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More About This Textbook
Overview
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.
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
Product Details
Related Subjects
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.
Table of Contents
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