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Chapter 13

Chapter 13. The Data Warehouse Hachim Haddouti. In this chapter, you will learn:. How operational data and decision support differ What a data warehouse is and how its data are prepared What star schemas are and how they are constructed ROLAP, MOLAP

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Chapter 13

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  1. Chapter 13 The Data Warehouse Hachim Haddouti

  2. In this chapter, you will learn: • How operational data and decision support differ • What a data warehouse is and how its data are prepared • What star schemas are and how they are constructed • ROLAP, MOLAP • What data mining is and what role it plays in decision support Hachim Haddouti and Rob & Coronel, Ch13

  3. The Need for Data Analysis • External and internal forces require tactical and strategic decisions • Search for competitive advantage • Business environments are dynamic • Decision-making cycle time is reduced • Different managers require different decision support systems (DSS) Hachim Haddouti and Rob & Coronel, Ch13

  4. Decision Support Systems • Decision Support • Is a methodology • Extracts information from data • Uses information as basis for decision making Hachim Haddouti and Rob & Coronel, Ch13

  5. Decision Support Systems • Decision support system (DSS) • Arrangement of computerized tools • Used to assist managerial decision • Extensive data “massaging” to produce information • Used at all levels in organization • Tailored to focus on specific areas and needs • Interactive • Provides ad hoc query tools Hachim Haddouti and Rob & Coronel, Ch13

  6. DSS Components Hachim Haddouti and Rob & Coronel, Ch13

  7. Operational vs. Decision Support Data • Operational data • Relational, normalized database • Optimized to support transactions • Real time updates • DSS • Snapshot of operational data • Summarized • Large amounts of data • Data analyst viewpoint • Timespan • Granularity • Dimensionality Hachim Haddouti and Rob & Coronel, Ch13

  8. History Unchanged Vision: right information to the right time and place Data-Ware-housesystem EIS (=Enter-prise IntelligenceSystem) IDF (=Informa-tion Delivery Facility) InformationWarehouse EIS (=Enter-prise Information System) MIS (=Manage-ment Informa-tionssystem) MAIS (=Marke-ting Informations-system) DSS (=DecisionSupport System) EIS (=ExecutiveInformation System) 60' 70' 80', Begin 90' Mid 90' Hachim Haddouti and Rob & Coronel, Ch13

  9. Data Warehouse • Integrated • Centralized • Holds data retrieved from entire organization • Subject-Oriented • Optimized to give answers to diverse questions • Used by all functional areas • Time Variant • Flow of data through time • Projected data • Non-Volatile • Data never removed • Always growing Hachim Haddouti and Rob & Coronel, Ch13

  10. Creating a Data Warehouse Hachim Haddouti and Rob & Coronel, Ch13

  11. Sales Financial Purchase Storage Personnel Internal Information Sources Data Warehouse Berichte Analyzes, Trends External information sources Market Customer Supllier competition Data Warehouse Shape Hachim Haddouti and Rob & Coronel, Ch13

  12. Data Marts • Single-subject data warehouse subset • Decision support to small group • Can be test for exploring potential benefits of Data warehouses • Address local or departmental problems Hachim Haddouti and Rob & Coronel, Ch13

  13. Twelve Data Warehouse Rules 1. Separated from operational environment 2. Data are integrated 3. Contains historical data over long time horizon 4. Snapshot data captured at given time 5. Subject-oriented data 6. Mainly read-only data with periodic batch updates from operational source, no online updates 7. Development life cycle differs from classical one, data driven not process driven Hachim Haddouti and Rob & Coronel, Ch13

  14. Twelve Data Warehouse Rules (Con’t.) 8. Contains different levels of data detail • Current and old detail • Lightly and highly summarized 9. Characterized by read-only transactions to large data sets 10. Environment has system to trace data resources, transformation, and storage 11. Metadata critical components • Identify and define data elements • Provide the source, transformation, integration, storage, usage, relationships, and history of data elements 12. Contains charge-back mechanism for usage • Enforces optimal use of data Hachim Haddouti and Rob & Coronel, Ch13

  15. Online Analytical Processing (OLAP) • Advanced data analysis environment • Supports decision making, business modeling, and operations research activities • Characteristics of OLAP • Use multidimensional data analysis techniques • Provide advanced database support • Provide easy-to-use end-user interfaces • Support client/server architecture Hachim Haddouti and Rob & Coronel, Ch13

  16. OLAP Client/Server Architecture Hachim Haddouti and Rob & Coronel, Ch13

  17. OLAP Server Arrangement Hachim Haddouti and Rob & Coronel, Ch13

  18. OLAP Server with Multidimensional Data Store Arrangement Hachim Haddouti and Rob & Coronel, Ch13

  19. OLAP Server with Local Mini-Data-Marts Hachim Haddouti and Rob & Coronel, Ch13

  20. Relational OLAP (ROLAP) • OLAP functionality • Uses relational DB query tools • Extensions to RDBMS • Multidimensional data schema support • Data access language and query performance optimized for multidimensional data • Support for very large databases (VLDBs) Hachim Haddouti and Rob & Coronel, Ch13

  21. Typical ROLAP Client/Server Architecture Hachim Haddouti and Rob & Coronel, Ch13

  22. Multidimensional OLAP (MOLAP) • OLAP functionality to multidimensional databases (MDBMS) • Stored data in multidimensional data cube • N-dimensional cubes called hypercubes • Cube cache memory speeds processing • Affected by how the database system handles density of data cube called sparsity Hachim Haddouti and Rob & Coronel, Ch13

  23. MOLAP Client/Server Architecture Hachim Haddouti and Rob & Coronel, Ch13

  24. Star Schema • Data-modeling technique • Maps multidimensional decision support into relational database • Yield model for multidimensional data analysis while preserving relational structure of operational DB • Four Components: • Facts • Dimensions • Attributes • Attribute hierarchies Hachim Haddouti and Rob & Coronel, Ch13

  25. Simple Star Schema Figure 13.12 Hachim Haddouti and Rob & Coronel, Ch13

  26. Slice and Dice View of Sales Hachim Haddouti and Rob & Coronel, Ch13

  27. Star Schema Representation • Facts and dimensions represented by physical tables in data warehouse DB • Fact table related to each dimension table (M:1) • Fact and dimension tables related by foreign keys • Subject to the primary/foreign key constraints Hachim Haddouti and Rob & Coronel, Ch13

  28. Star Schema for Sales Hachim Haddouti and Rob & Coronel, Ch13

  29. Data Mining • Seeks to discover unknown data characteristics • Automatically searches data for anomalies and relationships • Data mining tools • Analyze data • Uncover problems or opportunities • Form computer models based on findings • Predict business behavior with models • Require minimal end-user intervention Hachim Haddouti and Rob & Coronel, Ch13

  30. Extraction of Knowledge from Data Hachim Haddouti and Rob & Coronel, Ch13

  31. Example 1 Hachim Haddouti and Rob & Coronel, Ch13

  32. Example 2 Hachim Haddouti and Rob & Coronel, Ch13

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