The document discusses dimensional modeling and data warehousing. It describes how dimensional models are designed for understandability and ease of reporting rather than updates. Key aspects include facts and dimensions, with facts being numeric measures and dimensions providing context. Slowly changing dimensions are also covered, with types 1-3 handling changes to dimension attribute values over time.

1. Business Information Systems Dimensional Analysis Prithwis Mukerjee, Ph.D.

2. Dimensional Models A denormalized relational model Made up of tables with attributes

3. Relationships defined by keys and foreign keys Organized for understandability and ease of reporting rather than update

4. Queried and maintained by SQL or special purpose management tools.

5. From Relational to Dimensional Relational Model Designed from the perspective of process efficiency Marketing

6. Sales “Normalised” data structures Entity Relationship Model Used for transactional, or operational systems OLTP : OnLine Transaction Processing Based on data that is Current

7. Non Redundant Dimensional Model Designed from the perspective of subject Sales

8. Customers “De-normalised” data structures in blatant violation of normalisation

9. Used for analysis of aggregated data OLAP : OnLine Analytical Processing Based on data that is Historical

10. May be redundant

11. ER vs. Dimensional Models One table per entity

12. Minimize data redundancy

13. Optimize update

14. The Transaction Processing Model One fact table for data organization

15. Maximize understandability

16. Optimized for retrieval

17. The data warehousing model

18. Strengths of the Dimensional Model Predictable, standard framework

19. Respond well to changes in user reporting needs

20. Relatively easy to add data without reloading tables

21. Standard design approaches have been developed

22. There exist a number of products supporting the dimensional model “ The Data Warehouse Toolkit” by Ralph Kimball & Margy Ross “ The Data Warehouse Lifecycle Toolkit” by Ralph Kimball & Margy Ross

23. A Transactional Database OrderDetails OrderHeaderID ProductID Amount OrderHeader OrderHeaderID CustomerID OrderDate FreightAmount Products ProductID Description Size Customers CustomerID AddressID Name Addresses AddressID StateID Street States StateID CountryID Desc Countries CountryID Description

24. A Dimensional Model FactSales CustomerID ProductID TimeID SalesAmount Products ProductID Description Size Subcategory Category Customers CustomerID Name Street State Country Time TimeID Date Month Quarter Year

25. Extract Transform Load Relational Dimensional Model Process Oriented Subject Oriented Transactional Aggregate Current Historic

26. Facts & Dimensions There are two main types of objects in a dimensional model Facts are quantitative measures that we wish to analyse and report on.

27. Dimensions contain textual descriptors of the business. They provide context for the facts.

28. Fact & Dimension Tables FACTS Contains two or more foreign keys

29. Tend to have huge numbers of records

30. Useful facts tend to be numeric and additive DIMENSIONS Contain text and descriptive information

31. 1 in a 1-M relationship

32. Generally the source of interesting constraints

33. Typically contain the attributes for the SQL answer set.

34. GB Video E-R Diagram Customer #Cust No F Name L Name Ads1 Ads2 City State Zip Tel No CC No Expire Rental #Rental No Date Clerk No Pay Type CC No Expire CC Approval Line #Line No Due Date Return Date OD charge Pay type Requestor of Owner of Video #Video No One-day fee Extra days Weekend Title #Title No Name Vendor No Cost Name for Holder of

35. GB Video Data Mart Customer CustID Cust No F Name L Name Rental RentalID Rental No Clerk No Store Pay Type Line LineID OD Charge OneDayCharge ExtraDaysCharge WeekendCharge DaysReserved DaysOverdue CustID AddressID RentalId VideoID TitleID RentalDateID DueDateID ReturnDateID Video VideoID Video No Title TitleID TitleNo Name Cost Vendor Name Rental Date RentalDateID SQLDate Day Week Quarter Holiday Due Date DueDateID SQLDate Day Week Quarter Holiday Return Date ReturnDateID SQLDate Day Week Quarter Holiday Address AddressID Adddress1 Address2 City State Zip AreaCode Phone

36. Fact Table Measurements associated with a specific business process Grain: level of detail of the table

37. Process events produce fact records

38. Facts (attributes) are usually Numeric

39. Additive Derived facts included

40. Foreign (surrogate) keys refer to dimension tables (entities)

41. Classification values help define subsets

42. Dimension Tables Entities describing the objects of the process Conformed dimensions cross processes

43. Attributes are descriptive Text

44. Numeric Surrogate keys

45. Less volatile than facts (1:m with the fact table)

46. Null entries

47. Date dimensions

48. Produce “by” questions