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Advanced Data Warehouse Design: From Conventional to Spatial and Temporal Applications 1st ed. 2008. Corr. 2nd printing 2008 [Hardback]

  • Formāts: Hardback, 435 pages, height x width: 235x155 mm, weight: 859 g, XXI, 435 p., 1 Hardback
  • Sērija : Data-Centric Systems and Applications
  • Izdošanas datums: 15-Jan-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540744045
  • ISBN-13: 9783540744047
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  • Formāts: Hardback, 435 pages, height x width: 235x155 mm, weight: 859 g, XXI, 435 p., 1 Hardback
  • Sērija : Data-Centric Systems and Applications
  • Izdošanas datums: 15-Jan-2008
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3540744045
  • ISBN-13: 9783540744047
Citas grāmatas par šo tēmu:
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 Zimįnyi 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 to enlarge 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.

Recenzijas

"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 thebook.









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

1 Introduction 1
1.1 Overview
2
1.1.1 Conventional Data Warehouses
2
1.1.2 Spatial Databases and Spatial Data Warehouses
4
1.1.3 Temporal Databases and Temporal Data Warehouses
5
1.1.4 Conceptual Modeling for Databases and Data Warehouses
6
1.1.5 A Method for Data Warehouse Design
7
1.2 Motivation for the Book
8
1.3 Objective of the Book and its Contributions to Research
11
1.3.1 Conventional Data Warehouses
12
1.3.2 Spatial Data Warehouses
13
1.3.3 Temporal Data Warehouses
13
1.4 Organization of the Book
14
2 Introduction to Databases and Data Warehouses 17
2.1 Database Concepts
18
2.2 The Entity-Relationship Model
19
2.3 Logical Database Design
23
2.3.1 The Relational Model
23
2.3.2 The Object-Relational Model
32
2.4 Physical Database Design
38
2.5 Data Warehouses
41
2.6 The Multidimensional Model
43
2.6.1 Hierarchies
44
2.6.2 Measure Aggregation
45
2.6.3 OLAP Operations
47
2.7 Logical Data Warehouse Design
49
2.8 Physical Data Warehouse Design
51
2.9 Data Warehouse Architecture
55
2.9.1 Back-End Tier
56
2.9.2 Data Warehouse Tier
57
2.9.3 OLAP Tier
58
2.9.4 Front-End Tier
58
2.9.5 Variations of the Architecture
59
2.10 Analysis Services 2005
59
2.10.1 Defining an Analysis Services Database
60
2.10.2 Data Sources
61
2.10.3 Data Source Views
61
2.10.4 Dimensions
62
2.10.5 Cubes
64
2.11 Oracle 10g with the OLAP Option
66
2.11.1 Multidimensional Model
67
2.11.2 Multidimensional Database Design
68
2.11.3 Data Source Management
69
2.11.4 Dimensions
70
2.11.5 Cubes
71
2.12 Conclusion
73
3 Conventional Data Warehouses 75
3.1 MultiDim: A Conceptual Multidimensional Model
76
3.2 Data Warehouse Hierarchies
79
3.2.1 Simple Hierarchies
81
3.2.2 Nonstrict Hierarchies
88
3.2.3 Alternative Hierarchies
93
3.2.4 Parallel Hierarchies
94
3.3 Advanced Modeling Aspects
97
3.3.1 Modeling of Complex Hierarchies
97
3.3.2 Role-Playing Dimensions
100
3.3.3 Fact Dimensions
101
3.3.4 Multivalued Dimensions
101
3.4 Metamodel of the MultiDim Model
106
3.5 Mapping to the Relational and Object-Relational Models
107
3.5.1 Rationale
107
3.5.2 Mapping Rules
108
3.6 Logical Representation of Hierarchies
112
3.6.1 Simple Hierarchies
112
3.6.2 Nonstrict Hierarchies
120
3.6.3 Alternative Hierarchies
123
3.6.4 Parallel Hierarchies
123
3.7 Implementing Hierarchies
124
3.7.1 Hierarchies in Analysis Services 2005
124
3.7.2 Hierarchies in Oracle OLAP 10g
126
3.8 Related Work
128
3.9 Summary
130
4 Spatial Data Warehouses 133
4.1 Spatial Databases: General Concepts
134
4.1.1 Spatial Objects
134
4.1.2 Spatial Data Types
134
4.1.3 Reference Systems
136
4.1.4 Topological Relationships
136
4.1.5 Conceptual Models for Spatial Data
138
4.1.6 Implementation Models for Spatial Data
138
4.1.7 Models for Storing Collections of Spatial Objects
139
4.1.8 Architecture of Spatial Systems
140
4.2 Spatial Extension of the MultiDim Model
141
4.3 Spatial Levels
143
4.4 Spatial Hierarchies
143
4.4.1 Hierarchy Classification
143
4.4.2 Topological Relationships Between Spatial Levels
149
4.5 Spatial Fact Relationships
152
4.6 Spatiality and Measures
153
4.6.1 Spatial Measures
153
4.6.2 Conventional Measures Resulting from Spatial Operations
156
4.7 Metamodel of the Spatially Extended MultiDim Model
157
4.8 Rationale of the Logical-Level Representation
159
4.8.1 Using the Object-Relational Model
159
4.8.2 Using Spatial Extensions of DBMSs
160
4.8.3 Preserving Semantics
161
4.9 Object-Relational Representation of Spatial Data Warehouses
162
4.9.1 Spatial Levels
162
4.9.2 Spatial Attributes
164
4.9.3 Spatial Hierarchies
165
4.9.4 Spatial Fact Relationships
170
4.9.5 Measures
172
4.10 Summary of the Mapping Rules
174
4.11 Related Work
175
4.12 Summary
178
5 Temporal Data Warehouses 181
5.1 Slowly Changing Dimensions
182
5.2 Temporal Databases: General Concepts
185
5.2.1 Temporality Types
185
5.2.2 Temporal Data Types
186
5.2.3 Synchronization Relationships
187
5.2.4 Conceptual and Logical Models for Temporal Databases
189
5.3 Temporal Extension of the MultiDim Model
190
5.3.1 Temporality Types
190
5.3.2 Overview of the Model
192
5.4 Temporal Support for Levels
195
5.5 Temporal Hierarchies
196
5.5.1 Nontemporal Relationships Between Temporal Levels
196
5.5.2 Temporal Relationships Between Nontemporal Levels
198
5.5.3 Temporal Relationships Between Temporal Levels
198
5.5.4 Instant and Lifespan Cardinalities
199
5.6 Temporal Fact Relationships
201
5.7 Temporal Measures
202
5.7.1 Temporal Support for Measures
202
5.7.2 Measure Aggregation for Temporal Relationships
207
5.8 Managing Different Temporal Granularities
207
5.8.1 Conversion Between Granularities
208
5.8.2 Different Granularities in Measures and Dimensions
208
5.8.3 Different Granularities in the Source Systems and in the Data Warehouse
210
5.9 Metamodel of the Temporally Extended MultiDim Model
211
5.10 Rationale of the Logical-Level Representation
213
5.11 Logical Representation of Temporal Data Warehouses
214
5.11.1 Temporality Types
214
5.11.2 Levels with Temporal Support
216
5.11.3 Parent-Child Relationships
220
5.11.4 Fact Relationships and Temporal Measures
226
5.12 Summary of the Mapping Rules
228
5.13 Implementation Considerations
229
5.13.1 Integrity Constraints
229
5.13.2 Measure Aggregation
234
5.14 Related Work
237
5.14.1 Types of Temporal Support
237
5.14.2 Conceptual Models for Temporal Data Warehouses
238
5.14.3 Logical Representation
240
5.14.4 Temporal Granularity
241
5.15 Summary
242
6 Designing Conventional Data Warehouses 245
6.1 Current Approaches to Data Warehouse Design
246
6.1.1 Data Mart and Data Warehouse Design
246
6.1.2 Design Phases
248
6.1.3 Requirements Specification for Data Warehouse Design
248
6.2 A Method for Data Warehouse Design
250
6.3 A University Case Study
251
6.4 Requirements Specification
253
6.4.1 Analysis-Driven Approach
253
6.4.2 Source-Driven Approach
261
6.4.3 Analysis/Source-Driven Approach
265
6.5 Conceptual Design
265
6.5.1 Analysis-Driven Approach
266
6.5.2 Source-Driven Approach
275
6.5.3 Analysis/Source-Driven Approach
278
6.6 Characterization of the Various Approaches
280
6.6.1 Analysis-Driven Approach
280
6.6.2 Source-Driven Approach
282
6.6.3 Analysis/Source-Driven Approach
283
6.7 Logical Design
283
6.7.1 Logical Representation of Data Warehouse Schemas
283
6.7.2 Defining ETL Processes
287
6.8 Physical Design
288
6.8.1 Data Warehouse Schema Implementation
288
6.8.2 Implementation of ETL Processes
294
6.9 Method Summary
295
6.9.1 Analysis-Driven Approach
296
6.9.2 Source-Driven Approach
296
6.9.3 Analysis/Source-Driven Approach
297
6.10 Related Work
298
6.10.1 Overall Methods
300
6.10.2 Requirements Specification
301
6.11 Summary
305
7 Designing Spatial and Temporal Data Warehouses 307
7.1 Current Approaches to the Design of Spatial and Temporal Databases
308
7.2 A Risk Management Case Study
308
7.3 A Method for Spatial-Data-Warehouse Design
310
7.3.1 Requirements Specification and Conceptual Design
310
7.3.2 Logical and Physical Design
321
7.4 Revisiting the University Case Study
324
7.5 A Method for Temporal-Data-Warehouse Design
325
7.5.1 Requirements Specification and Conceptual Design
326
7.5.2 Logical and Physical Design
333
7.6 Method Summary
337
7.6.1 Analysis-Driven Approach
337
7.6.2 Source-Driven Approach
338
7.6.3 Analysis/Source-Driven Approach
339
7.7 Related Work
340
7.8 Summary
342
8 Conclusions and Future Work 345
8.1 Conclusions
345
8.2 Future Work
348
8.2.1 Conventional Data Warehouses
348
8.2.2 Spatial Data Warehouses
349
8.2.3 Temporal Data Warehouses
351
8.2.4 Spatiotemporal Data Warehouses
352
8.2.5 Design Methods
353
A Formalization of the MultiDim Model 355
A.1 Notation
355
A.2 Predefined Data Types
355
A.3 Metavariables
356
A.4 Abstract Syntax
357
A.5 Examples Using the Abstract Syntax
359
A.5.1 Conventional Data Warehouse
359
A.5.2 Spatial Data Warehouse
361
A.5.3 Temporal Data Warehouse
364
A.6 Semantics
366
A.6.1 Semantics of the Predefined Data Types
367
A.6.2 The Space Model
367
A.6.3 The Time Model
371
A.6.4 Semantic Domains
372
A.6.5 Auxiliary Functions
372
A.6.6 Semantic Functions
375
B Graphical Notation 383
B.1 Entity-Relationship Model
383
B.2 Relational and Object-Relational Models
385
B.3 Conventional Data Warehouses
386
B.4 Spatial Data Warehouses
388
B.5 Temporal Data Warehouses
389
References 391
Glossary 411
Index 425
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 UniversitƩ Libre de Bruxelles, Belgium (2006). Her research interests include data warehouses, OLAP systems, geographic information systems, and temporal databases.











Esteban Zimįnyi 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.