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xii Contents HAN 03-toc-ix-xviii-9780123814791 2011/6/1 3:32 Page xii #4

4.1.4 Data Warehousing: A Multitiered Architecture 130
4.1.5 Data Warehouse Models: Enterprise Warehouse, Data Mart,
and Virtual Warehouse 132
4.1.6 Extraction, Transformation, and Loading 134
4.1.7 Metadata Repository 134
4.2 Data Warehouse Modeling: Data Cube and OLAP 135
4.2.1 Data Cube: A Multidimensional Data Model 136
4.2.2 Stars, Snowflakes, and Fact Constellations: Schemas
for Multidimensional Data Models 139
4.2.3 Dimensions: The Role of Concept Hierarchies 142
4.2.4 Measures: Their Categorization and Computation 144
4.2.5 Typical OLAP Operations 146
4.2.6 A Starnet Query Model for Querying Multidimensional
Databases 149
4.3 Data Warehouse Design and Usage 150
4.3.1 A Business Analysis Framework for Data Warehouse Design 150
4.3.2 Data Warehouse Design Process 151
4.3.3 Data Warehouse Usage for Information Processing 153
4.3.4 From Online Analytical Processing to Multidimensional
Data Mining 155
4.4 Data Warehouse Implementation 156
4.4.1 Efficient Data Cube Computation: An Overview 156
4.4.2 Indexing OLAP Data: Bitmap Index and Join Index 160
4.4.3 Efficient Processing of OLAP Queries 163
4.4.4 OLAP Server Architectures: ROLAP versus MOLAP
versus HOLAP 164
4.5 Data Generalization by Attribute-Oriented Induction 166
4.5.1 Attribute-Oriented Induction for Data Characterization 167
4.5.2 Efficient Implementation of Attribute-Oriented Induction 172
4.5.3 Attribute-Oriented Induction for Class Comparisons 175
4.6 Summary 178
4.7 Exercises 180
4.8 Bibliographic Notes 184
Chapter 5 Data Cube Technology 187
5.1 Data Cube Computation: Preliminary Concepts 188
5.1.1 Cube Materialization: Full Cube, Iceberg Cube, Closed Cube,
and Cube Shell 188
5.1.2 General Strategies for Data Cube Computation 192
5.2 Data Cube Computation Methods 194
5.2.1 Multiway Array Aggregation for Full Cube Computation 195