1. 1 ACCTG 6910 Building Enterprise & Business Intelligence Systems (e.bis) Dimensional Modeling II Olivia R. Liu Sheng, Ph.D. Emma Eccles Jones Presidential Chair of Business

2. 2 Technical Architecture Design Product Selection & Installation End-User Application Specification End-User Application Development The Business Dimensional Lifecycle Project Planning Business Requirement Definition Business Requirement Definition Deployment Maintenance and Growth Project Management Dimensional Modeling Physical Design Data Staging Design & Development

3. 3 Outline Table structure, types, characteristics and terminology Design steps Dimensional models with varying types of fact and dimension tables

4. 4 Types of Facts Transactional facts (transactions or line items in transactions) Snapshots Factless facts

5. 5 Types of Dimensions Role playing dimensions Heterogeneous dimensions Slowly changing dimensions Large dimensions Many-to-many dimensions

6. 6 Keys and Attributes Primary key - a column whose value uniquely identifies each row (record) in the table. Attributes – columns in a table that are not designated as the primary key. Foreign key – a non-primary-key attribute for a table that corresponds to a primary key of another table.

7. 7 Attributes in DW tables Dimension Table –One Primary Key –Dimension Attributes Fact table –Primary key --- A collection of primary keys from all its associated dimension tables All warehouse keys in fact table are foreign keys referring to its associated dimension tables All/part of warehouse keys in fact table form the primary key of fact table –Fact Attributes

8. 8 Attributes in DW tables Data warehouse keys generated by the system

9. 9 Keys and Grain Keys –Primary or natural keys (from source systems) –Warehouse or synthetic keys (generated by a data warehouse tool) Grain –The level of detail of fact measures described in the DW, e.g., sales transactions from order line items by order date, product and customer

10. 10 Single-Fact-Table Data Warehouse Design Decisions 1.The business questions in focus and source information systems* 2.The grain of the fact table 3.The dimensions tables and keys 4.The fact attributes and dimension attributes *All DW attributes must be mapped to or derived from source attributes

11. 11 Single-Fact-Table Data Warehouse Design Decisions 1.The business questions in focus and source information systems 2.The grain of the fact table 3.The dimensions tables and keys 4.The fact attributes and dimension attributes

12. 12 Sample Business Questions Report Sales in terms of – (total) amt, (total) qty and (avg.) price Report Sales by PRODUCT name and/or category name Report Sales by CUSTOMER name, city and/or or state Report Sales by ORDER date, month, year, holiday, special event or other time constraints Report using a combination of the measures and constraints

13. 13 Relational Schema of B.com B2B System Orders ( Order_No, SID, BID, CID, Order_date) OrderLine (Order_No, Line_ID, PID, Actual_Del_Date, Target_Del_Date, Arrival_Date, Shipping_Fee, Tax, Quantity, Unit_Price,Defect_on_arrival) Delivery ( SID, CID, Unit_shipping_fee, UNIT_DEL_TIME) Contract ( CID, Contract_Name, Payment_term, Payment_num) Payment ( PaymentID, OrderNO, Pay_Amount, Date)

14. 14 Relational Schema of B.com B2B System Category ( CAT_ID, CAT_Name) Product ( PID, CAT_ID, P_Weight, P_Life, P_Name) Supplier ( SID, S_Name, S_City, S_State, S_Country) Product_Supply ( PID, SID, Unit_Price, Quantity_in_Stock, Production_in_Week) Buyer ( BID, B_Name, CityID, B_Type) Buyer_City ( CityID, C_Name, C_State, C_Country, C_Tax)

15. 15 Single-Fact-Table Data Warehouse Design Decisions 1.The business questions in focus and source information systems 2.The grain of the fact table 3.The dimensions tables and keys 4.The fact attributes and dimension attributes

16. 16 Grain of the Fact Table  Type of fact table: transactional facts  Potential grains: order or orderline  Constraints: order date, product, customer  Grain: sales from orderline (by order date, product, and customer)

17. 17 Single-Fact-Table Data Warehouse Design Decisions 1.The business questions in focus and source information systems 2.The grain of the fact table 3.The dimensions tables and keys 4.The fact attributes and dimension attributes

18. 18 Dimension Tables and Keys Key dimension tables jointly make up the primary key for a fact table

19. 19 Single-Fact-Table Data Warehouse Design Decisions 1.The business questions in focus and source information systems 2.The grain of the fact table 3.The dimensions tables and keys 4.The fact attributes and dimension attributes

20. 20 Determine Fact Attributes

21. 21 Types of Fact Attributes Additive fact attributes can be added along any dimension.

22. 22 Types of Fact Attributes Non-additive fact attributes cannot be added along any dimension.

23. 23 Types of Fact Attributes Semi-additive fact attributes can be added along some dimensions.

24. 24 Time Dimension Data warehouse needs an explicit time dimension table instead of just a time attribute (e.g, ORDERDATE). Save computation effort and improve query performance Complex queries regarding calendar calculation are hidden from end users of data warehouse.

25. 25 Time Dimension Besides the time attribute, time dimension table includes the following additional attributes: –Day_of_week (1-7); Day_number_in_month (1- 31); –Day_number_in_year (1-365) –Week_number (1-52); month (1-12), Quarter (1- 4) –Holiday_flag (y/n) –Fiscal_quarter, Fiscal_year

26. 26 Determine Dimension Attributes

27. 27 Avoid Snowflake Designs

28. 28 Avoid Snowflake Design Snowflake structure

29. 29 Avoid Snowflake Schemas Tradeoff of avoiding snowflake –Advantage: improve query performance and easy of understanding –Disadvantage: require more storage space