Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications / Edition 1

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Overview

A data warehouse stores large volumes of historical data required for analytical purposes. This data is extracted from operational databases; transformed into a coherent whole using a multidimensional model that includes measures, dimensions, and hierarchies; and loaded into a data warehouse during the extraction-transformation-loading (ETL) process.

Malinowski and Zimanyi explain in detail conventional data warehouse design, covering in particular complex hierarchy modeling. Additionally, they address two innovative domains recently introduced to extend the capabilities of data warehouse systems, namely the management of spatial and temporal information. Their presentation covers different phases of the design process, such as requirements specification, conceptual, logical, and physical design. They include three different approaches for requirements specification depending on whether users, operational data sources, or both are the driving force in the requirements gathering process, and they show how each approach leads to the creation of a conceptual multidimensional model. Throughout the book the concepts are illustrated using many real-world examples and completed by sample implementations for Microsoft's Analysis Services 2005 and Oracle 10g with the OLAP and the Spatial extensions.

For researchers this book serves as an introduction to the state of the art on data warehouse design, with many references to more detailed sources. Providing a clear and a concise presentation of the major concepts and results of data warehouse design, it can also be used as the basis of a graduate or advanced undergraduate course. The book may help experienced data warehouse designers toenlarge their analysis possibilities by incorporating spatial and temporal information. Finally, experts in spatial databases or in geographical information systems could benefit from the data warehouse vision for building innovative spatial analytical applications.

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Editorial Reviews

From the Publisher
"The origins of data warehousing are rooted in solid practicality. Data warehousing began as a response to the pain and frustration of the analytical/management community in large corporations.

It is thus a sign of maturation that a theoretical work has arisen that explains the theory behind data warehousing. This is that work.

The book is a well thought out. While not a breezy read, the book is nevertheless accessible to the common practitioner. One feature of the book is that it includes ample material on both traditional data warehousing and spatial and temporal data warehouses. The work on spatial and temporal data warehouses is an extension of current data warehouse thought and is welcome. In fact, it can be said that the heart of the book is the contributions on spatial and temporal data warehouses.

But there are many other features that are positive contributions as well. Equally covered are the relational model and the object relational model. The book fairly recognizes both the strengths and the weaknesses of the different model types and the book is quite fair in the criticism. (This is important because – for whatever reason – often times when models are discussed, the discussion often turns into a religious food fight, where each side professes that its model is the only true and righteous way. This book does not condescend to this low level of discussion, and that is one of the strengths of the book.)

I saw only one small passage that I took exception to in the book. The book states that data marts can be created directly from source systems. While this is true – such creations can be made – when they are made, the resulting structure is not a data warehouse. But this is a small point and does not detract from the other very positive contributions made by the book.

As one reads the chapters on the different types of structures that can be found in conventional, spatial and temporal data warehouses, there is a faint echo of the seminal works of Donald Knuth, who, decades earlier wrote the leading book on data structures. It is interesting to see how far data structures have evolved from the early days of Knuth to the sophisticated data warehouses of today.

One of the really nice features of this book is that it is readable. So many theoretical books get wrapped up in theory and conventions to the point that they are essentially unintelligible to the mere mortal. This book does a very nice job of merging theory with readability. One big thank you to the authors for this aspect of the book.

This book is a very welcome contribution to the body of knowledge surrounding data warehousing and analytics, and belongs on the bookshelf of every serious student and practitioner."

William H. "Bill" Inmon, Inmon Data Systems, Castle Rock, CO, USA - to be published in the Bill Inmon Newsletter by b-eye-network.com

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Product Details

  • ISBN-13: 9783540744047
  • Publisher: Springer Berlin Heidelberg
  • Publication date: 3/15/2008
  • Series: Data-Centric Systems and Applications Series
  • Edition description: 1st ed. 2008. Corr. 2nd printing 2008
  • Edition number: 1
  • Pages: 444
  • Product dimensions: 6.34 (w) x 9.37 (h) x 1.08 (d)

Meet the Author

Elzbieta Malinowski is a professor at the department of Computer and Information
Science at the Universidad de Costa Rica and a professional consultant in
Costa Rica in the area of the Data Warehousing. She received her master degrees
from Saint Petersburg Electrotechnical University, Russia (1982) and
University of Florida, USA (1996), and her Ph.D. degree from
Universitä© Libre de Bruxelles, Belgium (2006). Her research interests
include data warehouses, OLAP systems, geographic information systems,
and temporal databases.

Esteban Zimányi is a professor of computer science at the Engineering Department of the Université Libre de Bruxelles (ULB), Belgium. He received the BSc degree (1988) and the doctorate degree (1992) in computer science from the Sciences Department at the ULB. His current research interests include conceptual modeling, geographic information systems, spatio-temporal databases, and semantic web.

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Table of Contents

Introduction     1
Overview     2
Conventional Data Warehouses     2
Spatial Databases and Spatial Data Warehouses     4
Temporal Databases and Temporal Data Warehouses     5
Conceptual Modeling for Databases and Data Warehouses     6
A Method for Data Warehouse Design     7
Motivation for the Book     8
Objective of the Book and its Contributions to Research     11
Conventional Data Warehouses     12
Spatial Data Warehouses     13
Temporal Data Warehouses     13
Organization of the Book     14
Introduction to Databases and Data Warehouses     17
Database Concepts     18
The Entity-Relationship Model     19
Logical Database Design     23
The Relational Model     23
The Object-Relational Model     32
Physical Database Design     38
Data Warehouses     41
The Multidimensional Model     43
Hierarchies     44
Measure Aggregation     45
OLAP Operations     47
Logical Data Warehouse Design     49
Physical Data Warehouse Design     51
Data WarehouseArchitecture     55
Back-End Tier     56
Data Warehouse Tier     57
OLAP Tier     58
Front-End Tier     58
Variations of the Architecture     59
Analysis Services 2005     59
Defining an Analysis Services Database     60
Data Sources     61
Data Source Views     61
Dimensions     62
Cubes     64
Oracle 10g with the OLAP Option     66
Multidimensional Model     67
Multidimensional Database Design     68
Data Source Management     69
Dimensions     70
Cubes     71
Conclusion     73
Conventional Data Warehouses     75
MultiDim: A Conceptual Multidimensional Model     76
Data Warehouse Hierarchies     79
Simple Hierarchies     81
Nonstrict Hierarchies     88
Alternative Hierarchies     93
Parallel Hierarchies     94
Advanced Modeling Aspects     97
Modeling of Complex Hierarchies     97
Role-Playing Dimensions     100
Fact Dimensions     101
Multivalued Dimensions      101
Metamodel of the MultiDim Model     106
Mapping to the Relational and Object-Relational Models     107
Rationale     107
Mapping Rules     108
Logical Representation of Hierarchies     112
Simple Hierarchies     112
Nonstrict Hierarchies     120
Alternative Hierarchies     123
Parallel Hierarchies     123
Implementing Hierarchies     124
Hierarchies in Analysis Services 2005     124
Hierarchies in Oracle OLAP 10g     126
Related Work     128
Summary     130
Spatial Data Warehouses     133
Spatial Databases: General Concepts     134
Spatial Objects     134
Spatial Data Types     134
Reference Systems     136
Topological Relationships     136
Conceptual Models for Spatial Data     138
Implementation Models for Spatial Data     138
Models for Storing Collections of Spatial Objects     139
Architecture of Spatial Systems     140
Spatial Extension of the MultiDim Model     141
Spatial Levels     143
Spatial Hierarchies     143
Hierarchy Classification     143
Topological Relationships Between Spatial Levels     149
Spatial Fact Relationships     152
Spatiality and Measures     153
Spatial Measures     153
Conventional Measures Resulting from Spatial Operations     156
Metamodel of the Spatially Extended MultiDim Model     157
Rationale of the Logical-Level Representation     159
Using the Object-Relational Model     159
Using Spatial Extensions of DBMSs     160
Preserving Semantics     161
Object-Relational Representation of Spatial Data Warehouses     162
Spatial Levels     162
Spatial Attributes     164
Spatial Hierarchies     165
Spatial Fact Relationships     170
Measures     172
Summary of the Mapping Rules     174
Related Work     175
Summary     178
Temporal Data Warehouses     181
Slowly Changing Dimensions     182
Temporal Databases: General Concepts     185
Temporality Types     185
Temporal Data Types     186
Synchronization Relationships     187
Conceptual and Logical Models for Temporal Databases      189
Temporal Extension of the MultiDim Model     190
Temporality Types     190
Overview of the Model     192
Temporal Support for Levels     195
Temporal Hierarchies     196
Nontemporal Relationships Between Temporal Levels     196
Temporal Relationships Between Nontemporal Levels     198
Temporal Relationships Between Temporal Levels     198
Instant and Lifespan Cardinalities     199
Temporal Fact Relationships     201
Temporal Measures     202
Temporal Support for Measures     202
Measure Aggregation for Temporal Relationships     207
Managing Different Temporal Granularities     207
Conversion Between Granularities     208
Different Granularities in Measures and Dimensions     208
Different Granularities in the Source Systems and in the Data Warehouse     210
Metamodel of the Temporally Extended MultiDim Model     211
Rationale of the Logical-Level Representation     213
Logical Representation of Temporal Data Warehouses     214
Temporality Types     214
Levels with Temporal Support     216
Parent-Child Relationships     220
Fact Relationships and Temporal Measures     226
Summary of the Mapping Rules     228
Implementation Considerations     229
Integrity Constraints     229
Measure Aggregation     234
Related Work     237
Types of Temporal Support     237
Conceptual Models for Temporal Data Warehouses     238
Logical Representation     240
Temporal Granularity     241
Summary     242
Designing Conventional Data Warehouses     245
Current Approaches to Data Warehouse Design     246
Data Mart and Data Warehouse Design     246
Design Phases     248
Requirements Specification for Data Warehouse Design     248
A Method for Data Warehouse Design     250
A University Case Study     251
Requirements Specification     253
Analysis-Driven Approach     253
Source-Driven Approach     261
Analysis/Source-Driven Approach     265
Conceptual Design     265
Analysis-Driven Approach     266
Source-Driven Approach     275
Analysis/Source-Driven Approach     278
Characterization of the Various Approaches      280
Analysis-Driven Approach     280
Source-Driven Approach     282
Analysis/Source-Driven Approach     283
Logical Design     283
Logical Representation of Data Warehouse Schemas     283
Defining ETL Processes     287
Physical Design     288
Data Warehouse Schema Implementation     288
Implementation of ETL Processes     294
Method Summary     295
Analysis-Driven Approach     296
Source-Driven Approach     296
Analysis/Source-Driven Approach     297
Related Work     298
Overall Methods     300
Requirements Specification     301
Summary     305
Designing Spatial and Temporal Data Warehouses     307
Current Approaches to the Design of Spatial and Temporal Databases     308
A Risk Management Case Study     308
A Method for Spatial-Data-Warehouse Design     310
Requirements Specification and Conceptual Design     310
Logical and Physical Design     321
Revisiting the University Case Study     324
A Method for Temporal-Data-Warehouse Design     325
Requirements Specification and Conceptual Design      326
Logical and Physical Design     333
Method Summary     337
Analysis-Driven Approach     337
Source-Driven Approach     338
Analysis/Source-Driven Approach     339
Related Work     340
Summary     342
Conclusions and Future Work     345
Conclusions     345
Future Work     348
Conventional Data Warehouses     348
Spatial Data Warehouses     349
Temporal Data Warehouses     351
Spatiotemporal Data Warehouses     352
Design Methods     353
Formalization of the MultiDim Model     355
Notation     355
Predefined Data Types     355
Metavariables     356
Abstract Syntax     357
Examples Using the Abstract Syntax     359
Conventional Data Warehouse     359
Spatial Data Warehouse     361
Temporal Data Warehouse     364
Semantics     366
Semantics of the Predefined Data Types     367
The Space Model     367
The Time Model     371
Semantic Domains     372
Auxiliary Functions      372
Semantic Functions     375
Graphical Notation     383
Entity-Relationship Model     383
Relational and Object-Relational Models     385
Conventional Data Warehouses     386
Spatial Data Warehouses     388
Temporal Data Warehouses     389
References     391
Glossary     411
Index     425
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