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Data Warehousing

Data Warehousing. Data Warehousing. Make the right decisions for your organization Rapid access to all kinds of information Research and analyze the past data Identify and predict future trends The construction of data warehouse Involve data cleaning and data integration

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Data Warehousing

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  1. Data Warehousing

  2. Data Warehousing • Make the right decisions for your organization • Rapid access to all kinds of information • Research and analyze the past data • Identify and predict future trends • The construction of data warehouse • Involve data cleaning and data integration • Provide on-line analytical processing (OLAP) tools for the interactive analysis of data • W.H. Inmon • A data warehouse is a subject oriented, integrated, time-dependent and non-volatile collection of data in support of management’s decision making process

  3. Characteristics of Data Warehouse • Subject-oriented • Data warehouse is designed for decision support and around major subject, such as customer and sales • Not all information in the operational database is useful • Integrated • Integrate multiple heterogeneous sources and make it consistent • The data from different sources may use different names for the same entities

  4. Characteristics of Data Warehouse • Time dependent • Record the information and the time when it was entered • Data mining can be done from the data in some period of time • Non-volatile • Data in a data warehouse is never updated

  5. Data Warehousing • Data warehousing • The process of constructing and using data warehouse • Two types of databases • Operational database • Large database in operation • Built for high speed and large number of users • Data warehouse • Designed for decision support • Contain vast amounts of historical data • Data mart • A department subset of the data warehouse that focuses on selected subjects, and its scope is department-wide

  6. OLTP & OLAP System • OLTP (On-Line Transaction Processing) System • The major task of operational database is to perform on-line transaction and query processing • OLAP (On-Line Analytical Processing) System • Data warehouse system serves users on data analysis and decision making

  7. Differences ~ OLTP & OLAP • Characteristic • OLTP: operational processing • OLAP: informational processing • Orientation • OLTP: transaction-oriented • OLAP: analysis-oriented • User • OLTP: customer, DBA • OLAP: manager, analyst • Function • OLTP: day-to-day operations • OLAP: information requirement, decision support

  8. Differences ~ OLTP & OLAP • DB design • OLTP: ER based, application-oriented • OLAP: star/snowflake, subject-oriented • Data • OLTP: current; guaranteed up-to date • OLAP: historical • Unit of work • OLTP: short, simple query • OLAP: complex query • Access • OLTP: read/write • OLAP: mostly read • DB size • OLTP: 100 MB to GM • OLAP: 100 GB to TB

  9. Differences ~ OLTP & OLAP

  10. Data Warehousing Relational Data Model Relational Schema Multidimensional Data Model Star Schema or Snowflake Schema

  11. Model & Schema for Relational Database Relational Data Model Relational Schema

  12. Multidimensional Data Model • Example: AllElectronics creates a sales data warehouse in order to keep records of the store’s sales • Fact Table • sales amount in dollars and number of units sold (measure) • Dimension Tables • time, item, branch, and location • Multidimensional data model views data in the form of a data cube

  13. Two Dimensions • 2-D view of sales data for item sold per quarter in the city of Vancouver. The measure is dollars_sold (in thousands) Dimensions Measures

  14. Three Dimensions • 3-D view of sales data according to the dimensions time, item and location. The measure is dollars_sold (in thousands) Dimensions Measures

  15. Three Dimensions • 3-D data cube representation according to the dimensions time, item and location. The measure is dollars_sold (in thousands)

  16. Four Dimensions • 4-D data cube of sales data according to the dimensions supplier, time, item and location. The measure is dollars_sold (in thousands)

  17. Schemas for Multidimensional Data Model • Star Schema • Snowflake Schema • Fact Constellation Schema

  18. Star Schema

  19. Snowflake Schema

  20. Snowflake Schema • Some dimension tables are normalized to reduce redundancies and save storage space • Reduce the effectiveness of browsing since more join will be needed to execute a query • This saving of space is negligible in comparison to the magnitude of the fact table • Snowflake schema is not as popular as the start schema in data warehouse design

  21. Fact Constellation Schema • Multiple fact tables share dimension tables

  22. OLAP Technologies

  23. Concept Hierarchies

  24. Three-Tier DW Architecture

  25. Case Study in Data Warehousing

  26. 公司簡介

  27. 公司簡介

  28. 公司簡介

  29. 背景資料 • A公司利用傳統的E-R Model 來建立其關聯式資料庫系統 • A公司發現此種資料庫系統無法即時地滿足高階主管對有效資訊的取得與分析，進而做出決策 • 傳統的E-R Model資料模型的設計對資料的一致性 (Consistency) 及避免資料的重複(Duplication) 上有最佳的效率 • 對於Multi-constraint及Multi-join的多維度查詢除了會拉長查詢的時間外，還會搶奪系統資源，造成系統負荷過重而產生瓶頸

  30. 背景資料 • A公司決定利用多維度資料模型(Multidimensional Data Model) 所設計的資料庫系統來解決上述的問題 • 建立資料倉儲 (Data Warehousing) • 一次滿足所有的限制，而不需大量的合併動作，同時使用者介面也較為和善

  31. 建立多維度資料庫的步驟 • 了解作業流程與需求，以作為設計時的基礎知識，此部份可藉由與客戶的訪談、閱讀交易系統文件、分析現有作業流程而得知 • 界定Fact Table內要有哪些組成？此部份要注意到是否能滿足第一步驟所定義的需求 • 找出用戶的思考觀點及每一個思考觀點的層級關係，也就是Dimension Table • 定義Fact Table的Measure，這些Measure是各個維度所可能會取用的值

  32. 因果關係圖

  33. 因果關係圖

  34. 因果關係圖

  35. 因果關係圖

  36. 多維度資料庫的建立

  37. 多維度資料庫的建立

  38. 多維度資料庫的建立

  39. 多維度資料庫的建立

  40. 多維度資料庫的建立

  41. 多維度資料庫的建立

  42. 多維度資料庫的建立

  43. 多維度資料庫的建立

  44. 多維度資料庫的建立

  45. 多維度資料庫的建立 • 其餘表格依此類推。 • 最後共產生共20個 Fact Tables及數十個Dimension Tables 。 • 這些表格為OLAP系統或資料探勘(Data Mining)系統的輸入(Input)。 • 利用這些系統我們才能得到更進一步的統計及知識的輸出(Output)。

  46. Design of Data Warehouse • How can I design a data warehouse ? • Top-down approach • Bottom-up approach • Combination of both • In general, the warehouse design process consists of the following steps • Choose a business process to model • Choose the gain of the business process • Choose the dimensions • Choose the measures

  47. 其它應用實例

  48. 其它應用實例

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