1. Fox MIS Spring 2011 Data Warehouse Week 8 Introduction of Data Warehouse Multidimensional Analysis: OLAP

2. Data Warehouse Integrated, Subject-Oriented, Time-Variant, Nonvolatile database that provides support for decision making

3. Characteristics of Data Warehouse Integrated –Centralized –Holds data retrieved from entire organization Time Variant –Flow of data through time –Projected data Non-Volatile –Data never removed –Always growing Subject-Oriented –Optimized to give answers to diverse questions –Used by all functional areas

4. Multidimensional Analysis: OLAP (Online Analytical Processing)

5. 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 Online Analytical Processing (OLAP)

6. Example: Sales

7. Multidimensional View of Sales Multidimensional analysis involves viewing data simultaneously categorized along potentially many dimensions

8. OLAP Server with Multidimensional Data Store Arrangement

9. Simple OLAP

10. Slice and Dice

11. Pivoting

12. OLAB Cube Example

13. OLAP Screen Example

15. Data Warehouse Modeling: Star Schema Data-modeling technique Also called star-join schema, data cube, or multi-dimensional schema The simplest style of data warehouse schema. The star schema consists of one or more fact tables referencing any number of dimension tables Maps multidimensional decision support into relational database Yield model for multidimensional data analysis while preserving relational structure of operational DB Facts –The fact table holds the main data. It includes a large amount of aggregated data, such as price and units sold Dimensions –Dimension tables, which are usually smaller than fact tables, include the attributes that describe the facts. Attributes

16. Star Schema for Sales

17. Data Warehouse Implementation Road Map