PROCESS REFERENCE MODEL FOR DATA WAREHOUSE

DEVELOPMENT

A consensus-oriented approach

Ralf Knackstedt, Karsten Klose, Björn Niehaves, Jörg Becker

European Research Center for Information Systems, University of Muenster, Leonardo-Campus 3, Münster, Germany

Keywords: Data Warehouse development, Management Information Systems, Process Reference Model, Theoretical

Foundation, Epistemology

Abstract: IS literature provides a variety of Data Warehouse development methodologies focussing on technical

issues, for instance the automatical generation of Data Warehouse or OLAP schemata from conceptual

graphical models or the materialization of views. On the other hand, we can observe a growing influence of

conceptual modelling in the move of general IS development which is specifically addressing early phase

design issues. Here, conceptual modelling solves communicational problems which emerge when for

instance IT personnel and business personnel work together, mostly having distinct educational and

professional backgrounds as well as using distinct domain languages. Thus, the aim of this paper is to

provide the foundation of a Data Warehouse development methodology in form of a process reference

model which is based on a conceptual modelling approach.

1 INTRODUCTION

A broad variety of methods and procedural or phase

models aims at supporting the Data Warehouse

development process (Poe 1996, Hammergren 1996,

Hackney 1997, Devlin 1997, Inmon & Imhoff &

Sousa 1998, Golfarelli & Rizzi 1999). Nevertheless,

the lack of epistemological funding of research

methods and methodologies is apparent and

extensively discussed in the IS discipline (cp. for

example Hirschheim & Klein & Lyytinen 1995,

Keen 1980, Mingers 2001). Therefore, the aim of

this paper is do provide a process reference model, a

methodology, for Data Warehouse development

under special consideration of its theoretical-

epistemological fundaments.

We now specifically analyzed the consensus-

oriented approach on conceptual modeling

(Niehaves & Klose & Knackstedt & Becker, 2005)

which is characterized by an interpretivist position,

which is mainly colored by the critical linguistic

approach of (Kamlah & Lorenzen 1973). The

information models developed contain formalized

linguistic statements to be tested for validity in

combination with additional (empirical) research

methods. This is done through members of a

linguistic community in order to obtain consensus.

Therefore, elements of the semantic theory of truth

(Tarski 1944) and the consensus theory of truth are

considered and used.

In Section 2 we introduce the consensus oriented

approach. In Section 3 we specify certain elements

of the consensus-oriented approach and

operationalize them in the form of a research process

model. By this means, we are able to elucidate the

consensus-oriented approach to a Data Warehouse

development methodology in particular. We

conclude with a summary and an outlook in Section

2 THE CONSENSUS ORIENTED

APPROACH

The consensus oriented approach as a underlying

paradigm of our reference process model of data

warehouse development is related with the

assumption that there is a real world existing

independently from human speech and thinking

processes. Thus, we assume the ontological realism.

The approach aims to create a linguistic

community. Linguistic communities can be created

through the (re)construction of an ortho-language.

First parts of the language can be formed by the

alignment of individual (real world) objects to

nominators. In the context of IS-development

499

Knackstedt R., Klose K., Niehaves B. and Becker J. (2005).

PROCESS REFERENCE MODEL FOR DATA WAREHOUSE DEVELOPMENT - A consensus-or iented approach.

In Proceedings of the Seventh International Conference on Enterprise Information Systems, pages 499-505

DOI: 10.5220/0002534104990505

 SciTePress

important nominators are terms such as ‘customer

Meier’, ‘product 4711’ etc. Based on nominators,

predicators (in our context e. g. ‘customer’ and

‘product’) are introduced in order to expose and

communicate similarities of individual objects.

Language has in this case immense impact on a

subject’s perceptional processes. It defines the very

basic perception and differentiation system. Shared

language means shared conceptualizations about the

real world among the members of a particular

linguistic community.

In the move of the consensus-oriented approach

to Data Warehouse development, two distinct

languages come into play: particular conceptual

modelling languages as well as natural languages.

Following Tarski (Tarski 1944) the creation of the

linguistic community takes place on two levels. On

the first level (here named T* object language)

conceptual model statements are expressed. For

instance, using Entity Relationship Models (ERM)

members of the linguistic community have to agree

upon the term ‘entity type’; in the case of Event

Driven Process Chains (EPC) they have to agree

upon the term ‘event’. Moreover, a distinction

between a) the language of model instances and b)

the language of the modeling method and technique

has to be made. On the second level (here named T*

meta language) members of the community have to

agree on a language which facilitates them to debate

about the truth and nontruth of the statements

represented in a model (including for instance

German or English). In the next step, the meta

language T* is used to discuss the modeling system

which is formulated on the first level using the T*

object language until a consensus of a group of

experts is achieved. Afterwards, the results can be

evaluated within the scope of the interpersonal

verification (Kamlah & Lorenzen 1973, Kamlah &

Lorenzen 1996). The formalized linguistic

statements contained in a conceptual model are

logically decomposed (deduction) until they are

accessible as elemental statements for purposes of

truth verification. This takes place by means of a

group of experts who obtain a consensus. The main

instruments are observation, experiments,

interviewing and the interpretation of texts (Kamlah

& Lorenzen 1996). The validity of statements in the

model can be confirmed, for example, in the case of

business specific models, with a single case. In case

of a pattern or reference model, however, the

generalized abstraction of different individual

verifications (induction) is necessary. Here, research

methods such as field experiments, surveys, case

studies or action research can be applied. Based on

these results a revision of the conceptual model is

required.

Furthermore, the consensus oriented approach

results in the following epistemological positions:

Both empirical statements (Kamlah & Lorenzen

1996) and a priori statements can be made, which

may form the basis of conceptual models.

Conceptual modeling therefore derives its results via

theoretical reflection of the model contents, as well

as from the implementation of the model in

information systems and through observation.

Conceptual models are one form of artefacts of a

formalized language and can contain both empirical

and a priori knowledge. Both inductive and

deductive conclusions can be accessed firstly in the

context of the model creation and secondly in the

context of truth verification.

3 DATA WAREHOUSE

DEVELOPMENT METHOD

The consensus-oriented information modelling

approach provides a suitable foundation for a

methodological framework for the specification of

Data Warehouse systems. In terms of a process

reference model, types of tasks and procedure

recommendations can be identified. Thereby, the

creation of a linguistic community requires several

steps which have to be performed:

On the T* object language level a selection,

modification or development of an appropriate

modelling language is necessary. Moreover, it has to

be ensured that all project members have a common

understanding of the model constructs used. The

language-critique approach requires an explicit and

consistent introduction of terms. At the outset, basic

terms are introduced. Their definitions are based on

familiar terms which are shared in the understanding

of all project members. Normally, such generally

known terms can be taken from natural language.

Based on those terms, other terms are reconstructed

stepwise (explicitly introduced) unless it can be

assumed that their meaning is naturally known.

Thus, the terms received from the normalised

language are based on each other. But the derivation

of a term A must not use terms B which in turn

requires an introduction of term A. Hence, it is

necessary to ensure that terms are not reused in

different meanings.

For the multi-dimensional specification of Data

Warehouse requirements, a broad variety of

modelling techniques exists. The documentation of

multi-dimensional modelling techniques is often not

totally conforming to the demand of the language-

critique approach. For example, the introduction of

the modelling technique ADAPT of Bulos (Bulos

ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION

500

1996) (as many more do as well) emphasises a

detailed explanation of notation aspects in the form

of model examples and symbol lists. However, a

step-by-step introduction of the modelling technique

language constructs and their corresponding

documentation in the form of language-oriented

meta models is increasingly used and more and more

applied in the domain of Data Warehousing (for

example in the form of Entity-Relationship-Models

and additional textual explanations for the intended

semantics of the model elements).

An application of the language-critique approach

which can easily be transferred to the modelling of

Entity Relationship Models has been developed by

Wedekind (Wedekind, 1992) for example. Among

other things, he proposes the usage of the construct

operators subsumption, subordination, and

composition.

Using construct operators, core terms of Data

Warehousing can systematically be introduced. This

is presented by Holten by means of a modeling

technique for the specification of management views

in information warehouse projects (Holten 2003), for

example. Referring to his modelling technique the

following modelling constructs can be identified as

highly relevant in the context of Data Warehousing.

According to (Riebel 1979) Dimension Objects

are defined as all entities which can be related to a

decision in a business process (such as products or

customers). With respect to the analysing purpose,

Dimension Objects are assigned to Dimensions,

which arrange objects hierarchically (e.g. for the

analysis of product groups). Hierarchies can be

divided in several Hierarchy levels. Dimensions that

comprise identical Dimension Objects as leaf

elements are combined to Dimension Groupings.

Dimension Scopes represent the selection of several

Dimension Objects from a Dimension. A Dimension

Scope Combination can be regarded as a navigation

space of Dimension Objects that can be analysed by

the operations aggregation and disaggregation with

respect to the hierarchies of the combined

Dimension Scopes. Ratios define important aspects

of Dimension Objects such as invoice and payment

amount, gross margin, profitability, etc. and can be

organised in Ratio Systems, which define selected

relationships between Ratios in a mathematical or

business-logical sense. As business information can

not be expressed exclusively based on Ratios or

Dimension Objects, they are combined to

Information Objects. They describe the amount of

data (as combinations of Ratios and Dimension

Objects which is called Fact), a manager should

analyse by Dimensions and Ratio Systems.

A comparison of core terms used in the

modelling technique by Holten with terms used in

other modelling techniques (cp. Figure 1)

emphasises the necessity of the construction of a

language community (cp. Holten 2003, Holten &

Dreiling & Schmid 2002 [MetaMIS], Sapia &

Blaschka & Höfling & Dinter 1998, Hahn & Sapia

& Blaschka 2000 [ME/RM], Bulos 1996 [ADAPT],

Golfarelli & Maio & Rizzi 1998, Golfarelli & Rizzi

1999 [DFM]). On the one hand, the comparison

underlines that identical modelling constructs are

named in different ways (synonyms). On the other

hand, different modelling constructs are allocated

with idem terms (homonyms). Furthermore, several

modelling constructs remain generally unconsidered

or dissimilar considered (for example in

combination or in a multiple application of several

constructs) in the different modelling techniques.

In case of a missing general understanding of

terms used on instance level (for example the

product lines ‘food’ and ‘non-food’), an introduction

of these terms (in particular dimensions and ratios or

ratio systems) by means of the language-critique

approach is necessary as well. Synonyms and

homonyms especially occur with respect to ratios in

different enterprise departments (for example

turnover with or without taxes). For the construction

of language communities, glossaries and rules for

the determination of ratios (developed in consensus)

should be an integral element of a conceptual

modelling technique.

Moreover, the construction of a linguistic

community comprises an agreement on a language

which makes it possible to discuss about the new

developed or modified modelling language and its

model artefacts. Normally, a selection of a natural

language such as English, Spanish or German is

sufficient. Against the background of consensus-

oriented modelling, this language is called T* meta

language. In the context of Data Warehouse systems

specification, using T* meta language mainly aims

to achieve a consensus between project members

about necessary information needs.

Figure 1: Comparison of Data Warehouse Modelling

Techniques

PROCESS REFERENCE MODEL FOR DATA WAREHOUSE DEVELOPMENT - A consensus-oriented approach

501

In the context of consensus building, consensus-

oriented modelling requires that project members

speak the same language. Moreover, they have to be

‘rational’ and ‘competent’ in the project domain (cp

in detail Kamlah & Lorenzen 1996).

In the context of Data Warehouse development,

especially a consensus about information needs is

required. Literature debates of information need

analysis are often based on a model including

several overlapping circles (cp. Figure 2(a)).

Following Szyperski (Szyperski 1980), the first

circle represents the amount of information which is

actually available in an organization. The second

circle represents the amount of information

‘objectively’ needed for decision making or task

performing. The third circle comprises the amount

of information a user considers ‘subjectively’ to be

relevant for his/her task. Within the third circle,

another circle exists which represents the amount of

information that is explicitly demanded and

articulated by a user. The model implicates that

information represented in the first circle

(information demanded by managers) and third

circle (the available information) should be modified

with respect to the “objective” information need (cp.

the two arrows in Figure 2(a)).

In the context of consensus-oriented modelling, a

discussion and representation of an ‘objective’

information need is inappropriate, since in Data

Warehouse projects exclusively information needs

articulated and identified by project members are

relevant. Following our presented epistemological

position, this information need has to be interpreted

as ‘subjective’. Thereby, a consensus about the

“subjective” information need is required. On the

one hand, different perceptions are based on

dissimilar individual experiences of project

members. On the other hand, they are particularly

based on the methodical foundation of the

formulated information needs. In this context,

amongst others, the following three approaches can

be distinguished (cp. Figure 2(b)):

• End user involvement: The participation of end

users in the Data Warehouse development

process is indispensable. Their involvement

ensures the acceptance of the Data Warehouse

system through the consideration of individual

preferences and habits (for example the

understanding of ratios). Manager often the

information overload problem as they often

demand the total amount of data available which

is referred to a certain topic (Ackoff 1967). The

differentiation between identified and articulated

information needs made by Szysperski indicates

that not only methodical conditions for an

analysis of management information needs (for

example in the form of observations, interviews,

and surveys) have to be established.

Furthermore, suitable cultural conditions are

required which facilitate and motivate Data

Warehouse users to express their information

needs.

• Methods for specifications of information needs:

The development of methods for a theoretically

funded identification and specification of

information needs has been a major issue in

information systems research in the last decades.

Several information requirements engineering

methods and approaches (especially in the MIS

domain) have been developed and evaluated

(Martin 1983, Munro & Davis 1977, Sethi &

Teng 1988, Rockart 1979). Nevertheless, the

problem of information requirements engineering

is considered to have deficiencies in theory.

• Use of reference models: Reference models are

increasingly applied in the requirements

Figure 2: Information need analysis

Information supply

(currently available internal and

external data sources)

Subjective information need

(Information considered to be

relevant by the problem solver)

Information request

(actual information requested by

managers)

Objective information need

(problem-specific required

Information)

Information need formulated by

managers based on experiences

Information supply

(currently available internal and

external data sources)

Information need derived from

theoretical approaches (e.g. goal,

task or process analysis)

Information need derived from

reference models

Information need consolidated in

consensus

Extendable list of

information need

specifications by

„rational“ and

„competent“

persons

(a)

(b)

ICEIS 2005 - INFORMATION SYSTEMS ANALYSIS AND SPECIFICATION

502

specification phase of Data Warehouse Projects.

Reference models provide useful means to

reduce the effort of information modelling,

because they can be used as a starting point for

the construction of project-specific models.

Thus, reference models provide best (or

common) practice solutions for information

modelling projects. By means of reference

models, opinions and experiences of external

experts concerning the design of Data

Warehouse systems are additionally and

indirectly involved. The efficiency and

effectivity of reference model applications can be

increased by the use of configurative reference

models.

Accounting for different advantages and

disadvantages of the presented approaches, we

propose a multi-methodological procedure. By this

means, the acceptance of the Data Warehouse is

ensured through the involvement of end users.

Moreover, we overcome restrictions of an

information need which is exclusively formulated by

managers with an extended consideration of

methods for the specification of information needs.

Finally, reference models extend the (indirect)

participation of additional experts.

Following the consensus oriented approach, a

verification of the consensus consolidated is needed.

For an inter-personal verification in the context of

Data Warehouse projects, several artefacts

(distinguishable in granularity and implementation

orientation) can be taken into account. One

possibility is to decompose requirements

specification models in single statements.

Afterwards, these statements are verified (for

example relationships within dimensions of multi-

dimensional models or the consistence of ratio

formulations).

Another possibility is to implement a complete

Data Warehouse system on basis of requirements

specification models. This realisation may result in

the fact that there is no longer a consensus about

certain parts of the requirements specification

models. Instead of a complete implementation of the

Data Warehouse system, usually prototypes are

developed and realised for verification purposes.

Based on the verification, it may be necessary to

modify the information modelling results. These

modifications may affect specific information

models but also selected modelling methods (for

example due to the fact that additional

representations are required). Therefore, the T*

object language is used again. For the discussion of

necessary modifications, the T* meta language is

applied.

4 SUMMARY AND OUTLOOK

We summarise our results concerning the

development of Data Warehouse systems based on

Figure

Process reference model for Dat

a Warehouse development.

(1) Selection of

T* meta language

Documentation of T*

meta language

selection

(2) (5)

Development of T*

object language

Meta models,

glossary of the

domain language

(3) (8) Definition of

project goals

Project goals

(4) (8)

Engineering of

manager

information needs

Data Warehouse

requirements

specification based

on experiences

of managers

(4) (8) Appli-

cation of theroetical

approaches (goal,

tasks or process

analysis)

Data Warehouse

requirements

specification based

on information need

engineering

approaches

(4) (8)

Identification and

adaptation of

reference models

Adaptaded reference

model

… …

(6) Consolidation

of consensus with

respect to

information needs

Consolidated

Data Warehouse

model

(7) Verification of

Data Warehouse

model

Modification

requirements

Data Warehouse

System

Documentation of the

verification of single

statements

Data Warehouse

prototype

PROCESS REFERENCE MODEL FOR DATA WAREHOUSE DEVELOPMENT - A consensus-oriented approach

503

the consenus-oriented approach in terms of a process

reference model in Figure 3. Ovals represent types

of tasks. Rectangles symbolise types of documents

which are assigned to tasks. Documents can be

outputs which are created by tasks or represent

inputs which are used for the accomplishment of

tasks. Thereby, types of tasks may comprise other

tasks. Numbers assigned to tasks illustrate the

(reading) order of the process reference model.

However, several tasks are cross-sectional since they

are passed through more than once.

The project goal needs to be defined in advance

to the requirements specification. It facilitates the

coordination of parallel information need

engineering. Moreover, it particularly describes the

context of management tasks that should be

supported by the Data Warehouse system. The

project goal definition itself is a model which is

represented in the T* object language which requires

the development of a language community as well.

Our framework emphasises the phase of

conceptual modelling of Data Warehouse projects.

Logical and physical aspects are only addressed in

the context of the interpersonal verification. Thus,

our approach has to be combined with other works,

which stress logical and physical aspects of Data

Warehouse development.

In comparison with other existing Data

Warehousing procedure model approaches, the

presented framework uses the consensus-oriented

approach of conceptual modelling as a specific

theoretical foundation. Instead of practical

argumentation or mathematical deductions, our

approach is based on the language-critique

philosophical work of Kamlah and Lorenzen. Their

work is used as a basis, because it comprehensively

addresses the communication problems between

Data Warehouse project members. Furthermore, our

approach emphasises the explication of its

underlying epistemological assumptions, which is

associated with the definition of the consensus-

oriented modelling approach (cp. Niehaves et al.,

2005; Niehaves, 2004).

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