A Multidimensional Pattern Based Approach for the Design of Data Marts

A Multidimensional Pattern Based Approach for the Design of Data Marts

Hanene Ben-Abdallah (University of Sfax, Tunisia), Jamel Feki (University of Sfax, Tunisia) and Mounira Ben Abdallah (University of Sfax, Tunisia)
DOI: 10.4018/978-1-60566-232-9.ch009
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Abstract

Despite their strategic importance, the wide-spread usage of decision support systems remains limited by both the complexity of their design and the lack of commercial design tools. This chapter addresses the design complexity of these systems. It proposes an approach for data mart design that is practical and that endorses the decision maker involvement in the design process. This approach adapts a development technique well established in the design of various complex systems for the design of data marts (DM): Pattern-based design. In the case of DM, a multidimensional pattern (MP) is a generic specification of analytical requirements within one domain. It is constructed and documented with standard, real-world entities (RWE) that describe information artifacts used or produced by the operational information systems (IS) of several enterprises. This documentation assists a decision maker in understanding the generic analytical solution; in addition, it guides the DM developer during the implementation phase. After over viewing our notion of MP and their construction method, this chapter details a reuse method composed of two adaptation levels: one logical and one physical. The logical level, which is independent of any data source model, allows a decision maker to adapt a given MP to their analytical requirements and to the RWE of their particular enterprise; this produces a DM schema. The physical specific level projects the RWE of the DM over the data source model. That is, the projection identifies the data source elements necessary to define the ETL procedures. We illustrate our approaches of construction and reuse of MP with examples in the medical domain.
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Introduction

Judicious decision making within an enterprise heavily relies nowadays on the ability to analyze large data volumes generated by the enterprise daily activities. To apprehend the difficulties and often impossibility of manual analyses of huge data volumes, decision makers have manifested a growing interest in installing decision support systems (DSS) on top of their computerized information systems (IS) (Kimball R. 1996). This interest triggered the proposition of several methods dealing with various phases of the DSS life cycle. However, two main difficulties impede the wide spread adoption of so far proposed methods. One difficulty stems from the fact that some methods presume that decision makers have a good expertise in IS modeling; this is the case of bottom-up DSS design methods (Golfarelli M., Maio D. & Rizzi S. 1998a), (Golfarelli M., Lechtenbörger J., Rizzi S. & Vossen G. 1998b), (Hüsemann, B., Lechtenbörger, J. & Vossen G. 2000), (Chen Y., Dehne F., Eavis T., & Rau-Chaplin A. 2006), (Cabibbo L. & Torlone R. 2000) and (Moody L. D. & Kortink M. A. R. 2000). The second difficulty is due to the fact that other methods rely on the ability of decision makers to define their analytical needs in a rigorous way that guarantees their loadability from the data in the operational IS; this is the case of top-down DSS design methods (Kimball 2002), (Tsois A., Karayannidis N. & Sellis T. 2001).

Independently of any design method and software tool used during its development, a DSS is typically organized into a data warehouse (DW) gathering all decisional information of the enterprise. In addition, to facilitate the manipulation of a DW, this latter is reorganized into data marts (DM) each of which representing a subject-oriented extract of the DW. Furthermore, a DM uses a multidimensional model that structures information into facts (interesting observations of a business process) and dimensions (the recording and analysis axes of observations). This model enables decision makers to write ad hoc queries and to manipulate/analyze easily the results of their queries (Chrisment C., Pujolle G., Ravat F., Teste O. & Zurfluh G. 2006).

Despite the advantages of this dedicated multidimensional model, the design of the DM schema remains a difficult task. Actually, it is a complex, technical process that requires a high expertise in data warehousing yet, it conditions the success and efficiency of the obtained DM.

The originality of the work presented in this chapter resides in proposing a DM design approach that relies on the reuse of generic OLAP requirement solutions we call multidimensional patterns (MP). In fact, reuse-based development is not a novel technique in itself; it has been applied for several application domains and through various techniques, e.g., design patterns (Gamma E., Helm R., Johnson J. & Vlissides J. 1999), components (Cheesman J. & Daniels J. 2000), and more recently the OMG model driven architecture (MDA) (OMG 2003). However, the application of reuse techniques in the design of DSS has not been well explored.

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Table of Contents
Preface
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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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About the Contributors