Chapter 4: Data Warehousing and On-line Analytical Processing Data Warehouse Design and Usage (aDesign of Data Warehouses: A Business Analysis Framework (b)Data Warehouses Design Processes (cData Warehouse Usage (d) From On-Line analytical Processing to On-Line analytical Mining Data Warehouse implementation (a) Efficient Data Cube Computation Cube Operation materialization of data Cubes and Iceberg cubes (b)Indexing OLAP Data: Bitmap Index and Join Index (c Efficient Processing of OLAP Queries (d)oLaP Server Architectures: ROLAP VS MOLAP VS HOLAP Data generalization by attribute-Oriented Induction (a Attribute-Oriented Induction for Data Characterization (b)Efficient Implementation of Attribute-Oriented Induction (c)Attribute-Oriented Induction for Class Comparisons (d)Attribute-Oriented Induction VS Cube-Based OLAP Summary 33 Chapter 4: Data Warehousing and On-line Analytical Processing ◼ Data Warehouse Design and Usage ◼ (a) Design of Data Warehouses: A Business Analysis Framework ◼ (b) Data Warehouses Design Processes ◼ (c) Data Warehouse Usage ◼ (d) From On-Line Analytical Processing to On-Line Analytical Mining ◼ Data Warehouse Implementation ◼ (a) Efficient Data Cube Computation: Cube Operation, Materialization of Data Cubes, and Iceberg Cubes ◼ (b) Indexing OLAP Data: Bitmap Index and Join Index ◼ (c) Efficient Processing of OLAP Queries ◼ (d) OLAP Server Architectures: ROLAP vs. MOLAP vs. HOLAP ◼ Data Generalization by Attribute-Oriented Induction ◼ (a) Attribute-Oriented Induction for Data Characterization ◼ (b) Efficient Implementation of Attribute-Oriented Induction ◼ (c) Attribute-Oriented Induction for Class Comparisons ◼ (d) Attribute-Oriented Induction vs. Cube-Based OLAP ◼ Summary