1. Exploiting the DW data DW is a platform for creating a wide array of reports It solves data feed problems, but does not lead to specific decision support Need a model for organising data into meaningful reports Need specific interfaces for users

2. Extraction Cleaning Transformation Loading Relational Database on a dedicated Server De normalised, data Static Reporting Scrutinising Multidimensional Data Cubes OLAP tools Data Warehouse Source Systems Discovering Data Mining ……. Data Staging Area Exploiting the DW Data

3. Multidimensional Models The data is found at the intersection of dimensions. Product P/L_Line Time FINANCE Market Product Time SALES Customer

4. Representing multidimensional data

5. MOLAP Server The application layer stores data in a multidimensional structure The presentation layer provides the multidimensional view MOLAP Engine DSS client Application layer Warehouse Efficient storage and processing Complexity hidden from the user (but NOT from developer) Analysis using pre-aggregated summaries and pre-calculated measures

6. ROLAP Server The warehouse stores atomic data. The application layer generates SQL for the three- dimensional view. The presentation layer provides the multidimensional view. ROLAP engine DSS client Application layer Warehouse server Multiple SQL

7. MOLAP ServeruserWarehouse Query Data MDDB Periodicload

8. ROLAP Server user Warehouse Datacache Livefetch Cache Query Data Also Hybrid (HOLAP)

9. Choosing a Reporting Architecture Business needs Potential for growth interface enterprise architecture Network architecture Speed of access Openness MOLAP ROLAP Simple Complex QueryPerformance Good OK Analysis

10. Modeling Warehouses differ from operational structures:Warehouses differ from operational structures: –Analytical requirements –Subject orientation Data must map to subject oriented information:Data must map to subject oriented information: –Identify business subjects –Define relationships between subjects –Name the attributes of each subject Modeling is iterativeModeling is iterative Modeling tools are availableModeling tools are available

11. 1.Defining the business model 2.Creating the dimensional model 3.Modeling summaries 4.Creating the physical model Physical model 1 2, 3 4 Select a business process Modeling the Data Warehouse

12. Identifying Business Rules Product Type Monitor Status PC15 inchNew Server17 inchRebuilt 19 inchCustom None Location Geographic proximity 0 - 1 miles 1 - 5 miles > 5 miles Store Store > District > Region Time Month > Quarter > Year

13. Creating the Dimensional Model Identify fact tables –Translate business measures into fact tables –Analyze source system information for additional measures –Identify base and derived measures –Document additivity of measures Identify dimension tables Link fact tables to the dimension tables Create views for users

14. Dimension Tables Dimension tables have the following characteristics: Contain textual information that represents the attributes of the business Contain relatively static data Are joined to a fact table through a foreign key reference ProductChannel Facts (units, price) Customer Time

15. Fact Tables Fact tables have the following characteristics: Contain numeric measures (metrics) of the business May contain summarized (aggregated) data May contain date-stamped data Are typically additive Have key value that is typically a concatenated key composed of the primary keys of the dimensions Joined to dimension tables through foreign keys that reference primary keys in the dimension tables

16. ProductChannel Facts (units, price) Customer Time Dimension tables Fact table Dimensional Model (Star Schema)

17. Star Schema Model Central fact table Radiating dimensions Denormalized model Store Table Store_id District_id... Item Table Item_id Item_desc... Time Table Day_id Month_id Period_id Year_id Product Table Product_id Product_desc … Sales Fact Table Product_id Store_id Item_id Day_id Sales_dollars Sales_units...

18. Star Schema Model Easy for users to understand Fast response to simple queries Simple metadata Supported by many front end tools Less robust to change Does not support history

19. Using Summary Data Provides fast access to pre-computed data Reduces use of I/O, CPU, and memory Is distilled from source systems and pre- calculated summaries Usually exists in summary fact tables Phase 3: Modeling summaries

20. Designing Summary Tables UnitsSales(€)Store Product A Total Product B Total Product C Total Average Maximum Total Percentage

21. Summary Tables Example SALES FACTS SalesRegionMonth 10,000NorthJan 99 12,000SouthFeb 99 11,000North Jan 99 15,000WestMar 99 18,000South Feb 99 20,000North Jan 99 10,000EastJan 99 2,000WestMar 99 SALES BY MONTH/REGION MonthRegionTot_Sales$ Jan 99North41,000 Jan 99East10,000 Feb 99South40,000 Mar 99West17,000 SALES BY MONTH MonthTot_Sales Jan 9951,000 Feb 9940,000 Mar 9917,000