the script execution will attempt to
proceed.
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•
_RScriptFile: The directory where the R script file is stored.
•
_InputNames: The MicroStrategy metric names that are used as inputs to the R script.
For the R environment to be able to use the MicroStrategy metric names in objects and
graphics that R generates, the names associated with the inputs from MicroStrategy
need to be passed to R.
•
_Params: For internal use only.
•
BreakBy: The logical level where the calculation of values for an expression restarts.
You can select the break by attribute from the drop-down list.
•
SortBy: Controls the sorting of records before the data is passed to R. To specify a
particular sorting criterion, you can select the sort by value from the drop-down list. Use
the button to the right of the drop-down list to define whether the sort is ascending or
descending.
Financial functions
The financial functions plug-in package in MicroStrategy provides access to many standard
financial calculations. All finance-related calculations are performed by the MicroStrategy
Analytical Engine, regardless of the database environment.
Accrint (accrued interest)
(missing or bad snippet)
Expression
Where:
•
Ai is the number of accrued days for the ith quasi-coupon period within an odd period
•
NC is the number of quasi-coupon periods that fit an odd period (if this period contains a
fraction, that fraction is rounded up to the nearest integer)
•
NLi is the normal length, in days, of the ith quasi-coupon period within an odd period
Usage notes
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•
If Issue, FirstInterest, Settlement, or Frequency is not an integer, it is truncated
•
The engine returns an empty cell if:
▫
Issue, FirstInterest, or Settlement is not a valid date
▫
Par ≠ 1000
▫
Issue ≥ Settlement
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▫
•
Frequency is a value other than 1, 2, or 4
The Issue date, the FirstInterest date and the Settlement date should be included within
single quotations in the expression for the expression to be considered as a valid
expression
Example
This example displays the expression built using Accrint for a treasury bond with the
following terms:
•
March 22, 2003, issue date
•
June 20, 2003, first interest date
•
September 16, 2003, settlement date
•
10.0 percent coupon
•
$1,000 par value
•
Frequency is semiannual
•
Basis is 30/360
The accrued interest is defined as:
Accrint <Par=1000, Basis=0>
(‘3/22/2003’,’9/16/2003’,’6/20/2003’,0.1,2) {~+}
Accrintm (accrued interest at maturity)
(missing or bad snippet)
Expression
Where:
•
A is the accrued time (for interest-at-maturity items, the value used is the number of days
from issue to maturity)
•
D is the annual-yield basis
Usage notes
•
If Issue or Maturity is not an integer, it is truncated.
•
The engine returns an empty cell if
•
▫
Issue or Maturity is not a valid date
▫
Rate ≤ 0
▫
Par ≠ 1000
The Issue date and the Maturity date should be included within single quotations in the
expression for the expression to be considered as a valid expression.
Example
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This example displays the expression built using the Accrintm function for a note with the
following terms:
•
March 22, 2001, issue date
•
June 20, 2003, maturity date
•
10.0 percent coupon
•
$1,000 par value
•
Frequency is semiannual
•
Basis is Actual/365
The accrued interest at maturity is defined as:
Accrintm <Par=1000, Basis=3>
(‘3/22/2001’,’6/20/2001’,0.1){~+}
Coupdaybs (coupon period, beginning to settlement)
(missing or bad snippet)
Usage notes
•
If an argument is not an integer, it is truncated.
•
The Settlement date and the Maturity date should be included within single quotations in
the expression for the expression to be considered as a valid expression.
Coupdays (coupon period, number of days with
settlement)
(missing or bad snippet)
Usage notes
•
The number of days between milestones is computed depending on the chosen day
basis
•
Coupon functions are defined against the maturity day, depending on frequency
•
The Settlement date and the Maturity date should be included within single quotations in
the expression for the expression to be considered as a valid expression
Coupdaysnc (coupon period, settlement to next
coupon)
(missing or bad snippet)
Usage notes
•
If an argument is not an integer, it is truncated.
•
The engine returns an empty cell if
▫
259
Settlement or Maturity is not a valid date.
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•
▫
Frequency is a number other than 1, 2, or 4.
▫
Settlement ≥ Maturity.
The Settlement date and the Maturity date should be included within single quotations in
the expression for the expression to be considered as a valid expression.
Coupncd (next date after settlement)
(missing or bad snippet)
Usage notes
•
If an argument is not an integer, it is truncated.
•
The engine returns an empty cell if:
•
▫
Settlement or Maturity is not a valid date.
▫
Frequency is a number other than 1, 2, or 4.
▫
Settlement ≥ Maturity.
The Settlement date and the Maturity date should be included within single quotations in
the expression for the expression to be considered as a valid expression.
Coupnum (coupon, number payable between
settlement and maturity)
(missing or bad snippet)
Usage notes
•
The number of days between milestones is computed depending on the chosen day
basis.
•
Coupon functions are defined against the maturity day, depending on frequency.
•
The Settlement date and the Maturity date should be included within single quotations in
the expression for the expression to be considered as a valid expression.
Couppcd (coupon date, previous)
(missing or bad snippet)
Usage notes
•
If an argument is not an integer, it is truncated.
•
The engine returns an empty cell if:
▫
Settlement or Maturity is not a valid date.
▫
Frequency has a value other than 1, 2, or 4.
▫
Settlement ≥ Maturity.
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•
The Settlement date and the Maturity date should be included within single quotations in
the expression for the expression to be considered as a valid expression.
Cumipmt (cumulative interest paid)
(missing or bad snippet)
Cumprinc (cumulative principal paid)
(missing or bad snippet)
Db (fixed-declining balance (asset depreciation))
(missing or bad snippet)
Ddb (double-declining balance (asset depreciation))
(missing or bad snippet)
Disc (discount rate for a security)
(missing or bad snippet)
Expression
Where:
•
B is the number of days in a year (see Basis)
•
DSM is the number of days between settlement and maturity
Usage notes
•
If Settlement or Maturity is not an integer, it is truncated.
•
The engine returns an empty cell if
•
▫
Settlement or Maturity is not a valid date.
▫
Price ≤ 0 or Redemption ≤ 0.
▫
Settlement ≥ Maturity.
The Settlement date and the Maturity date should be included within single quotations in
the expression for the expression to be considered as a valid expression.
Dollarde (dollar price, converted from fraction to
decimal)
(missing or bad snippet)
Dollarfr (dollar price, converted from decimal to
fraction)
(missing or bad snippet)
Duration
(missing or bad snippet)
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Effect (effective annual interest rate)
(missing or bad snippet)
▫ Npery <= 1.
Fv (future value)
(missing or bad snippet)
Fvschedule (future value schedule)
(missing or bad snippet)
Usage notes
•
If Argument is nonnumeric, the engine returns an empty cell.
•
Use the Fv function for payments made with a constant interest rate.
Intrate (interest rate)
(missing or bad snippet)
Expression
Where:
•
Redemption is the amount actually received for the security
•
Investment is the amount invested in the security
•
B is the number of days in a year, depending on year basis
•
DIM is the number of days from settlement to maturity
Usage notes
The Settlement date and the Maturity date should be included within single quotations in the
expression for the expression to be considered as a valid expression.
Ipmt (interest payment)
(missing or bad snippet)
IRR (internal rate of return)
(missing or bad snippet)
Mduration (modified duration)
(missing or bad snippet)
MIRR (modified internal rate of return)
(missing or bad snippet)
Nominal (nominal annual interest rate)
(missing or bad snippet)
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Nper (number of investment periods)
(missing or bad snippet)
NPV (net present value of an investment)
(missing or bad snippet)
Oddfprice (odd-first-period price)
(missing or bad snippet)
Expression
There are two expressions for this function:
•
Odd Short First Coupon: for securities with a short first period
•
Odd Long First Coupon: for securities with a long first period
Odd Short First Coupon
Where:
•
A is the number of days from beginning of coupon period to settlement date (accrued
days)
•
DSC is the number of days from settlement to next coupon date
•
DFC is the number of days from the beginning of odd first coupon to first coupon date
•
E is the number of days in coupon period
•
N is the number of coupons payable between settlement date and redemption date; if
this number contains a fraction, it is raised to the next whole number
Odd Long First Coupon
Where:
263
•
Ai is the number of days from beginning of the ith quasi-coupon period within odd
period
•
DCi is the Number of days from date to first quasi-coupon (i=1) or number of days in
quasi-coupons (i=2,..., i=NC)
•
DSC is the Number of days from settlement to next coupon date
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•
E is the Number of days in coupon period
•
N is the Number of coupons payable between the first real coupon date and redemption
date; if this number contains a fraction, it is raised to the next whole number
•
NC is the Number of quasi-coupon periods that fit in odd period; if this number contains a
fraction it is raised to the next whole number
•
NLi is the Normal length in days of the full ith quasi-coupon period within odd period
•
Nq is the Number of whole quasi-coupon periods between settlement date and first
coupon
Usage notes
The Settlement date and the Maturity date should be included within single quotations in the
expression for the expression to be considered as a valid expression.
Oddfyield (odd-first-period yield)
(missing or bad snippet)
Oddlprice (odd-last-period price)
(missing or bad snippet)
Oddlyield (odd-last-period yield)
(missing or bad snippet)
Pmt (payment)
(missing or bad snippet)
Ppmt (principal payment)
(missing or bad snippet)
Price (price per $100 face value)
(missing or bad snippet)
Pricedisc (price, discounted)
(missing or bad snippet)
Expression
Where:
•
B is the number of days in a year (see Basis)
•
DSM is the number of days from settlement to maturity
Usage notes
•
If Settlement or Maturity is not an integer, it is truncated.
•
The engine returns an empty cell if:
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•
▫
Settlement or Maturity is not a valid date.
▫
DiscRate ≤ 0 or Redemption ≤ 0.
▫
Settlement ≥ Maturity.
The Settlement date and the Maturity date should be included within single quotations in
the expression for the expression to be considered as a valid expression.
Pricemat (price at maturity)
(missing or bad snippet)
Expression
Where:
•
B is the number of days in a year (see Basis)
•
DSM is the number of days from settlement to maturity
•
DIM is the number of days from issue to maturity
•
A is the number of days from issue to settlement
Usage notes
•
If Settlement, Maturity, or Issue is not an integer, it is truncated.
•
The engine returns an empty cell if
•
▫
Settlement, Maturity, or Issue is not a valid date.
▫
Rate < 0 or Yield < 0.
▫
Settlement ≥ Maturity.
The Settlement date, the Maturity date, and the Issue date should be included within
single quotations in the expression for the expression to be considered as a valid
expression.
Pv (present value)
(missing or bad snippet)
Rate (interest rate per period)
Returns the interest rate per period on a given annuity.
Syntax
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Rate <FV, Type, Guess> (Nperiod, Payment, PV)
Where:
•
FV is the future value (also called cash balance) expected after the last payment.
•
Type indicates when payments are due.
•
Guess is an estimate assumed to be close to the result sought.
•
Nperiod is the total number of payment periods.
•
Payment is the payment made for each period. Cannot change over the life of the
annuity. Typically, includes principal and interest, but no other fees or taxes.
•
PV is the present value of the annuity. It is the total amount that a series of future
payments is worth today.
Usage notes
•
For this function, consistency in the units used is necessary:
▫
Assuming monthly payments on a four-year loan at 12% annual interest, Nperiod
should be 4 × 12.
▫
Assuming annual payments on a four-year loan at 12% annual interest, Nperiod
should be 4.
Received (amount received at maturity)
(missing or bad snippet)
Expression
Where:
•
B is the number of days in a year (see Basis)
•
DIM is the number of days between settlement and maturity
Usage notes
•
If Settlement or Maturity is not an integer, it is truncated.
•
The engine returns an empty cell if
•
▫
Settlement or Maturity is not a valid date.
▫
Investment ≤ 0.
▫
Discount ≤ 0.
▫
Maturity ≤ Settlement.
The Settlement date and the Maturity date should be included within single quotations in
the expression for the expression to be considered as a valid expression.
Sln (straight-line depreciation)
(missing or bad snippet)
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Syd (sum of year’s digits depreciation)
(missing or bad snippet)
Tbilleq (T-bill equity)
(missing or bad snippet)
Tbillprice (T-bill price)
(missing or bad snippet)
Tbillyield (T-bill yield)
(missing or bad snippet)
Vdb (variable declining balance)
(missing or bad snippet)
XIRR (internal rate of return for payments at irregular
intervals)
(missing or bad snippet)
XNPV (net present value of an investment for payments
or incomes at irregular intervals)
(missing or bad snippet)
Yield
(missing or bad snippet)
Yielddisc (yield on a discounted security)
(missing or bad snippet)
Yieldmat (yield at maturity)
(missing or bad snippet)
Mathematical functions
The category of Mathematical functions contains more complex math functions than the
simple operators found in the Basic functions. This category includes exponential,
logarithmic, and trigonometric functions. These functions are calculated by either the
MicroStrategy Analytical Engine or the database. Those not supported by the database are
automatically computed by the Analytical Engine.
Abs (absolute value)
(missing or bad snippet)
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Acos (arc cosine)
(missing or bad snippet)
Acosh (arc cosine, hyperbolic)
(missing or bad snippet)
Asin (arc sine)
(missing or bad snippet)
Asinh (arc sine, hyperbolic)
(missing or bad snippet)
Atan (arc tangent)
(missing or bad snippet)
Atan2 (arc tangent 2)
(missing or bad snippet)
Atanh (arc tangent, hyperbolic)
(missing or bad snippet)
Ceiling (ceiling value)
(missing or bad snippet)
Combine (combination)
(missing or bad snippet)
Cos (cosine)
(missing or bad snippet)
Cosh (cosine, hyperbolic)
(missing or bad snippet)
Degrees (conversion to)
Returns the value of an angle converted from radians to degrees. This is a single-value
function.
Syntax
Degrees(Argument)
Where:
Argument is a metric representing a list of values to be converted from radians to degrees.
Example
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These simple examples illustrate how the Degrees function converts an angle entered in
radians into degrees.
Function/Result
Calculation
Degrees(2.27) = 130
Degrees(π/2) = 90
Exp (exponent)
(missing or bad snippet)
Factorial (factorial)
(missing or bad snippet)
Floor (floor value)
(missing or bad snippet)
Int (integer)
(missing or bad snippet)
Ln (logarithm, natural)
(missing or bad snippet)
Log (logarithm)
(missing or bad snippet)
Log10 (logarithm, base 10)
(missing or bad snippet)
Mod (modulus)
(missing or bad snippet)
Power
(missing or bad snippet)
Quotient
(missing or bad snippet)
Radians (conversion to)
(missing or bad snippet)
Randbetween (random number between two values)
(missing or bad snippet)
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Round (round to nearest integer)
(missing or bad snippet)
Round2 (round to specified precision)
(missing or bad snippet)
Sin (sine)
(missing or bad snippet)
Sinh (sine, hyperbolic)
(missing or bad snippet)
Sqrt (square root)
(missing or bad snippet)
Tan (tangent)
(missing or bad snippet)
Tanh (tangent, hyperbolic)
Returns the value of the hyperbolic tangent of a given number. This is a single-value
function.
Syntax
Tanh(Argument)
Where:
Argument is a metric representing a list of real numbers.
Expression
Trunc (truncate)
(missing or bad snippet)
Statistical functions
The statistical functions include a wide range of functions designed to provide you with the
tools to perform statistical analysis on your data.
AvgDev (average deviation)
(missing or bad snippet)
BetaDistribution
(missing or bad snippet)
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BinomialDistribution
(missing or bad snippet)
ChiSquareDistribution
(missing or bad snippet)
ChiSquareTest (chi-square test for goodness of fit)
Confidence (confidence interval)
(missing or bad snippet)
Correlation
(missing or bad snippet)
Covariance
(missing or bad snippet)
CritBinomial (criterion binomial)
(missing or bad snippet)
ExponentialDistribution
(missing or bad snippet)
Fisher (fisher transformation)
(missing or bad snippet)
FDistribution (f-probability distribution)
(missing or bad snippet)
Forecast
(missing or bad snippet)
ForecastV (forecast, vector input)
(missing or bad snippet)
FTest
(missing or bad snippet)
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Definition
P-value = Pr[Reject H0 | H0 is correct]
P-value is basically a probability of making a Type 2 error.
FTest returns the p-value for the hypothesis test in the following form:
•
H0:
▫
•
(Argument1) /
(Argument2) = Ratio
▫
(Argument1) /
(Argument2) < Ratio (Type = -1)
▫
(Argument1) /
(Argument2) ≠ Ratio (Type = 0: two-sided test)
▫
(Argument1) /
(Argument2) > Ratio (Type = 1)
▫
(Argument1) /
(Argument2) ≠ Ratio (Type = 2: one-sided test)
H1:
Syntax
FTest <Hypothesis type, Ratio> (Argument1, Argument2)
Usage notes
The following are invalid conditions:
•
Argument1 and Argument2 contain a different number of data points.
•
The variance of either data set is zero.
GammaDistribution
(missing or bad snippet)
Growth
(missing or bad snippet)
GrowthV (growth, vector input)
(missing or bad snippet)
HeteroscedasticTTest and HomoscedasticTTest
(missing or bad snippet)
Definition
P-value = Pr[Reject H0 | H0 is correct]
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P-value is a probability of making a Type 2 error.
HeteroscedasticTTest or HomoscedasticTTest returns the p-value for the hypothesis test in
the following form:
•
For H0 :
▫
•
(Argument1) - (Argument2) = offset
For H1 :
▫
(Argument1) - (Argument2) < offset (Type = -1)
▫
(Argument1) - (Argument2) ≠ offset (Type = 0: two-sided test)
▫
(Argument1) - (Argument2) > offset (Type = 1)
▫
(Argument1) - (Argument2) ≠ offset (Type = 2: one-sided test)
Syntax
HeteroscedasticTTest or HomoscedasticTTest <Hypothesis
type, offset> (Argument1, Argument2)
Usage notes
•
Heteroscedastic t-tests are based on the assumption that variances between two
sample data ranges are unequal [ (Argument1) ≠
(Argument2)].
•
Homoscedastic t-tests are based on the assumption that variances between two sample
data ranges are equal [ (Argument1) =
(Argument2)].
•
The following are invalid conditions:
▫
Argument1 and Argument2 have a different number of data points, and Hypothesis
type = 1 (paired).
▫
Offset or Hypothesis type is nonnumeric.
Example
For an example using both Heteroscedastic T-test and Homoscedastic T-test, see
Hypothesis Testing example, page 70.
HypergeometricDistribution
(missing or bad snippet)
Intercept
(missing or bad snippet)
InverseBetaDistribution (inverse of the beta
distribution)
(missing or bad snippet)
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InverseChiDistribution (inverse of chi-squared
distribution)
(missing or bad snippet)
InverseFisher (inverse of the Fisher transformation)
(missing or bad snippet)
InverseFDistribution (inverse of F-probability
distribution)
(missing or bad snippet)
InverseGammaDistribution (inverse of gamma
distribution)
(missing or bad snippet)
InverseLognormalDistribution (inverse of lognormal
distribution)
(missing or bad snippet)
InverseNormDistribution (inverse of normal cumulative
distribution)
(missing or bad snippet)
InverseNormSDistribution (inverse of standard normal
cumulative distribution)
(missing or bad snippet)
InverseTDistribution (inverse of T-distribution)
(missing or bad snippet)
Kurtosis
(missing or bad snippet)
LognormalDistribution
(missing or bad snippet)
MeanTTest (mean T-test)
(missing or bad snippet)
Definition
P-value = Pr[Reject H0 | H0 is correct]
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Functions Reference
P-value is basically a probability of making a Type 2 error.
MeanTTest returns the p-value for the hypothesis test in the following form:
•
For H0 :
▫
•
(Argument) =
For H1 :
▫
(Argument) < (Type = -1)
▫
(Argument) ≠ (Type = 0: two-sided test)
▫
(Argument) > (Type = 1)
▫
(Argument) ≠ (Type = 2: one-sided test)
Syntax
MeanTTest <Hypothesis type> (Argument, Hypothesis mean)
Where:
•
Argument is the value or list of values, represented by a fact or metric, that contains
sample data.
•
Hypothesis mean is .
•
Hypothesis type denotes the type of t-test to be performed. Hypothesis type can be 1, 0, 1, or 2.
NegativeBinomialDistribution
(missing or bad snippet)
NormalDistribution (normal cumulative distribution)
(missing or bad snippet)
PairedTTest (paired T-test, two-sample for means)
(missing or bad snippet)
Pearson (Pearson product moment correlation
coefficient)
(missing or bad snippet)
Permut (permutation)
(missing or bad snippet)
PoissonDistribution
(missing or bad snippet)
275
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Functions Reference
RSquare (square of pearson product moment
correlation coefficient)
(missing or bad snippet)
Skew
(missing or bad snippet)
Slope (of a linear regression)
(missing or bad snippet)
Standardize
(missing or bad snippet)
StandardNormalDistribution (standard normal
cumulative distribution)
(missing or bad snippet)
SteYX (standard error of estimates)
(missing or bad snippet)
TDistribution
(missing or bad snippet)
Trend
(missing or bad snippet)
TrendV (trend, vector input)
(missing or bad snippet)
VarTest (variance test)
Returns the P-value of a test that tests the variance of the data against a particular value.
(missing or bad snippet)
WeibullDistribution
(missing or bad snippet)
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A
MICROSTRATEGY AND
DATABASE SUPPORT FOR
FUNCTIONS
The functions provided with MicroStrategy can be evaluated by the MicroStrategy Analytical
Engine or passed to the database for processing. The sections and tables in this appendix
list MicroStrategy Analytical Engine and database support for MicroStrategy functions.
Reviewing this support allows you to determine whether a MicroStrategy function can be
supported for your MicroStrategy environment.
•
Analytical Engine support for functions, page 277
•
Databases that a function can be evaluated on, page 281
For additional information on how functions are processed, see Types of function
processing, page 55.
Analytical Engine support for functions
The functions listed in the following table are supported by the MicroStrategy Analytical
Engine. This allows metrics to be evaluated by MicroStrategy in cases where functions
cannot be evaluated by a database. It also allows metrics to support smart metric
functionality (see the Advanced Reporting Guide).
Function type
Basic functions
Functions supported by the MicroStrategy Analytical Engine
• Add
• Average
• Avg
• Count
• First
• GeoMean
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Functions Reference
Function type
Functions supported by the MicroStrategy Analytical Engine
• Greatest
• Last
• Least
• Max
• Median
• Min
• Mode
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
Date and time functions
All date and time functions are supported by the
MicroStrategy Analytical Engine
Internal functions
• Banding
• BandingC
• BandingP
• Case
• CaseV
Null and Zero functions
• IsNotNull
• IsNull
• NullToEmpty
• NullToZero
• ZeroToNull
OLAP functions
• ExpWghMovingAvg
• ExpWghRunningAvg
• FirstInRange
• Lag
• LastInRange
• Lead
• MovingAvg
278
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Function type
Functions supported by the MicroStrategy Analytical Engine
• MovingCount
• MovingDifference
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• NTile
• NTileSize
• NTileValue
• NTileValueSize
• Percentile
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
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Functions Reference
Function type
Functions supported by the MicroStrategy Analytical Engine
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• All Arithmetic functions are supported by the
MicroStrategy Analytical Engine
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
280
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Functions Reference
Function type
Functions supported by the MicroStrategy Analytical Engine
• Not
• Or
Data mining functions
All data mining functions are supported by the MicroStrategy
Analytical Engine.
Financial functions
All financial functions are supported by the MicroStrategy
Analytical Engine.
Mathematical functions
All mathematical functions are supported by the
MicroStrategy Analytical Engine.
Statistical functions
All statistical functions are supported by the MicroStrategy
Analytical Engine.
Databases that a function can be evaluated on
The tables below list the function support for each database that is certified for use with
MicroStrategy. If a function is listed for a database, the function can be evaluated within that
database. If a function cannot be evaluated within a database, the function may be able to be
supported by the MicroStrategy Analytical Engine (see Analytical Engine support for
functions, page 277).
Except where explicitly stated, only databases that are certified to work with MicroStrategy
10 are listed below. For information on the certification and support for databases, see the
MicroStrategy Readme:
•
Actian Vectorwise, page 282
•
Aster Database, page 292
•
Calpont InfiniDB, page 306
•
EXASolution, page 316
•
Greenplum, page 322
•
Hadoop Hive, page 327
•
HP Vertica, page 330
•
IBM DB2, page 340
•
IBM Informix, page 375
•
IBM Netezza, page 387
•
IBM Red Brick, page 406
•
Infobright, page 410
•
Kognitio, page 415
•
Maria DB, page 419
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Functions Reference
•
Microsoft Access, page 424
•
Microsoft SQL Server, page 432
•
MySQL, page 461
•
Oracle, page 466
•
Actian Matrix, page 494
•
PostgreSQL, page 509
•
Salesforce.com, page 519
•
SAND CDBMS, page 523
•
SAP HANA 1.x, page 532
•
Sybase, page 541
•
Teradata, page 565
Actian Vectorwise
The tables listed below describe the MicroStrategy function support for Actian Vectorwise
databases:
•
Actian Vectorwise 2.5, page 282
•
Actian Vectorwise 3.0, page 287
Actian Vectorwise 2.5
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
282
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Function type
Functions that can be evaluated on the database
• StdevP
• Sum
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
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Function type
Functions that can be evaluated on the database
OLAP functions
• OLAPRank
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
284
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Functions Reference
Function type
Functions that can be evaluated on the database
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
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Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• RandBetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
286
• Correlation
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Intercept
• RSquare
• Slope
Actian Vectorwise 3.0
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
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Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• OLAPRank
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
288
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Functions Reference
Function type
Functions that can be evaluated on the database
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
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Functions Reference
Function type
Functions that can be evaluated on the database
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
290
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• RandBetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
© 2017, MicroStrategy Inc.
• Correlation
291
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Intercept
• RSquare
• Slope
Aster Database
The tables listed below describe the MicroStrategy function support for Aster Databases:
•
Aster Database 4.6.x, page 292
•
Databases that a function can be evaluated on, page 281
•
Aster Database 5.1.x, page 301
Aster Database 4.6.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
292
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Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
• MovingCount
• MovingMax
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Functions Reference
Function type
Functions that can be evaluated on the database
• MovingMin
• MovingSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
294
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Functions Reference
Function type
Functions that can be evaluated on the database
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
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Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
Aster Database 5.0.x
296
Function type
Functions that can be evaluated on the database
Basic functions
• Add
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
297
Functions Reference
Function type
Functions that can be evaluated on the database
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
None
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
• MovingCount
• MovingMax
298
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Functions Reference
Function type
Functions that can be evaluated on the database
• MovingMin
• MovingSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
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299
Functions Reference
Function type
Functions that can be evaluated on the database
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
300
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
Aster Database 5.1.x
Function type
Basic functions
© 2017, MicroStrategy Inc.
Functions that can be evaluated on the database
• Add
301
Functions Reference
Function type
Functions that can be evaluated on the database
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
302
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Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
None
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
• MovingCount
• MovingMax
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Functions Reference
Function type
Functions that can be evaluated on the database
• MovingMin
• MovingSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
304
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Functions Reference
Function type
Functions that can be evaluated on the database
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
305
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
Calpont InfiniDB
The tables listed below describe the MicroStrategy function support for Calpont InfiniDB
databases:
306
•
Calpont InfiniDB 2.2.x, page 307
•
Calpont InfiniDB 3.x, page 312
© 2017, MicroStrategy Inc.
Functions Reference
Calpont InfiniDB 2.2.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
307
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• ConcatBlank
• Concat
308
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Functions Reference
Function type
Functions that can be evaluated on the database
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
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309
Functions Reference
Function type
Functions that can be evaluated on the database
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
310
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
© 2017, MicroStrategy Inc.
None
311
Functions Reference
Calpont InfiniDB 3.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
312
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Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• ConcatBlank
• Concat
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Functions Reference
Function type
Functions that can be evaluated on the database
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
314
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Functions Reference
Function type
Functions that can be evaluated on the database
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
315
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
EXASolution
The tables listed below describe the MicroStrategy function support for EXASolution
databases:
•
316
EXASolution 4.x, page 317
© 2017, MicroStrategy Inc.
Functions Reference
EXASolution 4.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Median
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
317
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
• MovingCount
• MovingMax
318
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingMin
• MovingSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
© 2017, MicroStrategy Inc.
319
Functions Reference
Function type
Functions that can be evaluated on the database
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
320
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
• Correlation
• Covariance
• Intercept
• RSquare
• Slope
© 2017, MicroStrategy Inc.
321
Functions Reference
Greenplum
The tables listed below describe the MicroStrategy function support for Greenplum
databases:
•
Greenplum 4.x, page 322
Greenplum 4.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
322
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
© 2017, MicroStrategy Inc.
323
Functions Reference
Function type
Functions that can be evaluated on the database
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
324
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
325
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
• Correlation
• Covariance
• Intercept
• RSquare
• Slope
326
© 2017, MicroStrategy Inc.
Functions Reference
Hadoop Hive
The tables listed below describe the MicroStrategy function support for Hadoop Hive
databases:
•
Hadoop Hive, page 327
Hadoop Hive
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• First
• GeoMean
• Greatest
• Last
• Least
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
327
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DaysBetween
• Hour
• Minute
• Month
• Second
• Week
• Year
Internal functions
None
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
328
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
© 2017, MicroStrategy Inc.
329
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
Statistical functions
• Covariance
• Standardize
HP Vertica
The tables listed below describe the MicroStrategy function support for HP Vertica
databases:
330
© 2017, MicroStrategy Inc.
Functions Reference
•
HP Vertica 5.1 , page 331
•
HP Vertica 6.x , page 336
HP Vertica 5.1
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
331
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
None
Null and Zero functions
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
332
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
© 2017, MicroStrategy Inc.
333
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
334
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
© 2017, MicroStrategy Inc.
• Standardize
335
Functions Reference
HP Vertica 6.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
336
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
None
Null and Zero functions
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
© 2017, MicroStrategy Inc.
337
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
338
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
© 2017, MicroStrategy Inc.
339
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
• Standardize
IBM DB2
The tables listed below describe the MicroStrategy function support for IBM DB2 databases:
340
•
DB2 V9.5 for Linux, UNIX, and Windows, page 341
•
DB2 V9.7 for Linux, UNIX, and Windows, page 347
© 2017, MicroStrategy Inc.
Functions Reference
•
DB2 V10.1 for Linux, UNIX, and Windows, page 353
•
DB2 V10.5 for Linux, UNIX, and Windows, page 359
•
DB2 for i 6.1, page 365
•
DB2 for i 7.1, page 370
DB2 V9.5 for Linux, UNIX, and Windows
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
341
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
342
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
© 2017, MicroStrategy Inc.
343
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
344
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
345
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
346
• Correlation
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Intercept
• RSquare
• Slope
DB2 V9.7 for Linux, UNIX, and Windows
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
347
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
348
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
© 2017, MicroStrategy Inc.
349
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
350
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
351
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
352
• Correlation
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Intercept
• RSquare
• Slope
DB2 V10.1 for Linux, UNIX, and Windows
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
353
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
354
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
© 2017, MicroStrategy Inc.
355
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
356
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
357
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
358
• Correlation
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Intercept
• RSquare
• Slope
DB2 V10.5 for Linux, UNIX, and Windows
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
359
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
360
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
© 2017, MicroStrategy Inc.
361
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
362
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
363
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
364
• Correlation
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Intercept
• RSquare
• Slope
DB2 for i 6.1
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
365
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
366
OLAP functions
None
Rank and NTile functions
• Rank
String functions
• Concat
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
© 2017, MicroStrategy Inc.
367
Functions Reference
Function type
Functions that can be evaluated on the database
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
368
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
© 2017, MicroStrategy Inc.
None
369
Functions Reference
DB2 for i 7.1
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
370
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
• Rank
String functions
• Concat
© 2017, MicroStrategy Inc.
371
Functions Reference
Function type
Functions that can be evaluated on the database
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
372
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
373
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
374
None
© 2017, MicroStrategy Inc.
Functions Reference
IBM Informix
The tables listed below describe the MicroStrategy function support for IBM Informix
databases:
•
IBM Informix IDS 11.5 and Informix Ultimate Edition 11.7, page 375
•
IBM Informix Ultimate Edition 12.1, page 379
•
IBM Informix XPS 8.x, page 383
IBM Informix IDS 11.5 and Informix Ultimate Edition 11.7
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• Max
• Min
• Multiply
• Stdev
• Sum
• Var
© 2017, MicroStrategy Inc.
375
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
376
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
© 2017, MicroStrategy Inc.
377
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
378
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Asin
• Atan
• Atan2
• Ceiling
• Cos
• Exp
• Floor
• Int
• Int2
• Ln
• Log10
• Mod
• Power
• Quotient
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
IBM Informix Ultimate Edition 12.1
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• Max
• Min
© 2017, MicroStrategy Inc.
379
Functions Reference
Function type
Functions that can be evaluated on the database
• Multiply
• Stdev
• Sum
• Var
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
380
© 2017, MicroStrategy Inc.
Functions Reference
Function type
OLAP functions
Functions that can be evaluated on the database
• FirstInRange
• Lag
• LastInRange
• Lead
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
© 2017, MicroStrategy Inc.
381
Functions Reference
Function type
Functions that can be evaluated on the database
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
382
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Asin
• Atan
• Atan2
• Ceiling
• Cos
• Exp
• Floor
• Int
• Int2
• Ln
• Log10
• Mod
• Power
• Quotient
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
IBM Informix XPS 8.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• Max
• Min
© 2017, MicroStrategy Inc.
383
Functions Reference
Function type
Functions that can be evaluated on the database
• Multiply
• Stdev
• Sum
• Var
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
384
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
© 2017, MicroStrategy Inc.
385
Functions Reference
Function type
Functions that can be evaluated on the database
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
386
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Asin
• Atan
• Atan2
• Ceiling
• Cos
• Exp
• Floor
• Int
• Int2
• Ln
• Log10
• Mod
• Power
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
IBM Netezza
The tables listed below describe the MicroStrategy function support for IBM Netezza
databases:
•
IBM Netezza 5.0.x, page 388
•
IBM Netezza 6.0.x, page 394
•
IBM Netezza 7.0.x, page 400
© 2017, MicroStrategy Inc.
387
Functions Reference
IBM Netezza 5.0.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
388
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
© 2017, MicroStrategy Inc.
389
Functions Reference
Function type
Functions that can be evaluated on the database
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
390
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
© 2017, MicroStrategy Inc.
391
Functions Reference
Function type
Functions that can be evaluated on the database
• Or
392
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
© 2017, MicroStrategy Inc.
• Correlation
393
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Fisher
• Intercept
• InverseFisher
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
IBM Netezza 6.0.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
394
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
© 2017, MicroStrategy Inc.
395
Functions Reference
Function type
Functions that can be evaluated on the database
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
396
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
© 2017, MicroStrategy Inc.
397
Functions Reference
Function type
Functions that can be evaluated on the database
• Or
398
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
© 2017, MicroStrategy Inc.
• Correlation
399
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
IBM Netezza 7.0.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
400
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
© 2017, MicroStrategy Inc.
401
Functions Reference
Function type
Functions that can be evaluated on the database
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
402
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
© 2017, MicroStrategy Inc.
403
Functions Reference
Function type
Functions that can be evaluated on the database
• Or
404
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
© 2017, MicroStrategy Inc.
• Correlation
405
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
IBM Red Brick
The tables listed below describe the MicroStrategy function support for IBM Red Brick
databases:
•
IBM Red Brick 6.3, page 406
IBM Red Brick 6.3
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• Max
• Min
• Multiply
• Sum
406
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
© 2017, MicroStrategy Inc.
407
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingCount
• MovingMax
• MovingMin
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
408
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Comparison operators
Functions that can be evaluated on the database
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
409
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Ceiling
• Exp
• Floor
• Int
• Int2
• Ln
• Mod
• Quotient
• Round
• Round2
• Sqrt
Statistical functions
None
Infobright
The tables listed below describe the MicroStrategy function support for Infobright databases:
•
Infobright 4.0.x, page 410
Infobright 4.0.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
410
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Stdev
• StdevP
• Sum
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
© 2017, MicroStrategy Inc.
411
Functions Reference
Function type
Null and Zero functions
Functions that can be evaluated on the database
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
412
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
413
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
414
None
© 2017, MicroStrategy Inc.
Functions Reference
Kognitio
The tables listed below describe the MicroStrategy function support for Kognitio databases:
•
Kognitio WX2 7.x, page 415
Kognitio WX2 7.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• First
• GeoMean
• Greatest
• Last
• Least
• Max
• Min
• Multiply
• StdevP
• Sum
• VarP
© 2017, MicroStrategy Inc.
415
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
None
Null and Zero functions
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdevP
• MovingSum
416
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
© 2017, MicroStrategy Inc.
417
Functions Reference
Function type
Functions that can be evaluated on the database
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
418
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Ln
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
• Standardize
Maria DB
The tables listed below describe the MicroStrategy function support for Maria DB databases:
•
Maria DB 5.5.x, page 420
© 2017, MicroStrategy Inc.
419
Functions Reference
Maria DB 5.5.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
420
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
© 2017, MicroStrategy Inc.
421
Functions Reference
Function type
Functions that can be evaluated on the database
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
422
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
423
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
Microsoft Access
The tables listed below describe the MicroStrategy function support for Microsoft Access
databases:
•
424
Microsoft Access 2000, 2002, or 2003, page 425
© 2017, MicroStrategy Inc.
Functions Reference
•
Microsoft Access 2007, page 428
Microsoft Access 2000, 2002, or 2003
Microsoft Access 2000, 2002, or 2003 are only supported for demonstration purposes.
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• Max
• Min
• Multiply
• Sum
© 2017, MicroStrategy Inc.
425
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
426
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• InitCap
• LeftStr
• Length
• Lower
• Position
• RightStr
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
© 2017, MicroStrategy Inc.
427
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
Mathematical functions
• Floor
• Int
• Int2
• Ln
• Mod
• Quotient
Statistical functions
None
Microsoft Access 2007
Microsoft Access 2007 is only supported for demonstration purposes.
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
428
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Stdev
• StdevP
• Sum
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Millisecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
© 2017, MicroStrategy Inc.
429
Functions Reference
Function type
Null and Zero functions
Functions that can be evaluated on the database
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
430
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
431
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Cos
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Round
• Round2
• Sin
• Sqrt
• Tan
Statistical functions
None
Microsoft SQL Server
The tables listed below describe the MicroStrategy function support for Microsoft SQL
Server databases:
432
•
Microsoft SQL Server 2005, page 433
•
Microsoft SQL Server 2008, page 438
•
Microsoft SQL Server 2008 R2 Parallel Data Warehouse, page 443
•
Microsoft SQL Server 2012, page 447
•
Microsoft SQL Server 2012 Parallel Data Warehouse, page 452
•
Microsoft SQL Database, page 457
© 2017, MicroStrategy Inc.
Functions Reference
Microsoft SQL Server 2005
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
433
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
434
OLAP functions
• OLAPRank
Rank and NTile functions
• Rank
String functions
• Concat
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
© 2017, MicroStrategy Inc.
435
Functions Reference
Function type
Functions that can be evaluated on the database
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
436
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
© 2017, MicroStrategy Inc.
None
437
Functions Reference
Microsoft SQL Server 2008
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
438
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• OLAPRank
Rank and NTile functions
• Rank
String functions
• Concat
© 2017, MicroStrategy Inc.
439
Functions Reference
Function type
Functions that can be evaluated on the database
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
440
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
441
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
442
None
© 2017, MicroStrategy Inc.
Functions Reference
Microsoft SQL Server 2008 R2 Parallel Data Warehouse
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
443
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• OLAPRank
444
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
© 2017, MicroStrategy Inc.
445
Functions Reference
Function type
Functions that can be evaluated on the database
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
446
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acosh
• Asin
• Asinh
• Atan
• Atanh
• Ceiling
• Cos
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
Microsoft SQL Server 2012
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
© 2017, MicroStrategy Inc.
447
Functions Reference
Function type
Functions that can be evaluated on the database
• GeoMean
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
448
• Banding
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
© 2017, MicroStrategy Inc.
449
Functions Reference
Function type
Functions that can be evaluated on the database
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
450
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
451
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atanh
• Atan2
• Ceiling
• Cos
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
Microsoft SQL Server 2012 Parallel Data Warehouse
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
452
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
453
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• OLAPRank
454
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
© 2017, MicroStrategy Inc.
455
Functions Reference
Function type
Functions that can be evaluated on the database
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
456
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acosh
• Asin
• Asinh
• Atan
• Atanh
• Ceiling
• Cos
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
Microsoft SQL Database
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
© 2017, MicroStrategy Inc.
457
Functions Reference
Function type
Functions that can be evaluated on the database
• GeoMean
• Max
• Min
• Multiply
• Stdev
• StdevP
• Sum
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
458
• Banding
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• OLAPRank
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
© 2017, MicroStrategy Inc.
459
Functions Reference
Function type
Functions that can be evaluated on the database
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
460
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
MySQL
The tables listed below describe the MicroStrategy function support for MySQL databases:
•
MySQL 5.x, page 462
© 2017, MicroStrategy Inc.
461
Functions Reference
MySQL 5.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
462
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
© 2017, MicroStrategy Inc.
463
Functions Reference
Function type
Functions that can be evaluated on the database
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
464
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
465
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
None
Oracle
The tables listed below describe the MicroStrategy function support for Oracle databases:
•
466
Oracle 10g, page 467
© 2017, MicroStrategy Inc.
Functions Reference
•
Oracle 10gR2, page 472
•
Oracle 11g, page 478
•
Oracle 11g R2, page 483
•
Oracle 12c, page 489
Oracle 10g
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• First
• GeoMean
• Greatest
• Last
• Least
• Max
• Median
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
467
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
468
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
© 2017, MicroStrategy Inc.
469
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
470
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
© 2017, MicroStrategy Inc.
471
Functions Reference
Function type
Statistical functions
Functions that can be evaluated on the database
• Correlation
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
Oracle 10gR2
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• First
• GeoMean
• Greatest
• Last
• Least
• Max
• Median
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
472
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
© 2017, MicroStrategy Inc.
473
Functions Reference
Function type
Functions that can be evaluated on the database
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
474
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
© 2017, MicroStrategy Inc.
475
Functions Reference
Function type
Functions that can be evaluated on the database
• Not
• Or
476
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
• Correlation
• Covariance
© 2017, MicroStrategy Inc.
477
Functions Reference
Function type
Functions that can be evaluated on the database
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
Oracle 11g
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• First
• GeoMean
• Greatest
• Last
• Least
• Max
• Median
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
478
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
© 2017, MicroStrategy Inc.
479
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
480
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
© 2017, MicroStrategy Inc.
481
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
482
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Statistical functions
Functions that can be evaluated on the database
• Correlation
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
Oracle 11g R2
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• First
• GeoMean
• Greatest
• Last
• Least
• Max
• Median
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
© 2017, MicroStrategy Inc.
483
Functions Reference
Function type
Functions that can be evaluated on the database
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
484
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
© 2017, MicroStrategy Inc.
485
Functions Reference
Function type
Functions that can be evaluated on the database
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
486
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
487
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
• Correlation
• Covariance
488
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
Oracle 12c
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• First
• GeoMean
• Greatest
• Last
• Least
• Max
• Median
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
489
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
490
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingStdevP
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
© 2017, MicroStrategy Inc.
491
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
492
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
© 2017, MicroStrategy Inc.
493
Functions Reference
Function type
Statistical functions
Functions that can be evaluated on the database
• Correlation
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
Actian Matrix
The tables listed below describe the MicroStrategy function support for Actian Matrix
databases:
Actian Matrix was previously referred to as ParAccel.
•
Actian Matrix 3.1.x, page 494
•
Actian Matrix 3.5.x, page 499
•
Actian Matrix 4.0.x, page 504
Actian Matrix 3.1.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
494
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Multiply
• Product
• Sum
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
© 2017, MicroStrategy Inc.
• FirstInRange
495
Functions Reference
Function type
Functions that can be evaluated on the database
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
496
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
© 2017, MicroStrategy Inc.
497
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
498
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Statistical functions
Functions that can be evaluated on the database
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• RSquare
• Slope
• Standardize
Actian Matrix 3.5.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Sum
• Stdev
• StdevP
• Var
• VarP
© 2017, MicroStrategy Inc.
499
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
500
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingMax
• MovingMin
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
© 2017, MicroStrategy Inc.
• <
501
Functions Reference
Function type
Functions that can be evaluated on the database
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
502
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Tan
• Tanh
• Trunc
Statistical functions
• Covariance
• Fisher
• Intercept
© 2017, MicroStrategy Inc.
503
Functions Reference
Function type
Functions that can be evaluated on the database
• InverseFisher
• Kurtosis
• RSquare
• Slope
• Standardize
Actian Matrix 4.0.x
Function type
Functions that can be evaluated on the database
Basic functions
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Sum
• Stdev
• StdevP
• Var
• VarP
504
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
© 2017, MicroStrategy Inc.
505
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingMax
• MovingMin
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
506
• <
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
507
Functions Reference
Function type
Functions that can be evaluated on the database
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Tan
• Tanh
• Trunc
Statistical functions
• Covariance
• Fisher
• Intercept
508
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• InverseFisher
• Kurtosis
• RSquare
• Slope
• Standardize
PostgreSQL
The tables listed below describe the MicroStrategy function support for PostgreSQL
databases:
•
PostgreSQL 8.4, page 509
•
PostgreSQL 9.x, page 514
PostgreSQL 8.4
Function type
Functions that can be evaluated on the database
Basic functions
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
509
Functions Reference
Function type
Functions that can be evaluated on the database
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• LastInRange
• OLAPRank
Rank and NTile functions
510
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
© 2017, MicroStrategy Inc.
511
Functions Reference
Function type
Functions that can be evaluated on the database
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
512
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
• Correlation
• Covariance
• Intercept
• RSquare
• Slope
© 2017, MicroStrategy Inc.
513
Functions Reference
PostgreSQL 9.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
514
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• LastInRange
• OLAPRank
© 2017, MicroStrategy Inc.
515
Functions Reference
Function type
Functions that can be evaluated on the database
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
516
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
517
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
• Correlation
• Covariance
• Intercept
• RSquare
• Slope
518
© 2017, MicroStrategy Inc.
Functions Reference
Salesforce.com
The tables listed below describe the MicroStrategy function support for Salesforce.com data
sources:
•
Salesforce.com, page 519
Salesforce.com
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Sum
Date and time functions
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthsBetween
• Quarter
• Second
• Week
• Year
© 2017, MicroStrategy Inc.
519
Functions Reference
Function type
Functions that can be evaluated on the database
Internal functions
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
520
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
521
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atanh
• Atan2
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Randbetween
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
Statistical functions
522
• Standardize
© 2017, MicroStrategy Inc.
Functions Reference
SAND CDBMS
The tables listed below describe the MicroStrategy function support for SAND CDBMS
databases:
•
SAND CDBMS 6.1, page 523
•
SAND CDBMS 7.1, page 527
SAND CDBMS 6.1
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Median
• Min
• Multiply
• Product
• Stdev
• Sum
• Var
© 2017, MicroStrategy Inc.
523
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
524
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
© 2017, MicroStrategy Inc.
525
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
526
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Ln
• Log10
• Power
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
Statistical functions
• Correlation
• Covariance
• Intercept
SAND CDBMS 7.1
Function type
Basic functions
© 2017, MicroStrategy Inc.
Functions that can be evaluated on the database
• Add
527
Functions Reference
Function type
Functions that can be evaluated on the database
• Average
• Avg
• Count
• GeoMean
• Max
• Median
• Min
• Multiply
• Product
• Stdev
• Sum
• Var
528
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
© 2017, MicroStrategy Inc.
529
Functions Reference
Function type
Functions that can be evaluated on the database
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
• Upper
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
530
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
531
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Ln
• Log10
• Power
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
Statistical functions
• Correlation
• Covariance
• Intercept
SAP HANA 1.x
The tables listed below describe the MicroStrategy function support for SAP HANA 1.x
databases:
532
•
SAP HANA 1.0 SP4 , page 533
•
SAP HANA 1.0 SP5 , page 537
© 2017, MicroStrategy Inc.
Functions Reference
SAP HANA 1.0 SP4
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Stdev
• Sum
• Var
© 2017, MicroStrategy Inc.
533
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
Null and Zero functions
• NullToZero
• ZeroToNull
534
OLAP functions
None
Rank and NTile functions
None
String functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• NotIn
• NotLike
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
© 2017, MicroStrategy Inc.
535
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
536
None
© 2017, MicroStrategy Inc.
Functions Reference
SAP HANA 1.0 SP5
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Stdev
• Sum
• Var
© 2017, MicroStrategy Inc.
537
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
Null and Zero functions
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• OLAPRank
538
Rank and NTile functions
None
String functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• NotIn
• NotLike
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
© 2017, MicroStrategy Inc.
539
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
540
None
© 2017, MicroStrategy Inc.
Functions Reference
Sybase
The tables listed below describe the MicroStrategy function support for SAP Sybase
databases:
•
SAP Sybase ASE 15.x, page 541
•
SAP Sybase IQ 15.2, page 545
•
SAP Sybase IQ 15.3, page 550
•
SAP Sybase IQ 15.4, page 555
•
SAP Sybase IQ 16.0, page 560
SAP Sybase ASE 15.x
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• Max
• Min
• Multiply
• Sum
© 2017, MicroStrategy Inc.
541
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
None
Rank and NTile functions
None
String functions
• Concat
• ConcatBlank
• InitCap
542
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
© 2017, MicroStrategy Inc.
543
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
544
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Asin
• Atan
• Atan2
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sqrt
• Tan
Statistical functions
None
SAP Sybase IQ 15.2
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
© 2017, MicroStrategy Inc.
545
Functions Reference
Function type
Functions that can be evaluated on the database
• Max
• Median
• Min
• Multiply
• Stdev
• Sum
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
546
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
© 2017, MicroStrategy Inc.
547
Functions Reference
Function type
Functions that can be evaluated on the database
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
548
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
549
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Asin
• Atan
• Atan2
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
• Correlation
SAP Sybase IQ 15.3
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
550
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• GeoMean
• Max
• Median
• Min
• Multiply
• Stdev
• Sum
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
© 2017, MicroStrategy Inc.
• Banding
551
Functions Reference
Function type
Functions that can be evaluated on the database
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
552
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
© 2017, MicroStrategy Inc.
553
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
554
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Asin
• Atan
• Atan2
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
• Correlation
SAP Sybase IQ 15.4
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
© 2017, MicroStrategy Inc.
555
Functions Reference
Function type
Functions that can be evaluated on the database
• GeoMean
• Max
• Median
• Min
• Multiply
• Stdev
• Sum
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
556
• Banding
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
© 2017, MicroStrategy Inc.
557
Functions Reference
Function type
Functions that can be evaluated on the database
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
558
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
559
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Asin
• Atan
• Atan2
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
• Correlation
SAP Sybase IQ 16.0
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
560
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• GeoMean
• Max
• Median
• Min
• Multiply
• Stdev
• Sum
• Var
• VarP
Date and time functions
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
© 2017, MicroStrategy Inc.
• Banding
561
Functions Reference
Function type
Functions that can be evaluated on the database
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• FirstInRange
• Lag
• LastInRange
• Lead
• MovingAvg
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
562
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
Arithmetic operators
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
© 2017, MicroStrategy Inc.
563
Functions Reference
Function type
Comparison operators for rank
Functions that can be evaluated on the database
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
564
Data mining functions
None
Financial functions
None
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Mathematical functions
Functions that can be evaluated on the database
• Abs
• Acos
• Asin
• Atan
• Atan2
• Ceiling
• Cos
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sqrt
• Tan
• Trunc
Statistical functions
• Correlation
Teradata
The tables listed below describe the MicroStrategy function support for Teradata databases:
•
Teradata 12, page 566
•
Teradata 13, page 571
•
Teradata 13.10, page 576
•
Teradata 14.0, page 581
•
Teradata 14.10, page 586
© 2017, MicroStrategy Inc.
565
Functions Reference
Teradata 12
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
566
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
• MovingCount
• MovingMax
© 2017, MicroStrategy Inc.
567
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingMin
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
568
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
© 2017, MicroStrategy Inc.
569
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
570
• Correlation
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
Teradata 13
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
571
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
• MovingCount
• MovingMax
572
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingMin
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
© 2017, MicroStrategy Inc.
573
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
574
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
© 2017, MicroStrategy Inc.
• Correlation
575
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
Teradata 13.10
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
576
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
• MovingCount
• MovingMax
© 2017, MicroStrategy Inc.
577
Functions Reference
Function type
Functions that can be evaluated on the database
• MovingMin
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
578
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
© 2017, MicroStrategy Inc.
579
Functions Reference
Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
Statistical functions
580
• Correlation
© 2017, MicroStrategy Inc.
Functions Reference
Function type
Functions that can be evaluated on the database
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
Teradata 14.0
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
© 2017, MicroStrategy Inc.
581
Functions Reference
Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
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Function type
Functions that can be evaluated on the database
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
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Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
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Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
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Function type
Statistical functions
Functions that can be evaluated on the database
• Correlation
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
Teradata 14.10
Function type
Basic functions
Functions that can be evaluated on the database
• Add
• Average
• Avg
• Count
• GeoMean
• Greatest
• Least
• Max
• Min
• Multiply
• Product
• Stdev
• StdevP
• Sum
• Var
• VarP
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Function type
Date and time functions
Functions that can be evaluated on the database
• AddDays
• AddMonths
• CurrentDate
• CurrentDateTime
• CurrentTime
• Date
• DayOfMonth
• DayOfWeek
• DayOfYear
• DaysBetween
• Hour
• MilliSecond
• Minute
• Month
• MonthEndDate
• MonthsBetween
• MonthStartDate
• Quarter
• Second
• Week
• Year
• YearEndDate
• YearStartDate
Internal functions
• Banding
• BandingC
• Coalesce
Null and Zero functions
• IsNotNull
• IsNull
• NullToZero
• ZeroToNull
OLAP functions
• Lag
• Lead
• MovingAvg
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Function type
Functions that can be evaluated on the database
• MovingCount
• MovingMax
• MovingMin
• MovingStdev
• MovingSum
• OLAPAvg
• OLAPCount
• OLAPMax
• OLAPMin
• OLAPRank
• OLAPSum
• RunningAvg
• RunningCount
• RunningMax
• RunningMin
• RunningStdev
• RunningStdevP
• RunningSum
Rank and NTile functions
• Rank
String functions
• Concat
• ConcatBlank
• InitCap
• LeftStr
• Length
• Lower
• LTrim
• Position
• RightStr
• RTrim
• SubStr
• Trim
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Function type
Arithmetic operators
Functions that can be evaluated on the database
• • x
• +
• /
• U-
Comparison operators
• <
• <=
• <>
• =
• >
• >=
• Begins With
• Between
• Contains
• Ends With
• In
• Like
• Not Begins With
• Not Between
• Not Contains
• Not Ends With
• Not In
• Not Like
Comparison operators for rank
• *<=
• *<>
• *=
• *>=
• *Between
• Not*Between
Logical operators
• AND
• IF
• Not
• Or
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Function type
Functions that can be evaluated on the database
Data mining functions
None
Financial functions
None
Mathematical functions
• Abs
• Acos
• Acosh
• Asin
• Asinh
• Atan
• Atan2
• Atanh
• Ceiling
• Cos
• Cosh
• Degrees
• Exp
• Floor
• Int
• Int2
• Ln
• Log
• Log10
• Mod
• Power
• Quotient
• Radians
• Round
• Round2
• Sin
• Sinh
• Sqrt
• Tan
• Tanh
• Trunc
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Function type
Statistical functions
Functions that can be evaluated on the database
• Correlation
• Covariance
• Fisher
• Intercept
• InverseFisher
• Kurtosis
• Pearson
• RSquare
• Skew
• Slope
• Standardize
• SteYX
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1
GLOSSARY
A
account
Creates a relationship between access privileges and user login credentials.
Account permissions are based on granted roles, and each role has specific
privileges. See also: application administrator, application designer, subscription
administrator, system administrator.
Activation Code
A code used to activate MicroStrategy Intelligence Server after installation. This
code is sent to an email address provided during activation.
ad hoc query
A SQL query dynamically constructed by desktop tools and whose results are not
known before it is sent to the server. The user is asking a new question that has not
been answered by an existing report.
address
Set of information that tells Narrowcast Server how to send services to a particular
subscriber. Each address can be associated with one and only one login/user. Each
address is defined to use a specific device. See also: device.
address display
A name that is displayed by receiving systems. For email delivery, the address
display is used to identify the email address. For example,
johnsmith@microstrategy-tutorial.demo might be displayed as “Smith, John” by the
receiving email client.
address ID
An ID value that identifies individual addresses. Each address can be subscribed to
a subscription set multiple times.
administration object
Narrowcast Server components that control the processing of Narrowcast
messages, providing the means to acquire, format, and transmit messages to
recipients. These objects are created and configured by the system administrator.
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administrator
A user who installs and monitors software and user configurations, maintains the
state of the software, and administers the MicroStrategy servers in the platform. An
administrator also defines users, assigns user login accounts and user privileges,
and analyzes the performance of the system.
aggregate data
Information or facts added together or "aggregated" to form summaries of
information considered as a whole.
aggregate function
A numeric function that acts on a column of data and produces a single result.
Examples include SUM, COUNT, MAX, MIN, and AVG.
aggregate table
A fact table that stores data that has been aggregated along one or more
dimensions.
All Subscription Data
An XML string that returns subscription information. It requires you to select an XSL
stylesheet to format the subscription information returned as desired.
analyst
A user who analyzes business data by accessing reports, performing drilling, and
otherwise manipulating reports and documents to see required business data. An
analyst receives useful data from information devices like smart phones and email
without necessarily understanding how such information is derived or delivered.
analytical application
In MicroStrategy, a software application designed to provide predefined reports and
other analytics based on a predefined metadata repository, for various industries to
gain insight into their business data. The application is not fixed to a specific physical
schema, giving it the flexibility to be ported to a company's existing data warehouse.
Analytical Engine
A component of the MicroStrategy Intelligence Server that performs all advanced
analytical functions. The Analytical Engine evaluates functions not supported by the
data warehouse RDBMS and it cross-tabulates reports.
analytics
Predefined tools that allow analysis within the Analysis Module's functional areas.
Analytics include reports (graph, grid, and so on), scorecards, dashboards, and so
on.
analytics library
The collection of reports and related objects in the MicroStrategy metadata
repository. Library objects include reports, metrics, filters, and prompts. Library
objects are defined based on attributes and facts (objects in the logical data model.)
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application administrator
Narrowcast Administrator user role. This role is designed for a Narrowcast
Administrator console user who performs some of the same tasks as the application
designer but has the ability to modify application objects created by any Narrowcast
Administrator account. This role is intended for quality managers, development
managers, configuration managers, and other users requiring full access to all
application objects. See also: application designer.
application designer
A Narrowcast Administrator user role. This role is designed for a Narrowcast
Administrator console user who develops and tests services, which include
documents, publications, schedules, and subscription sets. Configures and
manages Subscription Portals that allow end users to subscribe to a variety of
Narrowcast services via the Web. Configures data sources, content, and portal
layout. Publishes services and device types, and selects default devices for
Subscription Portals. Specifies information source properties and default site
preferences. Installs, configures, and administers the development environment.
Administers subscribers and subscriptions for development and testing purposes.
See also: application administrator.
application object
MicroStrategy object used to provide analysis of and insight into relevant data.
Application objects are developed in MicroStrategy Developer and they are the
building blocks for reports and documents. Application objects include these object
types: report, document, template, filter, metric, custom group, consolidation,
prompt.
application program interface (API)
A set of related functions that provides an interface between existing applications
and new applications. The API can be seen as a platform over a set of services on
which new applications can be built. The functions, or interfaces, are implemented in
a Dynamic Link Library and are defined in a standardized syntax. Application
functionality available in the platform can be integrated or embedded into other
applications through the use of the APIs.
attachment
Any file that is included in an email message. Attachments can originate from
outside Narrowcast Server (that is, any pre-existing file can be sent as part of a
service) or can be created by Narrowcast Server (that is, Narrowcast Server can
create an Excel attachment on the fly).
attribute
A data level defined by the system architect and associated with one or more
columns in a data warehouse lookup table. Attributes include data classifications like
Region, Order, Customer, Age, Item, City, and Year. They provide a means for
aggregating and filtering at a given level.
attribute element
A value of any of the attribute forms of an attribute. For example, New York and
Dallas are elements of the attribute City; January, February, and March are
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elements of the attribute Month.
attribute form
One of several columns associated with an attribute that are different aspects of the
same thing. Every attribute supports its own collection of forms.
attrition rate
The number of lost employees divided by the number of employees in a given time
period.
authentication object
Object used by an information source that specifies who the user is and the security
context within which that user will interact with that information source. Contains the
security information required to make a connection or perform task execution.
Depending on the information source module, it should contain information such as
the user login name and password. For a MicroStrategy Information Source, the
object is a MicroStrategy user. See also: personalization object.
auto text code
Dynamic text that is populated by the document or dataset, consisting of the
document’s or dataset’s settings rather than data from the data warehouse.
Examples of auto text codes, which can be considered as a type of variable, are
document name, page number, and execution time. Auto text codes are contained
in text field controls on a document. See also: Data field, Text field.
autostyle
A set of predefined formatting that can be easily applied to many reports in either
MicroStrategy Developer or MicroStrategy Web. Autostyles are a good way to apply
a corporate look and feel to reports.
axes (axis)
(1) A vector along which data is displayed. There are three axes—Row, Column,
and Page. When a user defines a template for a report, he places template units—
attributes, dimensions, metrics, consolidations, and custom groups—along each
axis. (2) One part of a multi-part graphical diagram. Many SDAM reports display
data on more than one graphical axis, such as the Quotation Activity Summary
report and the Quarterly Conversion Summary report.
B
banding
A method of organizing values according to a set of descriptive or meaningful data
ranges called buckets. Banding is also used for display purposes, where every other
row is a different color and the two colors alternate. Compare: consolidation.
base table
A fact table that stores data at the lowest level of dimensionality.
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block
A logical display element used to control the display of large reports in the limited
page and slide dimensions of Microsoft Word and PowerPoint. A block may consist
of multiple fetches. Blocks are defined by Microsoft Office product-specific
configuration settings.
break by
An attribute or hierarchy where calculations for an OLAP function restart. To break
by an attribute or hierarchy means to restart calculations that use OLAP, or Relative,
functions when the analytical engine reaches the next instance of the specified
attribute or hierarchy. Examples of OLAP functions include RunningStdevP, Rank,
NTile, and various expressions that calculate percent values. To break by an
attribute or hierarchy in an expression, you must set the BreakBy parameter.
business intelligence (BI) system
A system that facilitates the analysis of volumes of complex data by providing the
ability to view data from multiple perspectives.
C
cache
A special data store holding recently accessed information for quick future access.
This is normally done for frequently requested reports, whose execution is faster
because they need not run against the database. Results from the data warehouse
are stored separately and can be used by new job requests that require the same
data. In the MicroStrategy environment, when a user runs a report for the first time,
the job is submitted to the database for processing. However, if the results of that
report are cached, the results can be returned immediately without having to wait for
the database to process the job the next time the report is run.
caching
A special data storage method in which recently accessed values are stored for
quick future access. Caching is used primarily to improve report execution
performance.
calculated expression
A metric obtained dynamically, directly from metrics on a document dataset, by
using at least one of the metrics in the document. Calculated expressions allow you
to use simple arithmetic operators (+, -, *, /) to combine metrics from different
datasets in the document. See also: Derived metric.
catalog
A table that contains the names of all non-temporary tables in a data warehouse.
characteristic attribute
An attribute that is a parent of a child attribute, but not part of the "main" hierarchy
associated with the child attribute. For example, consider a hierarchy consisting of
Year, Month, Day. Day of Week is a parent of Day, and a characteristic attribute.
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child attribute
The lower-level attribute in an attribute relationship. See also: parent attribute or
relationship.
clustering
A way of using machine resources to provide an efficient and robust processing
environment for a Narrowcast Server system. A cluster consists of two or more
machines, each machine running at least one Narrowcast Server component.
These components are: MicroStrategy Logging Server, distribution manager (DM),
execution engine (EE).
column
(1) A one-dimensional vertical array of values in a table. (2) The set of fields of a
given name and datatype in all rows of a given table. (3) MicroStrategy object in the
schema layer that can represent one or more physical table columns or no columns.
component
A computing unit that provides a specific piece of the Narrowcast Server
functionality and interacts with other components. Examples are the Narrowcast
Administrator, execution engine, distribution manager, MicroStrategy Logging
Server, Object Repository, and Subscription Book Repository.
compound metric
A metric that cannot have a level placed on the entire metric, although it can be set
separately on each of the components.
conditional formatting
A method used to format specified controls in a document depending on predefined
criteria. It allows certain properties of controls, including sections, to be controlled by
data-driven conditions.
conditional metric
A metric containing filter criteria in its definition.
conditionality
Conditionality of a metric enables you to associate an existing filter object with the
metric so that only data that meets the filter conditions is included in the calculation.
configuration objects
A MicroStrategy object appearing in the system layer and usable across multiple
projects. Configuration objects include (among others) these object types: users,
database instances, database logins, schedules.
connection string
Stores the information required to connect to a database server. A connection string
usually includes a DSN and the user ID and password required to log in to the
database server. This information varies depending on the particular database
server.
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console user
A user who works with the Narrowcast Administrator console, managing
subscriptions, developing services, and administering the system, in contrast to an
end user. See also: end user.
consolidation
An object that can be placed on a template and is made up of an ordered collection
of elements called consolidation elements. Each element is a grouping of attribute
elements that accommodates inter-row arithmetic operations. Compare: custom
group.
content
The information in services, including all reports, miscellaneous text, and file
attachments that are accessible to the user. Content is dynamic in the sense that
personalization, error handling conditions, and device settings all influence the
overall content output and format of each service.
control
Any item in the document’s Layout area that you can select. This can be a text field,
line, rectangle, image, panel stack, selector, Grid/Graph, or HTML container. These
different kinds of controls are referred to as control types. See also: Grid/Graph,
HTML container, Panel stack, Selector, Text field.
control default
A set of options that can be set for each type of control and each section in a
document. You can set the defaults according to the control that is currently
selected; afterward, its format is applied to any object of the same type that you
create in the document.
custom group
An object that can be placed on a template and is made up of an ordered collection
of elements called custom group elements. Each element contains its own set of
filtering qualifications.
custom SQL
Additional SQL code independently created by the user for execution against the
data warehouse. MicroStrategy provides tools to write custom SQL, including
Freeform SQL and Query Builder.
D
dashboard
An interactive, visually intuitive display of data. A dashboard can summarize key
business indicators (KPIs) to provide a status check. Users can change how they
view the dashboard's data using interactive features, such as selectors, grouping,
widgets, and visualizations. Users can explore their data via multiple paths, using
text, data filtering, and layers of organization. See also: Visual Insight dashboard,
Dashboard-style document.
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dashboard-style document
A visually intuitive display of data that summarizes key business indicators for a
quick status check. A special type of document, dashboard-style documents usually
provide interactive features that let users change how they view the dashboard-style
document’s data.
Data Explorer
A portion of the interface used to browse through data contained in the warehouse.
Users can navigate through hierarchies of attributes that are defined by the
administrator to find the data they need.
data field
Dynamic text that is populated from a dataset with data that originated in the data
warehouse (or an Intelligence Server cache). A data field is only a reference to the
metric, attribute, consolidation, or custom group on a report. Data fields are
contained in text field controls on a document. See also: Auto text code, Text field.
data mart
A database, usually smaller than a data warehouse, designed to help managers
make strategic decisions about their business by focusing on a specific subject or
department.
data mart report
A special kind of report that saves its report data in a database rather than returning
those results to the user. Data mart reports either create a new table in the database
to store the report data or append the report data into an existing table.
data modeling
A method used to define and analyze data requirements needed to support the
business functions of an enterprise. These data requirements are recorded as a
conceptual data model with associated data definitions. Data modeling defines the
relationships between data elements and data structures.
data source
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, which refers more specifically to using a
database as your data source. Other data sources include text files, Excel files, and
MDX cube sources such as SAP BW, Microsoft Analysis Services, Oracle Essbase,
and IBM Cognos TM1.
data source name (DSN)
Provides connectivity to a database through an ODBC driver. A DSN generally
contains host machine name or IP address, instance name, database name,
directory, database driver, User ID, password, and other information. The exact
information included in the DSN varies by DBMS. Once you create a DSN for a
particular database, you can use it in an application to call information from the
database.
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data warehouse
A database, typically very large, containing the historical data of an enterprise. Used
for decision support or business intelligence, it organizes data and allows
coordinated updates and loads.
database connection
Stores all database-specific connection information such as DSN, driver mode and
SQL execution mode as well as connection caching information.
database instance
(1) Database server software running on a particular machine. Though it is
technically possible to have more than one instance running on a machine, there is
usually only one instance per machine. (2) The MicroStrategy object that represents
a logical definition of a data warehouse. It stores all information necessary for
MicroStrategy to access the data warehouse for a particular project.
database login
The login ID and password that MicroStrategy Intelligence Server uses to log in to a
particular database on behalf of a user. See also: login ID.
dataset
A MicroStrategy report used to define the data available on a Report Services
document.
Datasets
(1) A pane in the Document Editor that shows all objects (grouped by datasets) that
can be used in the document. (2) All objects that can be used in the document as
supplied by the datasets. Dataset objects are attributes, consolidations, custom
groups, and metrics.
decile(deciling)
The method by which a group is broken up into ten groups of equal elements. The
first decile consists of the top ten percent; the second, the 11th to 20th percent; the
third, the 21st to 30th percent; and so on. demographics, demographic data. Data
that locates, identifies, and describes a population and its properties; for example,
data describing the age groups of people living in certain geographical areas or
income categories. Other dimensions of demographic data include race, religion,
political preference, spending preferences, and family size.
demographics (demographic data)
Data that locates, identifies, and describes a population and its properties; for
example, data describing the age groups of people living in certain geographical
areas or income categories. Other dimensions of demographic data include race,
religion, political preference, spending preferences, and family size.
derived attribute
An attribute calculated from a mathematical operation on columns in a warehouse
table. For example, Age can be calculated from the expression [Current Date–Birth
Date]. See also: attribute.
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derived metric
A metric based on data already available in a report. It is calculated by the
Intelligence Server, not in the database. Use a derived metric to perform
calculations on report data after it has been returned from the database.
description column
Optional columns that contain text descriptions of attribute elements.
device
MicroStrategy object that represents message-receiving technology employed by
end users, such as a mobile phone or tablet. The device object specifies how a
publication should be formatted and transmitted to a specific device type. For
example, an Outlook 98 Device might specify that the first document in the
publication must be plain text and no longer than 128 characters. It might also
specify that the second document could be either plain text or HTML, and if it is
HTML, image references can be embedded. A device includes specifications for
both formatting and transmission. Devices are used in the definition of addresses to
specify what information transmitter will be used to transmit content to those
addresses and how that content should be formatted and packaged. See also:
address, content, information transmitter, administration object.
device ID
Numeric ID value that indicates the delivery method and device that a recipient
prefers. Devices are specified for subscriptions in dynamic subscription sets by
providing the device ID for each recipient. Device IDs are found under the properties
for each device within Narrowcast Administrator. For example, one subscription
might specify Outlook 2000, while another recipient might specify a mobile device.
One subscriber then only receives email content formatted for Outlook while the
other subscriber only receives mobile device content formatted for this phone.
dimension
An element or factor making up a complete entity or variable (a quantity that may
assume any one of a set of values).
directory server
A directory service provider running on a particular machine. Directory servers are
often part of email servers, and stores user names, addresses, and authentication
information. Unlike the Subscription Book, however, they are neither intended nor
well-suited to store and retrieve subscriptions. Directory servers usually enable
client connections through the lightweight directory access protocol (LDAP), and are
often used for centralized user authentication across many systems. See also:
Subscription Book, Lightweight Directory Access Protocol (LDAP).
display locale
Controls which object names are shown in Narrowcast Administrator. Since objects
can have multiple names to support end users in more than one language, one
name must be chosen for display in Narrowcast Administrator. The display locale
serves this role. The locale selected for this purpose should be the one that the
Narrowcast Administrator needs to use most frequently when defining objects. See
also: locale, execution locale, selection locale, system locale, user locale.
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distribution manager (DM)
(1) Delivery Engine object that receives service execution triggers, distributes
service data to the execution engines for processing, designates failover execution
engines, and tracks the status of other system components. If the primary
distribution manager fails, one of the backup distribution managers becomes the
primary and takes over processing where the failed component stopped. (2) Piece
of software or component used to instantiate a distribution manager object. (3)
Machine being used to instantiate the distribution manager object.
distribution set
A schedule and subscription set pair, created during service definition. Specifies
when and to whom Narrowcast Server will send a service. See also: schedule,
subscription set.
document
A display representing data coming from one or more reports, as well as positioning
and formatting information. A Report Services document is used to format data from
multiple reports in a single display of presentation quality.
document (Narrowcast)
A Narrowcast document contains the static information from the document template
and the dynamic content from document elements, and is ready to be transmitted by
Narrowcast Server. Each document has a specific type, such as HTML, plain text,
Excel, or derived from an imported file.
document (Report Services)
A type of data display that shows data usually coming from multiple reports. A
Report Services document formats data from multiple reports in a single display of
presentation quality. A dashboard is a type of Report Services document, often
including interactive components.
document element
Part of a Narrowcast document containing dynamic content that is generated when
that document is executed as part of a service. Consists of at least one information
object, plus error-handling instructions. Might include an XSL stylesheet selection,
depending on the type of information object chosen.
document formatter
Transforms the raw data of the information objects and the structure of the
Narrowcast document templates into a complete, formatted Narrowcast document.
The operations of document formatters include such tasks as XSL processing and
transformation, XML merging, character replacement, and string padding.
Formatting rules might account for device characteristics, locale definition, and other
recipient-specific formatting control information.
document formatting module (DFM)
A piece of software, or a component, that performs the work required for turning
Narrowcast document templates and information objects into Narrowcast
documents. The formatting process can use device characteristics, locale definition,
and other recipient-specific control information.
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document template
Provides the structure and layout for a Narrowcast document. For example, an
Excel document template includes the basic workbook structure, any predefined
macros, and static worksheet content.
dossier
An interactive, visually intuitive display of data. A dossier can summarize key
business indicators (KPIs) to provide a status check. Users can change how they
view the dossier's data using interactive features, such as selectors, grouping,
widgets, and visualizations. Users can explore their data via multiple paths, using
text, data filtering, and layers of organization. See also: Visual Insight dashboard,
Dashboard-style document.
drill
A method of obtaining supplementary information after a report has been executed.
The new data is retrieved by re-querying the Intelligent Cube or database at a
different attribute or fact level.
drill path (attribute drill path)
In MicroStrategy, a path that determines which attributes are presented to an
interface; typically a project defines drill paths from parent attributes to their children.
dynamic content
Document content that is dynamically retrieved at service execution time and that
can be personalized for each message recipient. Dynamic content is created using
content information objects. This content changes depending on the results returned
by the information object and can return different results for different subscribers.
For example, a weekly report returns different information from week to the next,
and each subscriber might request different content in his report. Examples include
a MicroStrategy report, a Web query, a SQL query, or content from an external
system. See also: static content.
dynamic subscription set
A subscription object containing at least one piece of subscription information
retrieved from an information object instead of from the Subscription Book
Repository. Useful for changing or alert-driven subscription sets. Some dynamic
subscription sets acquire all their information from a single information object.
Others retrieve only the Subscription ID from the information object while the rest of
the subscription information is acquired from the Subscription Book Repository. Still
others combine both methods. See also: segmentation information object,
Subscription ID, subscription information object, subscription set.
E
editor
A dialog used to create and edit MicroStrategy Objects. There is a Filter Editor,
Template Editor, Attribute Editor, Metric Editor, Report Editor, and so on.
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end user
A subscriber, the person receiving messages from Narrowcast Server service, in
contrast to a console user. See also: console user.
entity relationship diagram (ERD)
A diagram that 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.
entry level
The lowest level set of attributes at which a fact is available for analysis.
ETL
Short for extraction, transformation, and loading. (1) The process used to populate a
data warehouse from disparate existing database systems. (2) Third-party software
used to facilitate such a process.
execution engine (EE)
(1) Delivery Engine object that controls the execution of narrowcast messages,
called services. It receives service segments from the distribution manager, then
determines the content and subscriber information with input from other
components, including the Subscription Book Module and information source
modules. (2) A piece of software or component that is used to instantiate an
execution engine object. (3) A machine being used to instantiate an execution
engine object.
execution locale
Determines how content is generated for the users who receive it. When a
document is created, the application designer can specify that the document should
be executed in a specific execution locale. The definition of the locale then controls
how information should be gathered, formatted, packaged and delivered for this
document. For example, an application designer might define a French and a
German locale to gather information in the appropriate languages. If one of these
locales is chosen as the execution locale for a document, all dynamic content in this
document is retrieved using the corresponding language. See also: locale, display
locale, selection locale, system locale, user locale.
expression
Formulas built from functions, attributes, facts, metrics, and consolidations that can
be used to define attribute forms, fact calculations, metrics, or filters.
F
fact
(1) A measurement value, often numeric and typically aggregatable, stored in a data
warehouse. (2) A schema object representing a column in a data warehouse table
and containing basic or aggregated numbers—usually prices, or sales in dollars, or
inventory quantities in counts. See also: metric.
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fact table
A database table containing numeric data that may be aggregated along one or
more dimensions. Fact tables may contain atomic or summarized data. Compare:
base table.
fetch
The amount of report data retrieved from one call to MicroStrategy Web Services.
Fetches are used to control the amount of network traffic between MicroStrategy
Office and MicroStrategy Web Services and the amount of memory used by
MicroStrategy Intelligence Server. When you execute a report or document into a
Microsoft product, the Execution Status dialog box displays the progress of each
fetch. Fetch size is controlled by Microsoft Office product-specific configuration
settings. Maximum fetch size is governed by an Intelligence Server setting in
MicroStrategy Developer.
filter
A MicroStrategy object that specifies the conditions that the data must meet to be
included in the report results. Using a filter on a report narrows the data to consider
only the information that is relevant to answer your business question, since a report
queries the database against all the data stored in the data warehouse. A filter is
composed of at least one qualification, which is the actual condition that must be met
for the data to be included on a report. Multiple qualifications in a single filter are
combined using logical operators. Examples include "Region = Northeast" or
"Revenue > $1 million". A filter is normally implemented in the SQL WHERE clause.
Flash-enabled document
A Report Services document in which Flash Mode is selected as an available display
mode in the Document Properties dialog box.
flattened
A report display type in which the results of a report are displayed with all attributes
and metrics flattened side-by-side on one axis. Also, any subtotals within the report
are not displayed. It is convenient to execute your report or HTML document in this
display type if you intend to use Excel’s drop-down lists to filter, hide/show data, and
more.
folder
A MicroStrategy object used for grouping and storing in a single place a set of
objects that are similar, such as filters, templates, and reports.
folder list
A portion of the interface that lists all the folders found in the project in a hierarchical
fashion. It helps a user to visualize and browse through a MicroStrategy project.
form
One of several columns that are different representations of the same thing, as ID,
Name, Long Description, Abbreviation.
function plug-in
Allows user-defined functions to be used by the MicroStrategy Analytical Engine.
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G
governing parameters
Designed to keep the execution profile of a system within acceptable limits. Protects
the system from overconsumption of resources such as memory, disk space,
network capacity, and CPU cycles.
graph
A graphical image display of data. Sometimes referred to as a chart. See also
report.
graph analytic
An analytic showing data as points, lines, or bars, arranged according to axes based
on the chosen metrics. Although not all analytics can be displayed in every graph
type available, many analytics can be viewed in several ways. Choices for graph
display include bars, lines, area graphs, and three-dimensional graphs.
graph view
Report viewing mode that displays results as a graphical chart.
grid
A spreadsheet-style two-dimensional display of data. See also report.
grid analytic
An analytic consisting of rows and columns. Each row or column has a heading, and
each heading represents a prompt from the analytic. Grid analytics can be modified
easily, by drilling, moving columns, converting columns to rows, sorting, and using
page-by to display subsets of the analytic data as separate pages.
grid view
Report viewing mode that displays result data in a tabular format.
Grid/Graph
A control placed in a document that displays information in the same way a
MicroStrategy report does.
grouping
A way to create a hierarchical structure for a document.
H
hierarchy
A set of attributes defining a meaningful path for element browsing or drilling. The
order of the attributes is typically—though not always—defined such that a higher
attribute has a one-to-many relationship with its child attributes.
hint
A comment that passes instructions to a database optimizer about choosing an
execution plan for a given SQL statement. In MicroStrategy, a hint can be defined in
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VLDB properties to appear within a MicroStrategy-issued SQL statement.
History List
A folder where users can retrieve the results of previously executed or scheduled
reports and documents.
HTML container
A control that either displays real-time information from the web or displays
formatted HTML.
HTML document
(1) A compound report displaying multiple grids and graphs. (2) The MicroStrategy
object that supports such a report.
I
ID column
A column that contains attribute element identification codes. All attributes must
have an ID column.
imported file
A file imported from outside of Narrowcast Server and stored in its original format.
For example, a text file contains only plain text and an HTML file contains only
HTML. Storing information in the file format preserves the integrity of the original
data since files cannot be modified with Narrowcast Server. Files cannot be directly
included in publications; they must first be associated with imported documents,
which serve as containers to allow them to be attached to publications. Imported
files can also be used as stylesheets or templates for documents.
incremental fetch
A feature that returns a large set of information, such as subscriptions, to the
console in numerous small pieces as those pieces are needed. This ensures that
network traffic and client processing is kept to a minimum.
information delivery platform
Server that, whenever certain conditions are in effect, acquires information from a
variety of sources, personalizes that information, formats it, and transmits it through
a variety of technologies.
information object
Application object containing a set of instructions that specify how to get data from a
particular type of information source. Defined by using an information source
module, it is set up when a service is built and executed when the service that uses it
is run. Plays one of three roles: content, subscription, or segmentation. See also:
information object role, information object type, information source,information
source module (ISM).
information object role
Indicates how the information supplied by the information object is used by
Narrowcast Server. Three roles are available: Content ( Supplies information for the
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document), Subscription ( Provides a list of subscribers and related subscription
information for dynamic subscription sets), Segment (Specifies the first and last
subscriptions for each segment of the subscription set). See also: information object.
information object type
Indicates the format of the data returned by the information object and how
Narrowcast Server can use this data. Three types are available: Text (Returns a
plaintext string that is inserted directly into a document's content), XML (Returns an
XML string that must be combined with an XSL stylesheet to produce content
appropriate for each type of document), Image (Returns an image in JPG format
that is inserted directly into the document's content). See also: information object.
information source
Contains information about how to connect to a specific source of content using an
information source module. The Delivery Engine requests and acquires information
from information sources. An information source uses personalization objects for
personalization. See also: information source module (ISM), personalization object.
information source module (ISM)
An executable process that receives requests for information and returns that
information as well-defined data. It defines and processes information objects. One
ISM can be used to load various information sources. For example, an ISM can be
set up to access MicroStrategy Developer projects and can be used for any
MicroStrategy Developer projects that you want to use as an information source.
See also: information object, information source.
information transmission module (ITM)
An executable process or component designed to support a particular delivery
capability. For example, MicroStrategy Narrowcast Server provides the email
(SMTP) Module, which is used for the email (SMTP) Information Transmitter. The
ITM indicates what documents and document types it supports, as well as the
arrangement of these documents. Also, the ITM provides a user interface for
defining device characteristics. Those characteristics are stored in the Object
Repository and retrieved by the information transmission module to control
packaging and delivery. See also: device, document (Narrowcast), information
transmitter, publication.
information transmitter
Administration object that delivers formatted content, in the form of publications, to
end user devices. An information transmitter might serve the purpose of sending
email via SMTP or mobile device messages using an SMS gateway. In general, an
information transmitter supports one type of transmission technology, such as
SMTP, FTP, HTTP, or ODBC, but this is not a requirement. Information transmitters
depend on information transmission modules. See also: device, document
(Narrowcast), information transmission module (ITM), publication.
installation log file
The MicroStrategy setup program generates a log file in text format. This log file
contains records of all actions performed by the setup program and by other
executable files related to installation.
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Intelligent Cube
A copy of the report data saved in memory and used for manipulation of the view
definition. This division allows multiple reports with different views to share a
common data definition.
J
Java database connectivity
A Java API that enables Java programs to execute SQL statements. This allows
Java programs to interact with any SQL-compliant database. Since nearly all
relational database management systems (DBMSs) support SQL, and because
Java itself runs on most platforms, JDBC makes it possible to write a single
database application that can run on different platforms and interact with different
DBMSs. JDBC is similar to ODBC, but is designed specifically for Java programs,
whereas ODBC is language-independent.
join
A SQL operation that combines data from multiple tables into a single result table.
K
KPI (key performance indicator)
An indicator gauging how well a company progresses in numerous areas such as
finance, customer service, and product availability and distribution.
L
Layout area
The middle panel of the Document Editor in which you place data or other controls to
determine the appearance of the document when it is viewed as a PDF.
level
(1) In a data warehouse, facts are said to be stored at a particular level defined by
the attribute IDs present in the fact table. For example, if a fact table has a Date
column, an Item_ID column, and a fact column, that fact is stored at the Date/Item
level. (2) With regard to metric calculation, the level is the level of calculation for the
metric.
Lightweight Directory Access Protocol (LDAP)
An open standard that client computers use to communicate with directory service
providers. Client machines connect to a particular logical directory on a particular
physical directory server. See also: directory server.
Lightweight Directory Interchange Format (LDIF)
File format that is exported from LDAP directory services.
link
A connection from a document to another document or a report. A link lets an
analyst execute another document or report (the target) from a document (the
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source), and to pass parameters to answer any prompts that are in the target.
load balancing
A strategy aimed at achieving even distribution of MicroStrategy Web Universal user
sessions across MicroStrategy Intelligence Servers. MicroStrategy achieves fourtier load balancing by incorporating load balancers into MicroStrategy Web
Universal.
locale
Specifies what regional characteristics to apply to data and formatting, including
number format, date format, time format, and character sets. Your system might
support multiple locales. See also: display locale, execution locale, selection locale,
system locale, user locale.
Locale ID
Numeric ID value used to indicate the locale that a recipient prefers. Locales are
specified for subscriptions in dynamic subscription sets by providing the Locale ID.
Locale IDs are found under the properties for each locale within Narrowcast
Administrator. For example, one subscription might specify French, while another
recipient might specify German. One subscriber then only receives French content
while the other subscriber only receives German content.
logical data model
A graphical representation of data that is arranged logically for the general user, as
opposed to the physical data model or warehouse schema, which arranges data for
efficient database use.
login ID
Login identifier, typically supplied with a password.
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.
M
managed object
A schema object unrelated to the project schema, which is created by the system
and stored in a separate system folder. Managed objects are used to map data to
attributes, metrics, hierarchies and other schema objects for Freeform SQL, Query
Builder, and MDX cube reports.
manual fetch
A type of incremental report result fetching in which you are asked to confirm each
fetch. For example, when you execute a report, you are prompted to confirm that
each fetch, or section of report results, is displayed.
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many-to-many relationship
An attribute relationship in which multiple elements of a parent attribute can relate to
multiple elements of a child attribute, and vice versa.
many-to-one relationship
An attribute relationship in which (1) multiple elements of a parent attribute relate to
only one element of a child attribute, and (2) every element of the child attribute can
relate to multiple elements of the parent.
MDX cube
An MDX cube is a collection or set of data retrieved from an MDX cube source,
which is imported into MicroStrategy and mapped to various objects to allow query,
reporting, and analysis on the data.
MDX cube report
The central focus for MicroStrategy users to query, analyze, and visually present
data from MDX cube sources in a manner that answers and evaluates their
business questions. MDX cube reports provide the same data display and analysis
functionality as standard MicroStrategy reports, but rather than reporting on data
from a relational data warehouse, MDX cube reports report on data from MDX cube
sources.
MDX cube source
When integrated with MicroStrategy, the third-party tools SAP BW, Microsoft
Analysis Services, Oracle Essbase, and IBM Cognos TM1 are referred to as MDX
cube sources. You can import and map data from these different MDX cube sources
in MicroStrategy to query, report on, and analyze data with MicroStrategy.
MicroStrategy can integrate with MDX cube source data as well as access data from
a relational database concurrently.
messaging application program interface (MAPI)
Allows Windows applications to send email messages through external email
programs. Designed primarily to connect client applications such as Microsoft
Outlook to mail servers such as Microsoft Exchange Server. Not intended for serverto-server communications.
metadata
A repository whose data associates the tables and columns of a data warehouse
with user-defined attributes and facts to enable the mapping of the business view,
terms, and needs to the underlying database structure. Metadata can reside on the
same server as the data warehouse or on a different database server. It can even be
held in a different RDBMS.
metadata (or metadata repository)
A repository whose data associates the tables and columns of a data warehouse
with user-defined attributes and facts to enable the mapping of the business view,
terms, and needs to the underlying database structure. Metadata can reside on the
same server as the data warehouse or on a different database server. It can even be
held in a different RDBMS.
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metric
(1) A business calculation defined by an expression built with functions, facts,
attributes, or other metrics. For example: Sum(dollar_sales) or [Sales] - [Cost]. (2)
The MicroStrategy object that contains the metric definition. It represents a business
measure or key performance indicator. See also: fact.
MicroStrategy Analytics Module
A MicroStrategy project with prepackaged metadata, including best practices
reports, scorecards, and dashboards, key performance indicators, attributes,
business metrics, filters, and custom groups; default physical and logical data
models to allow the module to work with your physical schemas and data model or
with the module’s packaged data warehouse schema; and a reference guide for the
Analysis Module’s data model, the analysis area, metadata object definitions, data
dictionary, and individual report use scenarios.
MicroStrategy Intelligence Server
Core of the MicroStrategy architecture, MicroStrategy Intelligence Server manages
and organizes users, projects, and database connections; coordinates, prioritizes,
and executes all user requests; and allocates the resources necessary to complete
them. It tracks schedules, manages security, and provides the ability to monitor and
analyze the daily activity of the entire decision support environment.
MicroStrategy Logging Client
Service that receives logging messages from Narrowcast Server components and
relays them to the MicroStrategy Logging Server. Logging messages include
segment status information, statistics information, and error event notification. See
also: MicroStrategy Logging Server.
MicroStrategy Logging Server
Server that collects logging messages from MicroStrategy Logging Clients and
distributes them to consumers. Logging messages include segment status
information, statistics information, and error event notification. See also:
MicroStrategy Logging Client.
multidimensional analysis
A form of analysis of the data in a data warehouse that includes many relationships,
each representing a dimension. For example, a retail analysis may seek to
understand the relationships among sales by region, by quarter, by demographic
distribution (income, education level, gender), and by product. Multidimensional
analysis provides results for these complex relationships.
multipart MIME
Part of the MIME specification, which includes more than one body part in a body
section within a MIME message. This is typically used to enable the inclusion of
separate text and HTML message bodies, embedded images, and other body parts
within an email message. See also: multipurpose Internet mail extensions (MIME).
multipurpose Internet mail extensions (MIME)
The specification as defined by RFC 1521 (maintained by the Internet Engineering
Task Force at http://www.ietf.org/) for encoding message contents, attached files,
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embedded images, and other embedded files into a single (typically 7-bit) data
string. Many modern email clients support MIME. Because the MIME specification
includes encoding of message contents, it allows for messages that use character
sets other than US-ASCII. See also: multipart MIME.
N
Narrowcast Administrator account
An object that allows a user to log in to MicroStrategy Narrowcast Administrator. It is
created within Narrowcast Administrator, and its definition is stored in the Object
Repository of a system. Each Narrowcast Administrator account is based on a
specific Windows account. When a new Narrowcast Server system is created, two
Narrowcast Administrator accounts are automatically added to it, one of which is the
local Administrator Windows account for the machine that was used to create the
system. Narrowcast Administrator accounts can have various privileges, which
control what Narrowcast Administrator users can do. These privileges include:
Service design, Application administration, System administration, Subscription
administration.
Narrowcast Server system
A collection of machines, software components, and objects that collectively provide
the ability to process and deliver narrowcast messages to recipients. The objects
that make up a system are divided into two categories, administration objects and
application objects. See also: administration object, application object, clustering,
component.
O
object
Conceptually, an object is the highest grouping level of information about one
concept, used by the user to achieve the goal of specified data analysis. More
concretely, an object is any item that can be selected and manipulated, including
folders, reports, facts, metrics, and so on.
object template
A MicroStrategy object that allows you to start with a predefined structure when
creating a new object. You can use object templates for many MicroStrategy
objects, including metrics, documents, reports, and report templates.
ODBC (open database connectivity)
An open standard with which client computers can communicate with relational
database servers. Client machines make a connection to a particular logical
database, on a particular physical database server, using a particular ODBC driver.
ODBC driver
Software interface to an ODBC service provider. See also: open database
connectivity (ODBC).
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ODBC driver manager
Coordinates communication between a client application and database server. The
client application tells the driver manager that it needs to connect using a particular
connection string. The DSN found in this connection string provides the driver
manager with the type of database server to which the application needs access.
From this information, the driver manager decides what driver to use and initiates
the communication.
one-to-many relationship
An attribute relationship in which every element of a parent attribute can relate 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.
one-to-one relationship
An attribute relationship in which every element of the parent attribute relates to
exactly one element of the child attribute, and vice versa.
online analytical processing
In general, a system with analytical processing that involves activities such as
manipulating transaction records to calculate sales trends, growth patterns, percent
to total contributions, trend reporting, and profit analysis.
operational data store
A database that typically stores transactional data generated by and used in the
conducting of business operations. The data can be used as a trigger condition or as
content.
outline mode
Report viewing mode that creates indented, collapsible groupings of related
elements to make reports neater and easier to read.
P
page
An amount of data that is analogous to a page in a page-by report. Each page of a
page-by report represents one combination of elements from each attribute on the
page-by axis. For example, one page may contain data related to 2006 as well as
the Southeast region. A report with no attributes on the page-by axis is considered
to have one page. When you execute a report or document into a Microsoft product,
the Execution Status dialog box displays the progress of each page.
page-by
Segmenting data in a grid report by placing available attributes, consolidations, and
metrics on a third axis called the Page axis. Since a grid is two-dimensional, only a
slice of the cube can be seen at any one time. The slice is characterized by the
choice of elements on the Page axis. By varying the selection of elements, the user
can page through the cube.
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panel
A way of grouping data in a document so that users can navigate subsets of data as
if the subsets were pages in a smaller document. Each “page”, or layer of data, is a
panel; a group of panels is called a panel stack.
panel stack
The holder for a collection of panels, or layers of data, in a document. A user can
navigate or flip through the panels in a panel stack; only one panel is displayed at a
time.
parent attribute
The higher-level attribute in an attribute relationship with one or more children. See
also: child attribute or relationship.
partition
A relational database table broken down into smaller component tables. This can be
done at the database level or at the application level. See the MicroStrategy System
Administration Guide for more information.
partition mapping
The division of large logical tables into smaller physical tables based on a definable
data level, such as month or department. Partitions minimize 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.
partition mapping table
A warehouse table that contains information used to identify the partitioned base
tables as part of a logical whole. (A partitioned base table 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.) Also referred to as a PMT.
persistence
A default behavior in MicroStrategy Office ensuring that every report and document
you run in an Excel workbook, PowerPoint presentation, or Word document retains
a link to MicroStrategy Intelligence Server. This ensures that you and other users
can refresh the reports and documents to retrieve the latest data from the data
warehouse or other data sources. Every report or document executed also contains
properties that are persisted; these properties determine how the report or
document is displayed and can be modified by users once it is refreshed.
personalization
The process that allows each subscriber to receive data that has been filtered and
formatted specifically for him. Narrowcast Server uses locale, subscription, and
personalization objects to personalize services. See also: locale, personalization
object, subscription.
personalization object
An object handled by information sources for personalization. The three types are:
authentication, preference and security. The combination of the authentication,
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preference, and security objects control the data that is returned by an information
object. See also:authentication object, information object, information source,
preference object, question object, security object.
personalization set
A combination of locale, authentication, security, and preference objects to achieve
personalized information, or information filtered and formatted specifically for a
subscriber.
personalized page execution
In the context of the MicroStrategy Information Source Module, each page of a
report is used as a result for one or more subscribers. See also: page-by,
personalization set.
personalized report execution
In the context of the Information Source Module, each result is derived from the
separate execution of a report. Answers to prompts and MicroStrategy users both
impact personalization in this execution mode. See also: personalization set.
physical address
The address location used to locate recipients and deliver messages. For email
delivery, this is a standard SMTP address in the form recipient@domain.xxx. No
physical address is required for delivery to the Subscription Portal. The physical
address required by other information transmitters depends on the individual
information transmitter.
physical warehouse schema
A detailed graphic representation of your business data as it is stored in the data
warehouse. It organizes the logical data model in a method that make sense from a
database perspective.
PIN
The personal identification number required by some delivery methods. A PIN is not
used for email, mobile device, or Subscription Portal delivery. The use of a PIN by
other information transmitters depends on the individual information transmitter.
plug-in
An additional processing component integrated with MicroStrategy products. For
example, advanced statistical and financial functions can be added as additional
processing components.
plugin
An application that can easily be installed and executed by the MicroStrategy
Intelligence Server in one of several identical interfaces. For example, advanced
statistical and financial functions can be added as additional processing
components.
port number
The port number is how a server process identifies itself on the machine on which it
is running. For example, when the Intelligence Server machine receives a network
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call from a client (Developer, Web Universal, Narrowcast Server, Command
Manager, and so on), it knows to forward those calls to the Intelligence Server port
number that is specified in the call.
portability
The ability of an analytical application to be integrated into an existing data
warehouse. To port the Analysis Module, you “map” the module to the physical
schema of an existing data warehouse.
portal
A site that offers a centralized access point for finding and managing information via
a variety of different services. It offers a broad array of resources and services, such
as email, discussion forums, search engines, and other online services. A portal is
accessible through the use of a web browser. See also: Subscription Portal.
preference object
Type of personalization object that uses the answers to question objects to specify
the information that the user wants. At run-time, the preference object is applied to
information objects to personalize them. Preferences are usually controlled by the
user, but can be set by the subscription administrator. For an Information Source,
preference objects are prompt answers. See also: personalization object, question
object.
prefix
A prefix is stored in the project metadata associated with a table or tables and is
used by the Engine to generate SQL. Also, the Catalog Server uses it to obtain table
sample values and row counts. In most cases, it should match the name space field
since it is used to qualify on a specific table belonging to a certain owner or name
space. Prefixes can be defined and modified from the Warehouse Catalog interface.
preview fetch
A type of incremental report result fetching in which you can see a preview of the
report data as it runs. You specify how many rows of the report you want to preview
at a time, and fetching automatically stops after one fetch.
primary key
In a relational database, the set of columns required to uniquely identify a record in a
table.
privilege
Assigned to users, a privilege defines the functionality available to a user, for
example, which objects a given user can create and which applications and editors
he can use.
process
An executing application comprising one or more threads. Processes use temporary
private address spaces and control operating system resources such as files,
dynamic memory allocations, pipes, and synchronization objects.
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production metadata
The repository you create during the configuration portion of the installation process,
and which works with your data warehouse and serves as your working metadata
repository.
productivity analysis
A process that measures company productivity and identifies ways to increase it.
project
(1) The highest-level intersection of a data warehouse, metadata repository, and
user community, containing reports, filters, metrics, and functions. (2) An object
containing the definition of a project, as defined in [1]. The project object is specified
when requesting the establishment of a session.
project designer
The user category of one who creates projects and all of the schema objects (facts,
attributes, hierarchies) for a project. A project designer is thoroughly familiar with the
data model and schema object editors.
project source
Defines a connection to the metadata database and is used by various
MicroStrategy components to access projects. A direct project source is a two-tier
connection directly to a metadata repository. A server project source is a three-tier
connection to a MicroStrategy Intelligence Server. One project source can contain
many projects and the administration tools found at the project source level are used
to monitor and administer all projects in the project source.
prompt
MicroStrategy object in the report definition that is incomplete by design. The user is
asked during the resolution phase of report execution to provide an answer that
completes the information. A typical example with a filter is choosing a specific
attribute on which to qualify.
Property List
The list of settings used to specify the appearance or any other characteristic of a
control on a document.
publication
An ordered collection of documents that completely defines the content of a service
for a specific set of devices. Each publication is used for exactly one locale and one
information transmission module. A publication specifies: An information transmitter,
At least one device or A set of documents. See also: device, document
(Narrowcast), information transmitter.
Q
qualification
The actual condition that must be met for data to be included on a report. Examples
include “Region = Northeast” or “Revenue &gt; $1 million”. Qualifications are used in
filters and custom groups. You can create multiple qualifications for a single filter or
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custom group, and then set how to combine the qualifications using the logical
operators AND, AND NOT, OR, and OR NOT.
query
A request for data from a database or data warehouse. A report is a database
query.
Query Engine
The MicroStrategy component responsible for submitting SQL code to the
database.
question object
Type of personalization object that specifies the questions to ask the user to
determine the user’s preferences. Answers are used as preference objects. For a
MicroStrategy Information Source, a question object is defined by choosing a report,
and the questions to be asked depend on the definition of that report. In
personalized page execution mode, the user is asked what page to display, using
the page-by functionality. In personalized report execution mode, the questions
include all prompt objects defined on the selected report except the security object
prompt. See also: page-by, personalization object, preference object.
Quick Grid
A report display type in MicroStrategy Office in which the report is run as a CSV and
bulk-loaded into Excel in one fetch. Although this results in a fast report execution
time, formatting from the report definition, such as fonts, colors, and thresholds is not
applied. You can apply formatting to Quick Grids using Microsoft Excel’s
AutoFormats.
R
ranking
A type of OLAP function that returns the rank of a value in a group of values. Rows
with equal values with respect to the ordering are assigned the same rank.
relate table
A table containing the ID columns of two or more attributes, thus defining
associations between them.
relationship
An association specifying the nature of the connection between one attribute (the
parent) and one or more other attributes (the children). See also: child attribute or
parent attribute.
report
The central focus of any decision support investigation, a report allows users to
query for data, analyze that data, and then present it in a visually pleasing manner.
report designer
The user category of one who creates all application objects such as grid and graph
reports, filters, templates, documents, consolidations, and custom groups. The
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report designer understands all of the business intelligence capabilities of the
system.
report resolution
The process of filling placeholders such as prompts with information determined at
run time.
report template
A MicroStrategy object that allows you to define the layout of general categories of
information in a report. In a report template, you specify the information that you
want to retrieve from your data source, and the way that you want the data to be
displayed in Grid view. A report template does not include filter information. Report
templates are often referred to as just as templates.
retention(employee)
The process of maintaining or securing employee loyalty to minimize loss of key
talent.
role
A feature of the security subsystem that defines which objects a given user can
create and which operations he can perform. The different Narrowcast Server roles
are: System administrator (who manages the entire system), Application
administrator (who administers application objects only), Application designer (who
develops application objects only and cannot modify objects owned by other users),
Subscription administrator (who manages the Subscription Book), Portal
administrator (who configures and manages Subscription Portals).
S
schedule
Sets the time or frequency that a service is executed. A schedule represents a
recurrence pattern, not a fixed date. It is defined relative to time zones to account for
daylight savings, date boundaries, and other time zone-specific issues. To allow
subscribers globally to receive services at specific local times, a service can contain
more than one schedule.
schema
(1) The set of tables in a data warehouse associated with a logical data model. The
attribute and fact columns in those tables are considered part of the schema itself.
(2) The layout or structure of a database system. In relational databases, the
schema defines the tables, the fields in each table, and the relationships between
fields and tables.
schema object
MicroStrategy object created, usually by a project designer, that relates the
information in the logical data model and physical warehouse schema to the
MicroStrategy environment. These objects are developed in MicroStrategy
Architect, which can be accessed from MicroStrategy Developer. Schema objects
directly reflect the warehouse structure and include attributes, facts, functions,
hierarchies, operators, partition mappings, tables, and transformations.
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scorecard
A type of tally sheet displaying a company's performance using key performance
indicators (KPIs) that gauge how well a company progresses in areas such as
finance, customer service, and product availability and distribution. See also KPI
(key performance indicator).
security filter
A qualification associated with a user that is applied to all queries executed by that
user.
security object
Type of personalization object that specifies what information the user should have
access to. Security filtering criteria applied to an information source during
subscriber interaction. For a MicroStrategy Information Source, it is a response to a
prompt in the form of a single attribute element. Each user can have one security
object per information source. See also: personalization object, security object
prompt.
security object prompt
Specifies which attribute in the project corresponds to a user or subscriber. The
answer to this prompt is used as a security object in MicroStrategy. See also:
prompt, security object.
security role
In a MicroStrategy security model, the set of privileges that a user can have.
segment
A group of subscriptions within a subscription set. Subscription sets are divided into
multiple pieces, or segments, so that the work required to execute a service for all
subscriptions can be distributed across multiple systems to allow parallel work
processing. The size of the segment is part of the service definition and controls the
work packages that are sent to each execution engine.
segmentation
The task of dividing the subscriptions within a subscription set into equal-sized
groups or segments. Segmentation ensures that all subscription information objects
and content information objects return only information for subscriptions in the
current segment. Subscription sets are always segmented using the subscription ID,
and the boundaries of segments are specified as subscription ID values. For the
MicroStrategy Information Source, segmentation can be performed automatically by
Narrowcast Server, or the application designer can control how the subscription set
is segmented and how subscription information objects and content information
objects constrain the information they return.
segmentation information object
An information object that returns the segment boundary values for a subscription
set. Boundary values determine the size of the segment and are always given in the
form of subscription ID values corresponding to the first subscription ID in each
segment. Segment information objects are used only for dynamic subscription sets.
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segmentation prompt
A prompt that controls the information returned in a report used as a content
information object or subscription information object. Segmentation can be
performed automatically by Narrowcast Server, or prompts can be added to content
information objects and subscription information objects manually. This process
limits the number of subscriptions and content pages returned to only provide
information for subscriptions in the current segment.
selection locale
Determines which users should receive which content. When a user is created, the
subscription administrator (using Narrowcast Administrator) or the user (using the
Subscription Portal) selects a locale for which the user will receive content. When a
publication is created, the application designer chooses a selection locale that
determines which users receive the content in a publication. If a user chooses
Locale A, the application designer must choose Locale A as the selection locale for a
publication for the user to receive this publication. See also: locale, display locale,
execution locale, system locale, user locale.
selector
A type of control in a document that allows a user to: Flip through the panels in a
panel stack, to see different predefined layers of data, or “pages”, in the same
document. Display different attribute elements or metrics in a Grid/Graph.
SequeLink
Third-party (non-MicroStrategy) software that configures and manages data access
across multiple data stores, operating systems, and deployment options.
SequeLink machine
The machine where SequeLink is installed. This machine can be independent from
the rest of the Subscription Portal.
SequeLink services
SequeLink creates two NT services: SLAgent 54, which is the Administrator, and
SLSocket54, which is the Server.
server definition
A MicroStrategy object stored in the metadata containing information about the
configuration of an Intelligence Server.
server instance
The combination of an Intelligence Server running with a particular server definition.
service
An object that provides all the information needed for the Delivery Engine to
correctly generate messages. That information includes the delivery conditions,
content, personalization rules, and subscriptions for sending messages for a
particular purpose. For example, one service delivers a daily message containing a
stock portfolio update, while another delivers alerts to mobile devices when the
value of a stock changes significantly. A service comprises at least one schedulesubscription pair and a set of publications.
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service designer
Narrowcast Administrator user role. This role is for a Narrowcast Administrator
console user who develops and tests services, which include documents,
publications, schedules, and subscription sets. Configures and manages
Subscription Portals that allow end users to subscribe to a variety of Narrowcast
services via the web. Configures data sources, content, and portal layout. Publishes
services and device types, and selects default devices for Subscription Portals.
Specifies information source properties and default site preferences. Installs,
configures, and administers the development environment. Administers subscribers
and subscriptions for development and testing purposes. See also: application
administrator.
service provider interface (SPI)
The specification for a set of related functions that can be implemented by a
software developer to augment or enhance the capabilities of a software product or
platform. This allows a developer to enable his software to be called by an existing
piece of software. See also: application program interface (API), plug-in.
service queue
In Narrowcast Administrator, a visual display of upcoming services to be processed.
simple metric
A type of metric that can stand alone or be used as a building block for compound
metrics. Simple metrics always contain at least one aggregate function, such as sum
or average, applied to a fact, attribute, or another metric. The entire metric can only
contain one level.
slice
One page of content in a multi-page report. Narrowcast Server divides a single
multi-page report into multiple individual pages of content that are used as
personalized content for individual subscriptions. slicing attribute. A slicing attribute
is the attribute used to divide multi-page reports executed using personalized page
execution mode into multiple individual pages of content. The first attribute on the
page axis is used as the slicing attribute.
smart fetch
A type of report result fetching in which fetching continues until completion or you
cancel the operation. This is the default fetch method used in MicroStrategy Office.
smart tag alias
A smart tag name that you can specify for a report or project. When Microsoft Office
recognizes this name, the MicroStrategy Office Smart Tag actions menu is
displayed. This means that when the name you specify is typed, it becomes a smart
tag that provides several options to execute the report.
Software Development Kit
A distribution package of application program development software and the
instructions for its use. Allows customization of an application.
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sort
Arranging data according to some characteristic of the data itself (alphabetical
descending, numeric ascending, and so forth). See also: drill, page-by, subtotal.
sort by
The order of the return values of an expression in relation to the order of the value or
metadata object given. A sort by includes whether to sort in ascending or
descending order, and which metadata object to sort by. Sort by may also be
performed on the value of the subexpression, which is the input argument. To sort
by a value or metadata object in an expression, you must set the SortBy parameter.
SQL (Structured Query Language)
The standardized query language established in 1986 by the American National
Standards Institute (ANSI) and used to request information from tables in a
relational database and to manipulate the tables' structure and data.
SQL Engine
The MicroStrategy Intelligence Server component that in report execution converts
report requests into SQL to be used for a database query.
static content
Document content that is contained directly in the document and does not change
from one service execution to the next. It cannot be personalized for different
subscribers. Examples include an HTML template, a static URL, and so on.
Compare with: dynamic content.
Structured Query Language (SQL)
A relational database language used to read data from tables in a relational
database and to manipulate their structure and their data.
subscriber
A person who receives content from at least one service. Each individual who
receives messages from Narrowcast Server has a login that provides passwordcontrolled access to subscription and user preference information for the individual
and his addresses. Subscribing at least one of these addresses to a particular
service allows the individual to receive messages.
subscription
An enrollment in a service; a subscription is composed of one user and one address.
subscription administrator Narrowcast Administrator user role.This role is designed
for a console user who manages the Subscription Book, including users, addresses,
and subscription sets. Also installs, configures, and administers the Subscription
Book Module.
Subscription Book
Contains all users, addresses, and subscription sets. Stored in the Subscription
Book Repository. See also: subscription set.
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Subscription Book Module
A software component that provides the ability to create, edit, and use the objects
within a Subscription Book. Those objects include users, addresses, and
subscriptions. See also: Subscription Book Repository.
Subscription Book Repository
Stores all subscription information, including addresses and user information.
Subscription ID
The ID value used by Narrowcast Server to segment subscription sets. It is also
called Segmentation ID. This value is required for dynamic subscription sets.
subscription information
All information related to an individual subscription. This information allows
Narrowcast Server to deliver services to individual recipients in the desired manner.
subscription information object
Retrieves subscription information for a given segment. See also: dynamic
subscription set, information object, segmentation, subscription information.
Subscription Portal
A feature of Narrowcast Server that allows end users to subscribe to Narrowcast
Server services offered through a web-based portal. This enables end users to
experience personalized and proactive interactions, based on user-defined
permissions and preferences. See also: portal.
subscription set
A collection of addresses that can be subscribed to a service. Subscription sets are
either static or dynamic. A static subscription set is an application object that
retrieves subscription information from the Subscription Book Repository. Useful
when the set of end user addresses that should receive a service does not change.
A dynamic subscription set is an application object containing at least one piece of
subscription information retrieved from an information object instead of from the
Subscription Book Repository. Useful for changing or alert-driven subscription sets.
Dynamic subscription sets can acquire some or all of their information from the
information object. The rest of the subscription information is acquired from the
Subscription Book Repository. See also: subscription set object.
Subscription Set ID
A numeric value used to identify each subscription set.
subscription set object
Application object that defines how to retrieve or modify a subscription set. Does not
contain the subscription set, but provides instructions for how a Subscription Book
Module should retrieve it. See also: Subscription Book Module, subscription set.
subtotal
A totaling operation performed for a portion of a result set. See also: drill and pageby.
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summary metric
A shortcut to a subtotal, or a subtotal metric allowing explicit aggregation in
documents. A summary metric allows you to select the function to use to calculate
the subtotal.
system administrator
Narrowcast Administrator user role. This role is designed for a console user who
performs the following tasks: Installs and configures the information delivery
platform, Migrates system objects and application objects, Monitors, analyzes, and
tunes the system to ensure the smooth and balanced operation of the decision
support or business intelligence environment, Performs troubleshooting and error
recovery.
system developer
Narrowcast Administrator user role. This role is designed for a Narrowcast Server
user who employs the SDK and embeds Narrowcast Server technology into
another product or application.
system locale
A language in which all objects are guaranteed to have a name. Since objects can
have names in several locales (languages), it is necessary to have one locale where
a name always exists for all objects. The system locale serves this role, because the
system locale cannot be deleted. See also: locale, display locale, execution locale,
selection locale, user locale, text container See: document (Narrowcast).
T
table
The primary physical component of a data warehouse, logically consisting of
columns of data of varying types.
template
A MicroStrategy object that serves as a base on which you can build other objects of
the same type. You can create a template for almost any kind of MicroStrategy
object, such as filters or reports. Also see: Object template and Report template.
text field
A type of control in a document that displays text in the document. These different
types of text content are: Static text, which does not change and serves as a label.
Dynamic text, which is populated by the document or dataset. There are two types
of dynamic text: Data field, which is populated from a dataset with data that
originated in the data warehouse (or an Intelligence Server cache). A data field is
only a reference to an object on a report. Auto text code, which is populated by the
document or dataset, consisting of their settings rather than data from the data
warehouse. A combination of any or all of the above types in one text field. See also:
Data field, Auto text code.
threshold
Used to create conditional formatting for metric values. For example, a threshold
triggers the report that, if dollar sales is greater than $200, format that cell to have a
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blue background with bold type.
transformation
A schema object that encapsulates a business rule used to compare results of
different time periods. Transformations are used in the definition of a metric to alter
the behavior of that metric.
U
Unique Message Identifier
An ID value that is unique for all messages delivered by any Narrowcast Server
system. It can be used for message tracking purposes to determine which recipients
have received and opened messages. This is typically done by creating a URL
containing this ID value that refers to a zero-size image on a Web server that is
configured to track references to this URL.
user hierarchy
Named sets of attributes and their relationships, arranged in specific sequences for
a logical business organization. They are user-defined and do not need to follow the
logical model.
user ID
A numeric value used to identify individual users.
user information
The collection of information, including first name, last name, address, zip code, and
other personal information, that changes from one subscriber to the next.
user locale
Defines which content the user receives. This is set up by either the subscription
administrator via Narrowcast Administrator or by the user via Subscription Portal.
The user locale must match the selection locale. See also:locale, display locale,
selection locale, system locale.
V
view filter
The set of criteria that restricts the report data that is currently being viewed. It may
include filtering conditions based on any of the objects on the report.
Visual Insight dashboard
A visually-striking, interactive display that takes a minimal amount of time to set up
and use. You can add text, interactive data visualizations, data filtering, and multiple
layers of organization to your dashboard, then take advantage of Visual Insight's
formatting options to customize your display.
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W
widget
A type of control that presents data in a visual and interactive way; an interactive
Flash-only graph that dynamically updates when a new set of data is selected.
Some types include Gauge, Heat Map, and Stacked Area widgets.
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1
INDEX
A
Add 92
analytical engine 55
And 252
Apply functions 21, 26
ApplyAgg 132
ApplyComparison 133
ApplyComparison in filter 30
ApplyLogic 133
ApplyLogic 133
example 133
ApplyOLAP 133
example 134
ApplySimple 134
example 29, 134
argument 36
prompts 37
arithmetic operator 238
ApplyOLAP 133
Divide 239
ApplySimple 134
Minus 238
example 27, 30, 133-134
Plus 239
syntax 132
Times 239
using extra arguments in 132
Unary Minus 240
ApplyAgg 132
example 133
ApplyComparison 133
example 27, 30, 133
in filter 30
attribute 36
attribute form expression 44
accessing 45
average 93
moving 160
OLAP 171
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running 186
Between 243
Average 93
Between Enhanced 250
Avg 93
break by
example 32
B
banding functions 131
BandingC 137
BandingP 139
base formula 38
parameter 32
business example
confidence level 75
hypothesis testing 70
statistical descriptors - Simple 84
basic functions 92
C
Add 92
Average 93
Case 141
Avg 93
case functions 131
First 97
comparison for rank operator 248
GeoMean 98
Between Enhanced 250
Greatest 99
Equal Enhanced 249
Last 100
Greater Equal Enhanced 250
mathematical (arithmetic
operators) 238
Less Equal Enhanced 248
Max 104
Median 105
Min 106
Mode 107
Product 109
StDev 111
StDevP 110
Sum 113
Var 115
VarP 114
Begins With
comparison operator 243
string function 221
630
Not Between Enhanced 251
Not Equal Enhanced 249
comparison operator 240
Begins With 243
Between 243
Contains 243
Ends With 244
Equal 241
Greater 242
Greater Equal 242
In 244
Less Equal 241
Less Than 240
Like 244
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Not Begins With 246
DayOfMonth 120
Not Between 246
DayOfWeek 120
Not Contains 247
DayOfYear 121
Not Ends With 247
DaysBetween 121
Not Equal 241
FiscalMonth 122
Not In 247
FiscalQuarter 122
Not Like 248
FiscalWeek 123
compound metric 38
FiscalYear 123
consolidation element expression 46
Hour 124
accessing 47
MilliSecond 124
Contains 243
Minute 125
count
Month 125
moving 162
MonthEndDate 125
OLAP 174
MonthsBetween 126
running 187
MonthStartDate 126
CurrentDate 118
Quarter 127
CurrentDateTime 118
QuarterStartDate 127
CurrentTime 118
Second 128
custom expression with
ApplyComparison in filter 30
toDateTime 128
custom group expression 47
accessing 48
Week 129
WeekStartDate 129
Year 129
D
Data Mining functions 255
YearEndDate 130
YearStartDate 130
Date 118
DateDiff 119
date and time functions
DayOfMonth 120
CurrentDate 118
DayOfWeek 120
CurrentDateTime 118
DayOfYear 121
CurrentTime 118
DaysBetween 121
Date 118
Degrees 268
DateDiff 119
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deviation
Greater than 25
standard (population) 110
Median 23
standard (sample) 110-111
metric dimensionality 39
weighted standard (sample) 199
prompted date 27
dimensionality 38
RunningSum 24
distribution, standard normal
cumulative 111, 199
sort by 33
Divide 239
expression 18
example 18
E
Ends With
comparison operator 244
ExpWghMovingAvg 148
example 149
ExpWghRunningAvg 151
string function 224
engine
analytical 55
query 55
SQL 55
Equal 241
Equal Enhanced 249
example
And 26
Apply function 27, 133-134
ApplyAgg 133
ApplyComparison 27, 133
ApplyLogic 133
ApplyOLAP 134
ApplySimple 29, 134
Avg 23
break by 32
BreakBy 24
expression 18
F
fact expression 48
accessing 49
filter
ApplyComparison function in 30
filter expression 50
accessing 51
financial functions
Rate 266
First 97
first occurrence 229
FirstInRange 152
FiscalMonth 122
FiscalQuarter 122
FiscalWeek 123
FiscalYear 123
formula, metric 38
formula. See compound metric. 38
FTest 272
function parameter effects 36
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function
Input Metric Formula dialog box 44
basics of 20
Insert Function Wizard 53
parameters 31
internal functions
processing 55
ApplyAgg 132
using prompts 37
ApplyComparison 133
function plug-in 67
ApplyLogic 133
function type 21
ApplyOLAP 133
Apply function 26
ApplySimple 134
Apply functions
BandingC 137
example 27
BandingP 139
comparison function 25
Case 141
group-value function 23
IsNotNull 145
OLAP function 24
IsNull 145
single-value function 21-22
L
G
Lag 154
GeoMean 98
Last 100
Greater 242
last occurrence 225
Greater Equal 242
LastInRange 158
Greater Equal Enhanced 250
LastPosition 225
Greatest 99
Lead 159
LeftStr 225
H
HeteroscedasticTTest 273
hierarchy 24
Length 226
Less Equal 241
Less Equal Enhanced 248
HomoscedasticTTest 273
Hour 124
Less Than 240
Like 244
I
logical operator 252
If 142, 252
And 252
In 244
If 142, 252
InitCap 224, 234
Not 253
initial capitalization 224, 234
Or 253
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Lower 227
Minus 238
LTrim 227
Minute 125
Mode 107
M
Match 228, 232
mathematical functions
Degrees 268
Tanh 270
Max 104
maximum
moving 165
OLAP 176
running 188
MeanTTest 275
Median 105
metric 38
base formula 38
dimensionality 38
formula 38
metric expression
accessing 42
Metric Editor 42
MicroStrategy engine 55
function 55
function type 6
structure 55
Month 125
MonthEndDate 125
MonthsBetween 126
MonthStartDate 126
moving
average 160
average, exponential weight 148
count 162
difference 164
maximum 165
minimum 166
standard deviation (population) 167
standard deviation (sample) 169
sum 170
MovingAvg 160
example 161
MovingCount 162
MovingDifference 164
MovingMax 165
MovingMin 166
MovingStdev 169
MovingStDevP 167
MovingSum 170
MilliSecond 124
N
Min 106
minimum 106
634
Not 253
moving 166
Not Begins With 246
OLAP 179
Not Between 246
running 189
Not Between Enhanced 251
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Not Contains 247
MovingAvg 160
Not Ends With 247
MovingCount 162
Not Equal 241
MovingDifference 164
Not Equal Enhanced 249
MovingMax 165
Not In 247
MovingMin 166
Not Like 248
MovingStdev 169
NTile 202
MovingStDevP 167
NTileSize 204
MovingSum 170
NTileValue 205
OLAPAvg 171
NTileValueSize 212
OLAPCount 174
NULL/Zero functions
OLAPMax 176
IsNotNull 145
OLAPMin 179
IsNull 145
OLAPRank 181
ZerotToNull 146
OLAPSum 183
RunningAvg 186
O
occurrence, position of first 229
occurrence, position of last 225
OLAP
average 171
count 174
maximum 176
minimum 179
rank 181
sum 183
OLAP functions
ExpWghMovingAvg 148
ExpWghRunningAvg 151
FirstInRange 152
Lag 154
LastInRange 158
RunningCount 187
RunningMax 188
RunningMin 189
RunningStDev 191
RunningStDevP 190
RunningSum 192
WeightedMean 197
WeightedStDev 199
OLAPAvg 171
OLAPCount 174
OLAPMax 176
OLAPMin 179
OLAPRank 181
OLAPSum 183
operator
arithmetic 238
Lead 159
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comparison 240
RScriptU 256
logical 252
integrating with MicroStrategy 255
Or 253
Rank 217
rank and NTile functions
P
parameter 31
NTile 202
NTileSize 204
accessing 33
NTileValue 205
BreakBy 32
NTileValueSize 212
common 32
Percentile 213
SortBy 33
PercentRank 214
Pass-through functions. See Apply
functions. 26
PercentRankRelative 216
Percentile 213
Rank 217
PercentRank 214
rank operator, comparison for 248
PercentRankRelative 216
rank, OLAP 181
Plus 239
Rate 266
Position 229
RDBMS platforms 131
Product 109
Replace 230
prompt
RightStr 231
using in functions 37
RScript 255
RScriptAgg 255
Q
Quarter 127
RScriptSimple 256
QuarterStartDate 127
query engine 55
RScriptU 256
RTrim 232
R
R (analytics)
functions 255
636
RScriptAggU 256
running
average 186
average, exponential weight 151
RScript 255
count 187
RScriptAgg 255
maximum 188
RScriptAggU 256
minimum 189
RScriptSimple 256
standard deviation (population) 190
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standard deviation (sample) 191
sum 192
string
from left 225
RunningAvg 186
from right 231
RunningCount 187
string functions
RunningMax 188
Begins With 221
RunningMin 189
Ends With 224
RunningStDev 191
InitCap 224, 234
RunningStDevP 190
LastPosition 225
RunningSum 192
LeftStr 225
Length 226
S
Second 128
simple metric
base formula 38
single-value function 21
sort by
example 33
parameter 33
SortBy parameter 147
SQL engine 55
standard deviation
moving 167, 169
running 190-191
statistical functions
FTest 272
HeteroscedasticTTest 273
HomoscedasticTTest 273
MeanTTest 275
WeightedCorr 194
WeightedCov 196
StDev 111
Lower 227
LTrim 227
Match 228, 232
Position 229
Replace 230
RightStr 231
RTrim 232
SubStr 233
toNumber 235
toString 235
Trim 236
Upper 236
SubStr 233
subtotal expression 51
accessing 52
subtotals
user-defined 51
sum
moving 170
OLAP 183
running 192
StDevP 110
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Sum 113
of a sample 115
support. See technical support. 17
VarP 114
T
W
Tanh 270
Week 129
technical support 17
WeekStartDate 129
Times 239
WeightedCorr 194
toDateTime 128
WeightedCov 196
toNumber 235
WeightedMean 197
toString 235
WeightedStDev 199
totals
window size 61
user-defined subtotals 51
Y
transformation expression
Year 129
accessing 52
YearEndDate 130
trim
YearStartDate 130
left 227
right 232
Z
Trim 236
ZeroToNull 146
U
Unary Minus 240
Upper 236
upper case 236
user-defined subtotals 51
V
value
first in range 152
last in range 158
Var 115
variable. See compound metric. 38
variance
of a population 114
638
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