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
Data Warehouse-lntegrated Constructed by integrating multiple, heterogeneous data Sources relational databases flat files on -line transaction records Data cleaning and data integration techniques are applied Ensure consistency in naming conventions, encoding structures attribute measures etc. among different data sources E.g., Hotel price: currency tax, breakfast covered, etc When data is moved to the warehouse it is converted
6 Data Warehouse—Integrated ◼ Constructed by integrating multiple, heterogeneous data sources ◼ relational databases, flat files, on-line transaction records ◼ Data cleaning and data integration techniques are applied. ◼ Ensure consistency in naming conventions, encoding structures, attribute measures, etc. among different data sources ◼ E.g., Hotel price: currency, tax, breakfast covered, etc. ◼ When data is moved to the warehouse, it is converted
Data warehouse-Time variant The time horizon ha'raiz(n for the data warehouse is significantly longer than that of operational systems Operational database: current value data Data warehouse data: provide information from a historical perspective( e.g. past 5-10 years) Every key structure in the data warehouse Contains an element of time, explicitly or implicitly But the key of operational data may or may not contain time element
7 Data Warehouse—Time Variant ◼ The time horizon [hə'raɪz(ə)n] for the data warehouse is significantly longer than that of operational systems ◼ Operational database: current value data ◼ Data warehouse data: provide information from a historical perspective (e.g., past 5-10 years) ◼ Every key structure in the data warehouse ◼ Contains an element of time, explicitly or implicitly ◼ But the key of operational data may or may not contain “time element
Data Warehouse-Nonvolatile A physically separate store of data transformed from the operational environment Operational update of data does not occur in the data warehouse environment Does not require transaction processing, recovery, and concurrency control mechanisms Requires only two operations in data accessing initial loading of data and access of data
8 Data Warehouse—Nonvolatile ◼ A physically separate store of data transformed from the operational environment ◼ Operational update of data does not occur in the data warehouse environment ◼ Does not require transaction processing, recovery, and concurrency control mechanisms ◼ Requires only two operations in data accessing: ◼ initial loading of data and access of data
Data wareh。 use Vs。 Heter。 geneous DBMS Traditional heterogeneous DB integration a query driven approach Build wrappers/mediators on top of heterogeneous databases When a query is posed to a client site a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved and the results are integrated into a global answer set Complex information filtering compete for resources Data warehouse: update-driven, high performance Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis
9 Data Warehouse vs. Heterogeneous DBMS ◼ Traditional heterogeneous DB integration: A query driven approach ◼ Build wrappers/mediators on top of heterogeneous databases ◼ When a query is posed to a client site, a meta-dictionary is used to translate the query into queries appropriate for individual heterogeneous sites involved, and the results are integrated into a global answer set ◼ Complex information filtering, compete for resources ◼ Data warehouse: update-driven, high performance ◼ Information from heterogeneous sources is integrated in advance and stored in warehouses for direct query and analysis
Data Warehouse vs Operational DBMS OLTP (on-line transaction processing) Major task of traditional relational dBms Day-to-day operations: purchasing, inventory banking manufacturing payroll, registration accounting etc OLAP (on-line analytical processing Major task of data warehouse system Data analysis and decision making Distinct features(OLTP VS OLAP User and system orientation: customer Vs. market Data contents: current detailed vs, historical, consolidated Database design: ER+ application VS star subject View: current, local vs. evolutionary, integrated Access patterns: update vs read-only but complex queries
10 Data Warehouse vs. Operational DBMS ◼ OLTP (on-line transaction processing) ◼ Major task of traditional relational DBMS ◼ Day-to-day operations: purchasing, inventory, banking, manufacturing, payroll, registration, accounting, etc. ◼ OLAP (on-line analytical processing) ◼ Major task of data warehouse system ◼ Data analysis and decision making ◼ Distinct features (OLTP vs. OLAP): ◼ User and system orientation: customer vs. market ◼ Data contents: current, detailed vs. historical, consolidated ◼ Database design: ER + application vs. star + subject ◼ View: current, local vs. evolutionary, integrated ◼ Access patterns: update vs. read-only but complex queries
