1. Chapter 13 The Data Warehouse
Database Systems: Design, Implementation, and Management, Seventh Edition, Rob and Coronel

2. Star Schema Representation (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

3. Star Schema Performance-Improving Techniques
Four techniques to optimize data warehouse design are:
Normalizing dimensional tables
Maintaining multiple fact tables to represent different aggregation levels
Denormalizing fact tables
Partitioning and replicating tables
Achieve semantic simplicity and facilitate end-user navigation through the dimensions
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

4. Star Schema Performance-Improving Techniques (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

5. Star Schema Performance-Improving Techniques (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

6. Star Schema Performance-Improving Techniques (continued)
Denormalizing fact tables improves data access performance and save data storage space
Partitioning splits table into subsets of rows or columns
Replication makes copy of table and places it in different location
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

7. Implementing a Data Warehouse
Numerous constraints, including:
Available funding
Management’s view of role played by an IS department and of extent and depth of information requirements
Corporate culture
No single formula can describe perfect data warehouse development
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

8. The Data Warehouse as an Active Decision Support Framework
Data warehouse is not static database
It is dynamic framework for decision support that is always a work in progress
Data warehouse is critical component of modern DSS, but it is not the only critical component
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

9. A Company-Wide Effort That Requires User Involvement
Data warehouse data cross departmental lines and geographical boundaries
Building a data warehouse requires the designer:
Involve end users in process
Secure end users’ commitment from beginning
Create continuous end-user feedback
Manage end-user expectations
Establish procedures for conflict resolution
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

10. Satisfy the Trilogy: Data, Analysis, and Users
Data warehouse designer must satisfy:
Data integration and loading criteria
Data analysis capabilities with acceptable query performance
End-user data analysis needs
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

11. Apply Database Design Procedures
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

12. Data Mining Data mining tools do the following:
Analyze data
Uncover problems or opportunities hidden in data relationships
Form computer models based on their findings
Use models to predict business behavior
Require minimal end-user intervention
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

13. Data Mining (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel

14. Data Mining (continued)
Database Systems: Design, Implementation, & Management, 7th Edition, Rob & Coronel