Chapter 4: Data Warehousing and On-line Analytical Processing Data Warehouse: Basic Concepts Data Warehouse Modeling: Data Cube and OLAP Data Warehouse design and Usage Data Warehouse implementation a data generalization by attribute-Oriented Induction Summary
1 Chapter 4: Data Warehousing and On-line Analytical Processing ◼ Data Warehouse: Basic Concepts ◼ Data Warehouse Modeling: Data Cube and OLAP ◼ Data Warehouse Design and Usage ◼ Data Warehouse Implementation ◼ Data Generalization by Attribute-Oriented Induction ◼ Summary
Chapter 4: Data Warehousing and On-line Analytical Processing Data Warehouse: Basic Concepts (a) What is a Data Warehouse? (b)Data Warehouse: A Multi-Tiered Architecture (c)Three Data Warehouse Models: Enterprise Warehouse, Data Mart, and virtual Warehouse (d) Extraction, Transformation and Loading (e)Metadata Repository Data Warehouse modeling Data Cube and olap (a cube: A Lattice of Cuboid (b) Conceptual Modeling of Data Warehouses (c) Stars, Snowflakes, and Fact Constellations: Schemas for Multidimensional Databases (d)Dimensions: The role of Concept Hierarchy (e)Measures: Their Categorization and Computation (f Cube Definitions in Database systems (g Typical OLAP Operations (h)a starnet Query Model for querying Multidimensional Databases 2
2 Chapter 4: Data Warehousing and On-line Analytical Processing ◼ Data Warehouse: Basic Concepts ◼ (a) What Is a Data Warehouse? ◼ (b) Data Warehouse: A Multi-Tiered Architecture ◼ (c) Three Data Warehouse Models: Enterprise Warehouse, Data Mart, and Virtual Warehouse ◼ (d) Extraction, Transformation and Loading ◼ (e) Metadata Repository ◼ Data Warehouse Modeling: Data Cube and OLAP ◼ (a) Cube: A Lattice of Cuboids ◼ (b) Conceptual Modeling of Data Warehouses ◼ (c) Stars, Snowflakes, and Fact Constellations: Schemas for Multidimensional Databases ◼ (d) Dimensions: The Role of Concept Hierarchy ◼ (e) Measures: Their Categorization and Computation ◼ (f) Cube Definitions in Database systems ◼ (g) Typical OLAP Operations ◼ (h) A Starnet Query Model for Querying Multidimensional Databases
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 3
3 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
What is a data warehouse? Defined in many different ways, but not rigorously. a decision support database that is maintained separately from the organization s operational database Support information processing by providing a solid platform of consolidated, historical data for analysis a data warehouse is a subiect-oriented, integrated time-variant and nonvolatile collection of data in support of management's decision-making process. -W.H. Inmon Data warehousing: The process of constructing and using data warehouses
4 What is a Data Warehouse? ◼ Defined in many different ways, but not rigorously. ◼ A decision support database that is maintained separately from the organization’s operational database ◼ Support information processing by providing a solid platform of consolidated, historical data for analysis. ◼ “A data warehouse is a subject-oriented, integrated, time-variant, and nonvolatile collection of data in support of management’s decision-making process.”—W. H. Inmon ◼ Data warehousing: ◼ The process of constructing and using data warehouses
Data Warehouse-Subject-Oriented Organized around major subjects, such as customer product, sales Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process
5 Data Warehouse—Subject-Oriented ◼ Organized around major subjects, such as customer, product, sales ◼ Focusing on the modeling and analysis of data for decision makers, not on daily operations or transaction processing ◼ Provide a simple and concise view around particular subject issues by excluding data that are not useful in the decision support process