Data Provenance: A Categorization of Existing Approaches

Data Provenance: A Categorization of Existing Approaches
Data Provenance: A Categorization of Existing Approaches∗
Boris Glavic, Klaus Dittrich
University of Zurich
Database Technology Research Group
glavic@ifi.unizh.ch, dittrich@ifi.unizh.ch
Abstract: In many application areas like e-science and data-warehousing detailed
information about the origin of data is required. This kind of information is often referred to as data provenance or data lineage. The provenance of a data item includes
information about the processes and source data items that lead to its creation and
current representation. The diversity of data representation models and application
domains has lead to a number of more or less formal definitions of provenance. Most
of them are limited to a special application domain, data representation model or data
processing facility. Not surprisingly, the associated implementations are also restricted
to some application domain and depend on a special data model. In this paper we give
a survey of data provenance models and prototypes, present a general categorization
scheme for provenance models and use this categorization scheme to study the properties of the existing approaches. This categorization enables us to distinguish between
different kinds of provenance information and could lead to a better understanding of
provenance in general. Besides the categorization of provenance types, it is important
to include the storage, transformation and query requirements for the different kinds of
provenance information and application domains in our considerations. The analysis
of existing approaches will assist us in revealing open research problems in the area of
data provenance.
1
Introduction
With the increasing amount of available storage space and the acceleration of information
flow induced by the internet, a growing interest in information about the creation process
and sources of data has developed.
Scientists in the fields of biology, chemistry or physics use data from so-called curated
databases. Most of the data stored in a curated database is a result of manual transformations and derivations. Researchers that use data from a curated database are interested
in information about the sources and transformations that were applied to this data. This
information can be used to assess the data quality, examine or reconsider derivation processes or re-run a specific experiment.
Data warehouses are used to integrate data from different sources and with different data
representations, and to analyze the integrated data. Such analyses could benefit from in∗ founded
by SNF Swiss National Science Foundation: NFS SESAM
formation about the original data sources and transformations used to create the data in the
data warehouse.
In workflow management systems and more generally in systems based on SOA (service
oriented architecture), several, probably distributed services are used to accomplish complex computational tasks. For the user it would be interesting to understand how a result
from such a computation was created.
When combined with timestamped secure certificates, data provenance could be used to
investigate copyright issues. For example, a scientist could prove that he was the first one
to perform a certain experiment or that he is the creator of a specific piece of information.
In business applications, document management systems are used to manage the company
documents and permit multiple users to work on the same document. A supervisor can
use provenance information to gain a better understanding of the workflow in his company
or find the origin of erroneous documents or document parts. The same ideas apply to
distributed software development tools.
Other application domains that could benefit from provenance information include interactive statistical environments, visualization and KDD (knowledge discovery in databases).
While this broad range of application areas would benefit from provenance information,
the type of provenance data, manipulation and querying facilities needed differ from application to application. Therefore, we try to find out the differences and similarities between
the various application and data model provenance needs and present a general scheme for
the categorization of provenance. By defining this scheme and applying it to existing work
we hope to reveal open questions in the area of data provenance.
The remainder of this paper is organized as follows. In section 2 we discuss existing
research approaches for managing provenance and introduce a consistent terminology. In
section 3 we present our categorization scheme based on a generic view of provenance.
The application of this scheme to study the properties of existing approaches is presented
by means of an example in section 4. Finally in section 5 we cover open research questions
and conclude in section 6.
2
Overview of Existing Approaches
A broad diversity of terms is used in the literature for provenance related concepts. To
prevent confusion we introduce a consistent terminology for the most important concepts
in data provenance and relate them to the terminology used by other researchers. A number
of synonyms like lineage or pedigree are used for data provenance. We decided to use
the term provenance, because it is short and intuitive. The terms provenance model and
provenance management system are used to distinguish between a conceptual model for
provenance and a system for the management of provenance information. Provenance
was studied for different data models and levels of detail. Each data model has its own
terminology for data and hierarchical structures. We use the term data item for a structural
unit of data, which is the target of provenance management and the notion level of detail
for the granularity of a data item. For example a data item could be an XML-document, a
database tuple, a database relation, a database schema construct or a a file in a file system.
Tuple and relation are two possible levels of detail for a data item in a relational database.
To abstract from different systems for storing data like relational databases, object oriented
databases and file systems, we refer to these storage systems as data repositories. The term
hierarchy position is used for the position of a data item in the structural hierarchy of a
data model. For example, for database tuples the hierarchy position could be represented
by the database and relation the tuple is stored in.
There are two basic views of provenance. The first one describes the provenance of a data
item as the processes that lead to its creation and the other one focuses on the source data
from which the data item is derived from. In contrast to [Tan04], where these concepts
are called provenance of data and provenance of a data product, we use the terms source
provenance and transformation provenance because these notions seem to be more intuitive. The term transformation refers to the creation process itself and the terms source
and result refer to the input and output of a transformation.
Most of the existing research can be classified by their approach to provenance recording.
One research direction focuses on computing provenance information when data is created, while the other computes provenance data when it is requested. Tan [Tan04] refers
to these approaches as lazy and eager. Most of the eager approaches are based on annotations about source data items and transformations, and most of the lazy approaches rely
on inversion or input tracing of transformations.
Buneman et al. distinguish in [BKT01] between Why- and Where-provenance. While
Why-Provenance captures all source data items that contributed to the creation of a result
data item, Where-provenance captures the concrete origin of a result. We use the terms
contributing source and original source instead of Why- and Where-Provenance. In section 3 these concepts are discussed more in detail.
Several surveys on provenance have been published [BF05, SPG05a, SPG05b], but most
of them cover only a specific type of provenance or separate different kinds of provenance
according to their application domain. In [SPG05b] a taxonomy of provenance systems
is introduced. While forming a valuable overview of existing approaches, this taxonomy
fails in seeing provenance from a more abstract, conceptual point of view.
A number of authors address provenance in the context of services and workflow management. The PReServ (Provenance Recording for Services ) [GMM05, GJM+ 06a, GJM+ 06b]
approach uses a central provenance management service. In [GMM05] this web service
receives messages, called p-assertions, from the web services used for data transformation. The provenance data is stored by the provenance management service in a so-called
provenance store. PReServ uses a common interface to enable different storage systems as
a provenance store. The system relies on the user or service developer to modify existing
services to support p-assertions. Simmhan et al. [SPGM06] expect services to post provenance information on a message board. A provenance service collects these messages and
stores them in a provenance store. The myGrid system [SRG03] provides middleware for
biological experiments represented as workflows. myGrid records all service invocations
including parameters and data items. This log information can be used to derive the prove-
nance of a data item produced by a workflow [GGS+ 03, KTL+ 03, ZGG+ 03, ZGSB04].
Chimera [FVWZ02, Fos03] offers a Virtual Data Catalog for provenance information. A
user registers transformations, data objects and derivations (an execution of a transformation) in the Chimera Virtual Data Catalog (VDC). The VDC is implemented as a relational
database. VDL (Virtual Data Language) provides query and data definition facilities for
the Chimera system. While the first prototype is limited to file system data objects and
executable program transformations, the system is to be extended to support relational or
object-oriented databases and SQL-like transformations. Chimera is a part of the GryPhyN
project [AF00], a research project developing techniques for processing and managing
large distributed data sets in data grids.
Cui et al. [CWW00, Cui02] focus on lazy computation of provenance for data warehouses.
They study views in data warehouses and develop algorithms to generate queries for provenance tracing. These queries trace all source data tuples that lead to the creation of a tuple
in a view. In [CW01] their approach is extended to general transformations that could not
be expressed in SQL. While rather complete in the domain of transformations and views
in data warehouses, their approach is limited to this domain and does not include other
data models or user generated provenance. In the work of Fan and Poulovassilis [FP05],
provenance data is recorded at schema level in the context of schema transformations in a
data warehouse.
The topic of provenance for relational databases was first discussed in the context of visualization [WS97]. Here data transformations are represented as functions from one attribute domain to another. Provenance is traced by using inversions of these functions.
Another approach for provenance management in a visualization environment is presented
in [Gro04]. Groth et al. record user actions in an interactive visualization environment
and present the whole user interaction as a DAG (directed acyclic graph). The user can
navigate in this graph and jump back to previous states of the system.
In [Wid05], Widom presents considerations for the Trio-system. Trio is a database system for handling uncertain data and provenance. The formal foundation and implementation details are presented in [ABS+ 06]. Uncertain data has been intensely studied by
the database community for more than two decades, but the combination of uncertainty
and provenance introduce new challenges. Trio shows that provenance information can
be used to solve some of the problems that arise from introducing uncertain data in a
database system. While interesting because of the combination of provenance and uncertainty, the provenance computation of the Trio system based on earlier work for data
warehouse views [CWW00, Cui02]. The provenance of the tuples in the database is stored
in an auxiliary table. Trio records one level of provenance, meaning only the tuples from
which a tuple was directly derived are stored in the provenance relation. A database-wide
unique tuple ID is used to identify tuples. Halevy states in [HFM06] that the management
of provenance in combination with uncertainty is needed for dealing with data spaces, a
concept for next generation data management systems introduced in this publication.
In the GIS research area, the importance of provenance for evaluating the quality of data
items has been recognized early on. Most publications from this area focused on the development of metadata standards [HE97] which include provenance information. A well-
designed metadata standard could provide a basis for provenance management system, but
the proposed standards are limited to the GIS-domain and cannot be easily generalized.
More important the metadata defined by these standards is meant to be provided by a user
and may not be appropriate for automatic processing.
In [BCC06], data from various data sources is represented in a tree-structure. In this
framework updates, insertions and deletes are copy, paste and insertion operations on these
data trees. The authors present a query language that operates on data trees. Buneman et
al. used this tree-representation of data for archiving [BKTT02]. They require unique keys
for every data object. These keys can be used to associate different versions of a data item
in different versions of a database (or data repository).
Provenance is related to data annotation. Annotation systems like DB-Notes [CTV05] and
MONDRIAN [GKM05] enable a user to annotate a data item with an arbitrary number
of notes. These notes are normally propagated when annotated data is transformed. Using annotations to represent information about a data item is a common approach in life
sciences. The possibilities of using annotations to maintain provenance information were
first discussed in [BKTT02, BS02]. This approach was also taken in [ZGG+ 03, MPL+ 06,
CTV05]. The DB-Notes system [CTV05] enable a user to store annotations at the attribute
level for data in a relational database. DB-Notes is based on a relational database and
queries are represented in pSQL, an SQL-like language including constructs for managing
annotations.
Data provenance is also related to temporal data management and versioning. Like in
temporal data management, in provenance previous versions of a data item are queried
and accessed. So provenance management systems may benefit from existing storage
methods and query optimizations for temporal databases. Intelligent archiving techniques
[BKTT02] need methods capable of identifying an object in different versions of a database
or document. The identification methods used in this context may be also applicable to
provenance management.
3
A categorization scheme for conceptual properties of data provenance
In this section we discuss data provenance from a conceptual point of view, extend the
terminology introduced in section 1 and define a general categorization scheme for provenance management systems. We define several functionalities a provenance management
system can provide and order these functionalities in a hierarchy of categories. The three
main categories of our categorization scheme are provenance model, query and manipulation functionality and storage model and recording strategy. We present an overview figure
for each main category (figures 1, 2 and 3). Categories and functionalities are represented
by boxes and ellipses in these figures.
3.1
Provenance model
provenance model
source
provenance
transformation
provenance
world model
representation of
transformations
handle
different
detail levels
transformation
class
transformation
closed
world
model
open
world
model
source
hierarchy
source
definition
handle
different
detail levels
meta
data
input
source
semiautomatic
transformation
automatic
transformations
recognise
transformation
versions
recognise
item
duplicates
representation of
source data
original
source
data
meta
data
identification
recognise
item
versions
supported trans‐
formations
manual
transformations
original
source
con‐
tributing
source
positive
contributing
source
negative
contributing
source
recognise
transformation
duplicates
Figure 1: Provenance model
The category provenance model describes the expressive power of the conceptual model
used by a provenance management system to define the provenance of a data item. We
define a number of functionalities and categorize a provenance system by means of the
functionalities it supports. As stated before, the provenance of a data item can be divided
into the two parts transformation provenance and source provenance. Source provenance
is information about the data that was involved in the creation of a data item. There are
three conceptual definitions for source provenance. These are original source, contributing
source and input source. The input source of a data item includes all data items that were
used in the creation of this data item. The positive contributing source of a data item
includes data items that are essential for the creation of this data item. In formal terms
the positive contributing source of a data item X is the union of all minimal subsets of the
input source that, when used in the process that lead to the creation of X, would also lead
to the creation of X. The original source of a data item consists of all data items which
include data that is copied to the resulting data item.
For example, assume we manage the provenance of data in a relational database with two
relations r1 and r2 and handle data items at tuple level of detail. When executing the SQLquery SELECT r1 .name FROM r1 , r2 WHERE r1 .id = r2 .id against the a database including relations r1 and r2 the input source of a resulting tuple t includes all tuples in r1
and r2 . The positive contributing source of t consists of all tuples t0 from relation r1 and
t00 from relation r2 with t.name = t0 .name and t0 .id = t00 .id. At last the original source
of t includes all tuples t0 from relation r1 with t.name = t0 .name.
The concepts original source and positive contributing source where first introduced in
[BKT01] under names Why- and Where-provenance. Note that the following subset relationship holds:
input source ⊇ positive contributing source ⊇ original source
Some applications would benefit from information about data items that are not existent
in the source, but would inhibit the creation of a resulting data item, if they were included
in the source. This concept has been first introduced by Widom et. al. in [CWW00].
We use the term negative contributing source for this concept. In contrast to the concept
positive contributing source, this definition is not straightforward. It seems feasible to
include either all data items that would alone prohibit the creation of the result or include
combinations of data items that would prohibit the creation of the result. In most data
repositories the amount of data stored in the repository is only a very small fraction of the
all possible data that could be stored in the repository. So in the general case, it is not
possible to actually store the negative contributing source of a data item.
Additional to the considerations which kind of sources should be included in the source
provenance, a provenance management system can record various information about each
source data item. A source could be represented as the original data, metadata attached to
the source, the source hierarchy structure or a combination of this representations.
A provenance system can either record source data items at one level of detail or be able to
handle multiple levels of detail. For example, the source of a tuple data item in relational
view could include all tuples from a relation r. If the provenance model handles multiple
levels of detail the source could be represented as relation r instead of representing it by all
tuples from r. Managing provenance information at different levels of detail is the more
flexible approach and can result in smaller storage overhead of provenance information,
but requires a more complex provenance model.
Transformation provenance is information about the transformations that were involved
in the creation of a data item. To make a clear separation between a concrete execution
of a process and the process itself we use the term transformation class for the first and
transformation for the later. Foster et al. use the terms transformation and invocation for
these concepts [Fos03]. In our concept a transformation is not limited to be an automatic
process, but may be a manual process or a semi-automatic process with user interaction.
The transformation provenance of a data item could include metadata like author of the
transformation, the user who executed the transformation and the total execution time.
Examples for transformations are SQL statements used to create views in a database, the
workflow descriptions of a workflow management system and executable files with command line parameters.
An important part of the provenance model is the world model, which could be either
closed or open. In a closed world model the provenance management system controls
transformations and data items. Contrary in an open world model the provenance management system has no or only limited control over the executed transformations and data
items. Data items and transformations can be executed, manipulated, created or deleted
without notification. From the view of the provenance management system, the world has
an uncertain behavior which results in complex provenance recording or make it impossible to record exact provenance information. The closed world and open world model are
extremes and there are many possible world models that are neither closed world nor open
world models.
Besides the functionalities a provenance management system provides to handle source
and transformation provenance, it should be able to recognize if two data items from two
different data repositories represent the same real world object. For example the same data
item could be stored in many databases or even in a database and as an XML-document.
As real world objects tend to change over time, it is important to have mechanisms for
checking if two data items are different versions of one real world object. Identification
is especially important, when updates to the data repositories are not controlled by the
provenance management system. In this case the information about source data items
recorded by the system might be incorrect, because these data items were changed or
deleted by an update.
The data item equality needed for provenance management systems with open world models is a semantic equality, which has been studied in depth by the data integration community [Ken91, New88, PAGM96, BN05]. Semantic equality in general is not solvable
for open world models, but there are several heuristical approaches to this problem (e.g.
[Zia99]). The problem of identifying different versions of the same object also applies to
archiving and is discussed in [BKTT02].
There are several possibilities to identify duplicates. A straightforward approach would be
to check if the data item and the duplicate represent exactly the same information. We refer
to this approach as value based duplicate identification. If data items have a key property,
then another approach is to identify duplicates by their key property. For example, if data
items are tuples in a relational database, two tuples could be defined to be duplicates if they
have the same attribute values or if they have the same key attribute values. Using the primary key constraints of a relational database for identification could be problematic when
no further restrictions are introduced, because the primary key uniqueness is restricted to
one relation and primary keys can be changed by updates.
Many data models have an explicit or implicit hierarchical structure. This hierarchy in
combination with a key property or value equivalance could be used to identify a data
item. For example, if the provenance of tags in XML-documents is recorded, duplicates
could be defined by the name of the tag and the position of the tag in the hierarchy of the
document.
3.2
Query and manipulation funcionalities
To be useful for a real world application, a provenance management system should provide facilities to manipulate and query provenance information and data items. We do not
discuss manipulation and querying of data items without integration of provenance infor-
manipulation and query facilities
provenance data
manipulation
provenance
based
recreation
create
inverse
transformation
provenance
Merging
pliting
query
source
provenance
merging
data
model
spliting
storage
strategy
type
Figure 2: Query and manipulation funcionalities
mation, because these query and manipulation facilities have been extensively studied and
are well understood.
If a provenance management system handles transformations at various levels of detail,
it should provenance mechanisms for merging multiple transformations into one transformation and split a complex transformation into a sequence or graph of simpler transformations. This functionality is similar to the composition of processes implemented by
workflow management systems [Moh96]. Provenance data can be used to recreate result
data items [Fos03], which cannot be accessed or are expensive to access, by executing the
transformations that were used to create the result data item. If a provenance management
system is able to compute the inverse of a transformation, then the inversion can be used
to recreate source data items from result data items. For example Widom et al. [CWW00]
compute queries for tracing the contributing source tuples for tuples in a materialized view
based on the view definition statement.
Split and merge operations could also be applied to the data item dimension. Split divides a
higher-level data item into its lower-level parts and merge combines lower-level data items
into a higher-level data item. While the split operation is quite clear, the merge operation
raises some questions. For example, what is the result of the merge operation on a subset
of the lower-level data items that form a higher-level data item, and how can this result
be distinguished from the result of a merge operation on the whole set. A provenance
management system that records provenance information for different data models should
provide facilities for converting the representation of a data item from one data model to
another.
So far we here omit the aspect of provenance storage strategy. Provenance information
may be attached to the physical representation of a data item or stored in a separated data
repository. We discuss this topic in detail in the next subsection. At this point we are only
interested in the fact that a provenance management system may support more than one
storage strategy and might offer mechanisms for changing the storage strategy for data
items.
Depending on the properties of the provenance model and world model it may be difficult
or even impossible to implement the postulated manipulation operations.
3.3
Storage and recording
storage and recording model
storage
strategy
nocoupling
tightcoupling
storage
model
loosecoupling
nopropagation
recording
strategy
propagation
restricted
propagation
complete
propagation
user
controlled
no
eager
system
controlled
Figure 3: Storage and recording
The category storage and recording includes the approaches a provenance management
system uses to store provenance information, to record provenance information and to
propagate provenance information recorded for source data items.
Storage strategy describes the relationship between the provenance data and the data which
is the target of provenance recording. There are three principal storage strategies: the nocoupling, the tight-coupling and the loose-coupling recording strategy. The no-coupling
strategy stores provenance information in one or many provenance repositories. These
repositories are dedicated to storing only provenance data. The second option, tightcoupling recording, stores provenance directly associated with the data for which provenance is recorded. The loose-coupling strategy uses a mixed storage scheme where provenance and data are stored in one storage system but logically separated.
Most approaches based on annotation use a tight-coupling or loose-coupling strategy by
attaching provenance annotations to data items or storing annotations in the same data
repository, but separated from the data items. Service based approaches in general record
provenance for several data repositories in a distributed environment. These approaches
normally deal with a very heterogeneous environment with limited control over the execution of processes and manipulation of data item, which make it difficult to record provenance information. Some control over this environment and especially over the provenance information can be gained by using a closed world model and a no-coupling storage
strategy.
There are multiple storage models for storing provenance. In principle every data model
could be used to store provenance information, but not every combination of storage model
and storage strategy is reasonable.
If provenance is recorded for a transformation which uses source data items with attached
provenance information, how is this information propagated to the result data items? The
three possible answers to this question are no-propagation, restricted propagation and
complete propagation. With no-propagation the provenance of source data items of a
transformation is ignored when creating provenance data for result data items of the transformation. Contrary under complete propagation the result data items of a transformation
inherit all provenance data from source data items according to the kind of source used in
the provenance model. Under restricted propagation a result data item inherits a part of
provenance from the source data items, e.g. data provenance that was created during the
last n transformations.
The provenance recording strategy specifies when provenance data is recorded. We consider user controlled recording, eager recording, no recording and system controlled recording. Obviously, with user controlled recording the user decides when and for which data
item he likes to record provenance information. Eager recording records provenance simultaneously with every transformation. The no recording approach generates provenance at query time. Under system propagation recording data creation is controlled
by the provenance management system. This system could use strategies like record
the provenance data once a day or record the provenance after every n transformations.
Eager recording and no recording are related to the eager and lazy provenance tracking
approaches introduced in [Tan04].
4
Categorization of existing approaches according to our categorization scheme
In this section we categorize an existing model according to the scheme presented in the
last section. A categorization of all existing approaches is beyond the scope of this paper,
so we limit the discussion to one example. A long version of this paper including more
categorizations is available on the author’s homepage.
4.1
Provenance of views in a data warehouse
world model
identification
representation of transformations
supported transformations
representation of source data
source definition
provenance based recreation
provenance model
closed world model in [CWW00]extended by openness in [CW01] for transformations
no data item versions supported and no duplicate recognition
concrete execution (relational algebra expression) with handling of different levels
of detail
SQL view definitions. In [CW01] extended with general data warehouse transformations (e.g. ETL)
original source data: database tuples
contributing source for views. Only input source for some of the general transformations introduced in [CW01]
query and manipulation facilities
no
transformation provenance manipulation
source provenance manipulation
data model manipulation
storage strategy manipulation
query
storage strategy
storage model
propagation
recording strategy
concept relies on computation of inverses
no
no
no
receive provenance
storage and recording model
no storage
no explicit storage of provenance information
no-propagation
lazy: computed when queried
In [CWW00], Widom et al. study the provenance of materialized views in a data warehouse. They
present algorithms to create inverses of a view definition for tracing the positive contributing source
of tuples in the view, based on the view definition. Transformations are represented by the relational
algebra view definition and are handled at different levels of detail (by defining provenance for the
basic relational operators and for the concatenation of these operators thus allowing views based on
views). The provenance data is computed lazily and is not stored for further use. In the original
paper the closed world model of a data warehouse is used and extended in [CW01] to allow certain
openness and new transformations not representable in relational algebra by including general data
warehouse transformations.
5
Open Questions and Future Research Directions
Our categorization scheme includes functionalities not jet included in provenance management systems. For example developing a provenance management system for open world models is a challenging problem. Furthermore many of the manipulation facilities present in the scheme are not
included in the existing approaches. A formal model designed with the insight gained in this article
could be the basis of a provenance management system that handles not only various storage models, but also different types of source and transformation provenance. Source and transformation
provenance are not completely independent and it would be interesting to investigate under which
circumstances it is possible to convert one into the other and study how much redundancy is introduced by storing source and transformation provenance. It seems also reasonable to investigate
which of the functionalities included in our categorization scheme exclude or imply each other.
Some of the problems faced when dealing with provenance are related to data integration problems.
For example the concept of semantic identity needed to recognize duplicates or versions of data
items in an open world model was studied by various data integration publications. A provenance
management system handling different kind of data items stored in distributed repositories needs
to integrate this data to gain a unified view on the data. Data integration systems might benefit by
including provenance management. For example provenance data could be used to identify duplicate
pieces of data or could help a user to assess the quality of integrated data.
It would be interesting to apply concepts developed in the area of temporal database and versioning
in to the provenance management of updateable data repositories.
6
Conclusions
We have presented a categorization scheme for different types of provenance and categorized existing approaches according to this scheme. The categorization scheme helps us to gain a systematic
overview of the capabilities and limitations of these approaches. Most categories used in our scheme
are based on concepts developed by other researchers, but we investigated new combinations of these
concepts and extended some of these concepts with new aspects.
In future work we will investigate which of the functionalities included in our categorization scheme
exclude or imply each other. Such an analysis would help us to gain a better understanding of
provenance in general. This investigation could be extended to cover investigation of implementation
problems and complexity analysis for different functionality combinations.
We will also define a formal language for the management of provenance data. This language should
include generation, querying and manipulation of provenance data. Unlike existing approaches, this
language should cover not only different data models, but also manage different types of provenance
information. It will include language constructs for converting between different data models and
kinds of provenance data. We plan to explore the computational complexity and the implementation problems of this language with the goal of creating a prototype implementation. Because of
the complexity of the problem, the prototype will be limited to a specific kind of provenance and
restricted manipulation options in the beginning.
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