=Paper=
{{Paper
|id=Vol-263/paper-5
|storemode=property
|title=Bridging the Gap between Data Warehouses and Organizations
|pdfUrl=https://ceur-ws.org/Vol-263/paper5.pdf
|volume=Vol-263
|dblpUrl=https://dblp.org/rec/conf/caise/Stefanov06
}}
==Bridging the Gap between Data Warehouses and Organizations==
https://ceur-ws.org/Vol-263/paper5.pdf
1160 CAiSE'06 Doctoral Consortium
Bridging the Gap between Data Warehouses
and Organizations
Veronika Stefanov⋆
Women’s Postgraduate College for Internet Technologies
Institute of Software Technology and Interactive Systems
Vienna University of Technology
stefanov@wit.tuwien.ac.at
Abstract. Data Warehouse (DWH) systems are used by decision mak-
ers for performance measurement and decision support. Currently the
main focus of the DWH research field is not as much on the interaction
of the DWH with the organization, its context and the way it supports
the organization’s strategic goals, as on database issues. The aim of my
thesis is to emphasize and describe the relationship between the DWH
and the organization with conceptual models, and to use this knowledge
to support data interpretation with business metadata.
1 Problem Statement and Research Question
Data Warehouse (DWH) systems represent a single source of information to
analyze the development and results of an organization[1]. Measures such as the
number of transactions per customer or the increase of sales during a promotion
are used to recognize warning signs and to decide on future investments with
regard to the strategic goals of the organization.
Currently, the main focus of the DWH research field is on database issues,
such as view maintenance, aggregation of data, indexing, data quality, or schema
integration[2]. What has not yet been considered appropriately is the context of
the DWH, its interaction with the organization and the way it supports the
organization’s strategic goals. The conceptual models in Data Warehousing are
strongly data-orientated[3] and do not allow for formally describing DWH con-
text. Models that describe the DWH from various viewpoints, including an out-
side view of the DWH system, its environment and expected usage, are missing.
Moreover, eventhough the data in the DWH by its very nature has to be closely
related to the concerns of the organization, current DWHs also lack sufficient
business metadata that would inform users about the organizational context and
implications of what they are analyzing[4].
This PhD proposal targets the relationship between the DWH and the orga-
nization with two interrelated research questions:
⋆
This research has been funded by the Austrian Federal Ministry for Education,
Science, and Culture, and the European Social Fund (ESF) under grant 31.963/46-
VII/9/2002.
CAiSE'06 DC 1161
How can the relationship between the Data Warehouse and the structure,
behavior, and goals of the organization...
(1) be formally described?
(2) support the interpretation of data?
Section 2 describes the research goals and the research field, followed by the
expected results and their evaluation in Sect. 3, the contribution and beneficiaries
of the expected results in Sect. 4, and a time plan and potential risks in Sect. 5.
Section 6 describes the preliminary results achieved so far, followed by related
work (Sect. 7), and a conclusion (Sect. 8).
2 Research Goals, Field, and Scope
I address the research questions stated in Sect. 1 with two goals:
1) Development of a Conceptual Modeling Language. Diagrams that show
how the organization is related to the DWH will be developed, to make it
possible to model how the organization interacts with the DWH, and how
its structure and behavior are mirrored by the DWH (data) structure.
2) Creation of Business Metadata. Knowledge about the organization, cap-
tured in an enterprise model, will be linked to the DWH by means of model
weaving [5] and used to gain business metadata. Business metadata describes
the business context of the data, its purpose, relevance, and potential use[4].
These goals represent different ways of applying the same knowledge about
the relationship between the DWH and the organization, and they achieve differ-
ent contributions (see Sect. 4). Because this thesis applies modeling techniques
to the DWH as the application area, it positions itself in a multidisciplinary
research field between Model Engineering and Data Warehousing, as visualized
in Fig. 1.
Data
Conceptual Warehousing
Model Modeling X
Engineering
Fig. 1. Research Field of the PhD
The scope of this thesis is limited to the conceptual level and the relation-
ship between the DWH and the organization only. It does not include DWH
development projects (which have their own goals and also interact with the or-
ganization), technical details of data mapping and DWH design or methodology.
1162 CAiSE'06 Doctoral Consortium
3 Methodology and Evaluation
The goals of the PhD will be achieved and the results evaluated as follows:
Development of a Conceptual Modeling Language. To reach the first goal,
conceptual models to show the relationship between the DWH and the struc-
ture, behavior and goals of the organization will be developed. Models for
five different aspects are planed. The models will be based on UML 2.0 and
implemented as UML Profiles (preliminary results in Sect. 6.1).
Conceptual Models are difficult to evaluate. Related approaches in the area
of DWH (see Sect. 7) are usually applied to examples and scenarios. Serrano
et al. [6] attempt to empirically evaluate DWH data models with quantita-
tive metrics. Wolff and Frank [7] propose a multi-perspective framework for
evaluating conceptual models with regard to organizational change.
The preliminary results described in Sect. 6.1 were tested with example busi-
ness processes. As soon as more mature models are available, I am planning
to test them in a real-world setting at a bank, where a colleague has already
expressed interest.
Creation of Business Metadata. To achieve the second goal, weaving mod-
els[5] will be developed to link conceptual models with the DWH data model.
Through the weaving links, business metadata can be generated (for prelimi-
nary work, see Sect. 6.2.2). A prototype of a tool for creating weaving models
and generating business metadata will be developed and tested on a real-
world DWH.
4 Contributions and Beneficiaries
Conceptual models bring benefits during the earlier phases of the DWH lifecycle,
such as requirements analysis and design, whereas business metadata supports
the operational phase. Modeling how the organization interacts with the DWH,
and how its structure, behavior and goals are mirrored in the DWH provides
(1) Increased Visibility and (2) Improved Communication. This is useful dur-
ing development of a DWH, leading to (3) Facilitated Requirements Analysis,
(4) Requirements-driven Design and (5) Streamlined DWH Evolution and Re-
Engineering. It also supports (6) Documentation and (7) Maintenance.
Business Metadata provides background information directly in the DWH,
leading to (1) Improved Data Interpretation as well as (2) Enhanced Usability
and User Acceptance of Gathered Data.
The beneficiaries of this thesis are therefore (a) all people involved in de-
signing, building and maintaining a DWH (i.e. the architects and designers as
well as the users). Their tasks are facilitated, and their project communication
is improved by capturing volatile and implicit knowledge, and making it visible.
And (b), during the operational phase of a DWH, users and maintainers benefit
from improved interpretation through business metadata.
CAiSE'06 DC 1163
5 Time Plan and Risks
I plan to finish my PhD thesis by the end of 2007. This year (2006) is dedicated
to developing additional conceptual models and the business metadata weaving
models.
Among the risks of this PhD thesis are the interdisciplinary subject coupled
with an unconsolidated understanding of the nature of Data Warehousing, which
leads to a small immediate community, as well as the uncertain availability of
suitable real-world examples.
6 Preliminary Results
This section gives an overview over the already completed parts of the thesis.
Section 6.1 addresses research question 1 and presents a modeling approach for
the relationship between DWHs and Business Processes. It is an excerpt of three
papers that have already been published[8–10]. Concerning research question 2,
Sect. 6.2 presents a weaving model based on an enterprise goal model.
6.1 Data Warehouses and Business Processes: A Conceptual Model
DWH information is accessed by business processes. Conceptual models can
make the relationship between the DWH and the business processes visible. The
UML Profile for Business Intelligence (BI) Objects[8] allows to show where and
how a DWH is used by business processes, and which parts of the business
processes depend on which parts of the DWH. We defined seven types of BI
objects , representing the different types of data repositories, as well as the data
models and the means of presentation of the data. Figure 2 shows an example
process using the stereotypes “Fact” and “DWH”. The BI objects are defined
as stereotypes in a UML profile. The use of the stereotypes is guided by OCL
constraints[11] provided with the profile, which can be automatically checked by
many modeling tools.
Fig. 2. Example fraud detection process, modeled as UML 2.0 activity diagram with
BI Objects: Subprocesses access data from (a) two facts and (b) the whole DWH[8]
1164 CAiSE'06 Doctoral Consortium
In [9], we investigated the relationship between DWHs and business pro-
cesses from the viewpoint of performance measurement. DWHs provide Key
Performance Indicators (KPIs), also called metrics or performance measures in
other disciplines, that are accessed by business processes.
The Performance Measurement Perspective is an extension to the Event-
Driven Process Chain (EPC)[12]. It provides model elements for KPIs and other
performance measurement capabilities of a DWH environment.
Finally, [10] offers a broader look at the relationship between DWHs and
business processes, as it also takes active, real-time DWHs into account. We
presented a two-fold approach that adds two perspectives to the EPC. In addition
to the Traditional BI Perspective, which contains modeling elements for a classic
DWH environment, the Active BI Perspective allows to model how an active
DWH influences the control flow of a business process.
Regarding related work, many business process models include features to
show data access, but they do not take the special characteristics of DWH data
into account.
6.2 Business Metadata concerning Enterprise Goals
In order to provide business metadata in the DWH, the context of the DWH, i.e.
the structure, behavior and goals of the organization, has to be modeled in an
Enterprise model. This model is then weaved with the data model of the DWH,
to create links for metadata.
Organizational
Goals Processes Structure
Products Applications
Fig. 3. A basic enterprise model
6.2.1 Enterprise Model Enterprise models are used to formally represent
the structure, behavior and goals of an enterprise organization. They are usu-
ally organized into separate aspects[13].For example, an organization chart can
be used to describe the departments, groups and roles that exist within the or-
ganization, and a business process model to describe the structure of business
processes. Figure 3 shows the outline of a basic enterprise model, organized into
five packages. The business metadata to be created is aimed at covering all ar-
eas of the Enterprise model. “Enterprise model” is used here in a much wider
sense than commonly in Databases, where the term often denotes enterprise data
models.
CAiSE'06 DC 1165
6.2.2 A Weaving Model between Enterprise Goals and the Data
Warehouse Model The first approach to create business metadata for DWHs
exploits the relationship between decision support and enterprise goals. What
is good or bad performance, and which decisions should be taken based on the
data, depends on the goals to be reached. Enterprise goals concern market share,
inventory levels or customer satisfaction and can be seen as an abstraction of
business structure and behavior, as they form the basis for decisions and the
way a company does business. They govern the design of business processes and
the way the organization behaves.
We introduce weaving links[5] between a multidimensional data metamodel
(a simplified form of [14]) and an enterprise goal metamodel as shown in Fig. 4.
The links of the weaving model can be used to gain business metadata for the
DWH, such as in the example in Tab. 1.
1 1 *
Department Parameter Aggregation Level
1
1 * *
1..*
* 1 * * Measure
Person Metric Kla
Klasse_2
* anonym
1 1..* 1
0..1 *
* * 1
0..1
* 1 Fact
Goal Timeframe
1..*
2..* 1
* 1
Unit Target Value 1..2 Dimension
1
* Goal Metamodel Data Metamodel
Fig. 4. Three weaving links between enterprise goals and multidimensional metamodels
The central link in Fig. 4, connecting the Metric of a goal with a Measure
from the DWH (and optionally with an Aggregation Level ), can be explained
as follows: In the goal model, a metric measures the degree of fulfillment of a
goal (e.g. goal “reduce inventory cost” was reached to 80%. The metric with
its target value and timeframe is related to the corresponding DWH measure,
i.e. “inventory cost” of the Fact “Inventory”, which supplies the actual values.
When accessing the measure, the weaving link allows to access all the infor-
mation recorded in the enterprise goal model, e.g. who the metric is reported
to or which goal it corresponds to. The upper link relates aggregation levels to
the Parameters of a metric, whereas the third link connects the Timeframe of
a metric’s Target Value to the Dimensions containing temporal values in the
DWH.
There are many approaches to support DWH design with goal modeling [15,
16]. But, the goals analyzed in these cases are either goals of the DWH itself
(e.g. data quality, usefullness, availability) or goals of the DWH project (e.g.
timeliness), but not goals of the enterprise organization.
1166 CAiSE'06 Doctoral Consortium
Metric name: Reduction of inventory cost
Target value + unit: 100 Euro
Responsible + contact info: Ms. Smith, ext. 51564, ...
Reported to (person/dept.) + contact info: Ms. Baker, ext. 51324, ...
Goal supported by this metric: reduce inventory cost
Optional: Conflicting or supporting goals: conflict: "provide on-time delivery"
Table 1. Example business metadata for the measure “inventory cost”
7 Related Work
The approaches described in this PhD proposal are in line with requirements-
driven DWH design. Approaches to DWH design generally fall into two main cat-
egories[16, 17]. Data-driven (also supply-driven or bottom up) approaches focus
on the data sources that are available. The main question is how this data can be
extracted and transformed into a multidimensional data model. Requirements-
driven (also demand-driven or top down) approaches on the other hand instead
use the user requirements and enterprise goals as a starting point[18], and leave
the identification of data sources to a later phase.
Conceptual modeling in the area of Data Warehousing has largely focussed
on database related areas, namely the data model and schema transformations.
The main data model in Data Warehousing is the multidimensional model, also
called star schema[19]. It is meant to provide intuitive and high performance
data analysis[1]. There are many approaches to modeling the multidimensional
data structures of DWHs (for comparisons, see [20]). The structure of the data
model of a DWH is relevant to this work only in terms of relating and connecting
it to other models, in order to enrich the DWH with business metadata.
Linking DWH business metadata with technical metadata to provide a better
context for decision support was first suggested in [4]. Several business metadata
categories and a number of desirable characteristics are defined. The business
metadata is described with UML classes and associations and linked directly to
technical metadata within the same model. The approach only covers metadata
and does not include separate conceptual models of the business context.
8 Conclusion
DWH systems are used by decision makers for performance measurement and
decision support. Since the main focus of the research field is on database issues,
most effort has been put on improving on how the DWH works, and the question
how it is used has mostly been neglected so far.
In this thesis, I propose to use conceptual models for describing the relation-
ship between the DWH and the structure, behavior, and goals of the organiza-
tion, to increase the visibility of this relationship and to improve communication
by capturing this knowledge. Moreover, business metadata can be added to the
DWH that informs users about the context and background of the data, in order
to improve data interpretation.
CAiSE'06 DC 1167
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