Development of Data Warehouse Conceptual Models: Method Engineering Approach

Development of Data Warehouse Conceptual Models: Method Engineering Approach

Laila Niedrite (University of Latvia, Latvia), Maris Solodovnikova Treimanis (University of Latvia, Latvia) and Liga Grundmane (University of Latvia, Latvia)
DOI: 10.4018/978-1-60566-232-9.ch001
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There are many methods in the area of data warehousing to define requirements for the development of the most appropriate conceptual model of a data warehouse. There is no universal consensus about the best method, nor are there accepted standards for the conceptual modeling of data warehouses. Only few conceptual models have formally described methods how to get these models. Therefore, problems arise when in a particular data warehousing project, an appropriate development approach, and a corresponding method for the requirements elicitation, should be chosen and applied. Sometimes it is also necessary not only to use the existing methods, but also to provide new methods that are usable in particular development situations. It is necessary to represent these new methods formally, to ensure the appropriate usage of these methods in similar situations in the future. It is also necessary to define the contingency factors, which describe the situation where the method is usable.This chapter represents the usage of method engineering approach for the development of conceptual models of data warehouses. A set of contingency factors that determine the choice between the usage of an existing method and the necessity to develop a new one is defined. Three case studies are presented. Three new methods: userdriven, data-driven, and goal-driven are developed according to the situation in the particular projects and using the method engineering approach.
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Data warehouses are based on multidimensional models which contain the following elements: facts (the goal of the analysis), measures (quantitative data), dimensions (qualifying data), dimension attributes, classification hierarchies, levels of hierarchies (dimension attributes which form hierarchies), and attributes which describe levels of hierarchies of dimensions.

When it comes to the conceptual models of data warehouses, it is argued by many authors that the existing methods for conceptual modelling used for relational or object-oriented systems do not ensure sufficient support for the representation of multidimensional models in an intuitive way. Use of the aforementioned methods also ensures a waste of some of the semantics of multidimensional models. The necessary semantics must be added to the model informally, but that makes the model unsuitable for automatic transformation purposes. The conceptual models proposed by authors such as Sapia et al. (1998), Tryfona et al. (1999) and Lujan-Mora et al. (2002) are with various opportunities for expression, as can be seen in a comparison of the models in works such as (Blaschka et al., 1998), (Pedersen, 2000) and (Abello et al, 2001). This means that when a particular conceptual model is used for the modelling of data warehouses, some essential features may be missing. Lujan-Mora et al. (2002) argue that problems also occur because of the inaccurate interpretation of elements and features in the multidimensional model. They say that this applies to nearly all conceptual models that have been developed for data warehousing. The variety of elements and features in the conceptual models reflect differences in opinion about the best model for data warehouses, and that means that there is no universal agreement about the relevant standard (Rizzi et al., 2006).

There are two possible approaches towards the development of a conceptual model. One can be developed from scratch, which means additional work in terms of the formal description of the model’s elements. A model can also be developed by modifying an existing model so as to express the concepts of the multidimensional paradigm.

The conceptual models of data warehouses can be classified into several groups in accordance with how they are developed (Rizzi et al., 2006):

  • Models based on the E/R model, e.g., ME/R (Sapia et al., 1998) or StarE/R (Tryfona et al., 1999);

  • Models based on the UML., e.g., those using UML stereotypes (Lujan-Mora et al., 2002);

  • Independent conceptual models proposed by different authors, e.g., Dimensional Fact Model (Golfarelli et al., 1998).

In the data warehousing field there exists the metamodel standard for data warehouses - the Common Warehouse Metamodel (CWM). It is actually a set of several metamodels, which describe various aspects of data warehousing. CWM is a platform independent specification of metamodels (Poole et al., 2003) developed so as to ensure the exchange of metadata between different tools and platforms. The features of a multidimensional model are basically described via an analysis-level OLAP package, however, CWM cannot fully reflect the semantics of all conceptual multidimensional models (Rizzi et al., 2006).


Existing Methods For The Development Of Conceptual Models For Data Warehouses

There are several approaches to learn the requirements for a conceptual data warehouse model and to determine how the relevant model can be built. Classification of these approaches is presented in this section, along with an overview of methods, which exist in each approach. Weaknesses of the approaches are analysed to show the necessity to develop new methods. The positive aspects of existing approaches and the existence of many methods in each approach, however, suggests that several method components can be used in an appropriate situation.

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Editorial Advisory Board
Table of Contents
David Taniar
Chapter 1
Laila Niedrite, Maris Solodovnikova Treimanis, Liga Grundmane
There are many methods in the area of data warehousing to define requirements for the development of the most appropriate conceptual model of a data... Sample PDF
Development of Data Warehouse Conceptual Models: Method Engineering Approach
Chapter 2
Stefano Rizzi
In the context of data warehouse design, a basic role is played by conceptual modeling, that provides a higher level of abstraction in describing... Sample PDF
Conceptual Modeling Solutions for the Data Warehouse
Chapter 3
Hamid Haidarian Shahri
Entity resolution (also known as duplicate elimination) is an important part of the data cleaning process, especially in data integration and... Sample PDF
A Machine Learning Approach to Data Cleaning in Databases and Data Warehouses
Chapter 4
Maurizio Pighin, Lucio Ieronutti
Data Warehouses are increasingly used by commercial organizations to extract, from a huge amount of transactional data, concise information useful... Sample PDF
Interactive Quality-Oriented Data Warehouse Development
Chapter 5
Dirk Draheim, Oscar Mangisengi
Nowadays tracking data from activity checkpoints of unit transactions within an organization’s business processes becomes an important data resource... Sample PDF
Integrated Business and Production Process Data Warehousing
Chapter 6
Jorge Loureiro, Orlando Belo
OLAP queries are characterized by short answering times. Materialized cube views, a pre-aggregation and storage of group-by values, are one of the... Sample PDF
Selecting and Allocating Cubes in Multi-Node OLAP Systems: An Evolutionary Approach
Chapter 7
Jorge Loureiro, Orlando Belo
Globalization and market deregulation has increased business competition, which imposed OLAP data and technologies as one of the great enterprise’s... Sample PDF
Swarm Quant' Intelligence for Optimizing Multi-Node OLAP Systems
Chapter 8
Franck Ravat, Olivier Teste, Ronan Tournier
With the emergence of Semi-structured data format (such as XML), the storage of documents in centralised facilities appeared as a natural adaptation... Sample PDF
Multidimensional Anlaysis of XML Document Contents with OLAP Dimensions
Chapter 9
Hanene Ben-Abdallah, Jamel Feki, Mounira Ben Abdallah
Despite their strategic importance, the wide-spread usage of decision support systems remains limited by both the complexity of their design and the... Sample PDF
A Multidimensional Pattern Based Approach for the Design of Data Marts
Chapter 10
Concepción M. Gascueña, Rafael Guadalupe
The Multidimensional Databases (MDB) are used in the Decision Support Systems (DSS) and in Geographic Information Systems (GIS); the latter locates... Sample PDF
A Multidimensional Methodology with Support for Spatio-Temporal Multigranularity in the Conceptual and Logical Phases
Chapter 11
Francisco Araque, Alberto Salguero, Cecilia Delgado
One of the most complex issues of the integration and transformation interface is the case where there are multiple sources for a single data... Sample PDF
Methodology for Improving Data Warehouse Design using Data Sources Temporal Metadata
Chapter 12
Shi-Ming Huang, John Tait, Chun-Hao Su, Chih-Fong Tsai
Data warehousing is a popular technology, which aims at improving decision-making ability. As the result of an increasingly competitive environment... Sample PDF
Using Active Rules to Maintain Data Consistency in Data Warehouse Systems
Chapter 13
Marcin Gorawski, Wojciech Gebczyk
This chapter describes realization of distributed approach to continuous queries with kNN join processing in the spatial telemetric data warehouse.... Sample PDF
Distributed Approach to Continuous Queries with kNN Join Processing in Spatial Telemetric Data Warehouse
Chapter 14
Maria Luisa Damiani, Stefano Spaccapietra
This chapter is concerned with multidimensional data models for spatial data warehouses. Over the last few years different approaches have been... Sample PDF
Spatial Data Warehouse Modelling
Chapter 15
Jérôme Darmont
Performance evaluation is a key issue for designers and users of Database Management Systems (DBMSs). Performance is generally assessed with... Sample PDF
Data Warehouse Benchmarking with DWEB
Chapter 16
Lars Frank, Christian Frank
A Star Schema Data Warehouse looks like a star with a central, so-called fact table, in the middle, surrounded by so-called dimension tables with... Sample PDF
Analyses and Evaluation of Responses to Slowly Changing Dimensions in Data Warehouses
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