Data Warehousing on the Internet

1. Accessing the Corporate Knowledge Base Data Warehousing on the Internet Tom Hammergren President, OBJX Inc. - hammergren@objx.com

2. Books by Tom Hammergren • Data Warehousing: Building the Corporate Knowledge Base ISBN: 1-85032-856-0 • Data Warehousing on the Internet: Accessing the Corporate Knowledge BaseISBN: 1-85032-857-9 late 1997 • Data Warehousing Abstract Data: Expanding the Corporate Knowledge Base late 1998

3. Data Artifacts within a Business Cycle Atomic Process - every business cycle can be carved up into several discrete processes with their own leading indicators, which lead to action, which produce trailing indicators. ACTION Trailing Indicators Leading Indicators

4. Data Warehousing Defined Data Warehousing is the process whereby organizations extract meaning from their information assets through the use of special stores called data warehouses. Dr. Ramon Barquin The Data Warehousing Institute ACTION Trailing Indicators Leading Indicators Warehouse - a location or facility for storing goods and merchandise. Data - facts and information about something.

5. Sample Information Packages • Student Services • Student Retention • Recruitment • Financial Aid • Financial Analysis • Activity Based Costing • Budgets • Facilities Planning • Market Trends (Distance Learning) • Maintenance & Expansion • Human Resources • Payroll • Appointment Process

6. Information Package Usage • Are course enrollments subject to technology trends? • Which courses are impacted? • How are they impacted? • How many National Merit Scholars were admitted this year? • How does that compare to the last 5 years? • How does it compare to current recruitment? • What attributes describe the most likely to be recruited successfully? • Which departments have the most tenured instructors? • What percentage of student body do they address?

7. Data Application BusinessProcesses Technology Support Architecture

8. Process and Configuration Management Services Distributed Design and Coordination Services Source Data Data Integration Services Data Management Services Business Application Services Selection & Mapping Relational Server MetaData Data Warehous Filtering & Staging OLAP Server Application Models Integration Models Data Mart(s) Visualization Services Staging Area Cleansing & Transforming Data Mining Server Personal Data Cache

9. Process and Configuration Management Services Distributed Design and Coordination Services Source Data Data Integration Services Data Management Services Business Application Services Selection & Mapping Relational Server MetaData Data Warehous Filtering & Staging OLAP Server Application Models Integration Models Data Mart(s) Visualization Services Staging Area Cleansing & Transforming Data Mining Server Personal Data Cache

10. Event RPC SQL Standard Application Architecture Technology & Support? User Interface Presentation Logic Processing Logic Data Access

11. Web Application Architecture HTTP Server Web Browser HTML Form User Interface Information entered by the user Common Gateway Interface Presentation Logic User Authentication Processing Logic Information entered by the user Access List Data Access Gateway Program Database Interface

12. Web Application Architecture HTTP Server Web Browser HTML Form User Interface Information entered by the user Presentation Logic Common Gateway Interface Formatted Results Processing Logic Gateway Program Process Information Format Results Database Interface Verify Query Query Results Data Access Process Query

13. Issues Facing Web Developer • User Selector • Avoid rouge queries • Avoid meaningless queries • Dynamic Inter-Field Editing • Session Logging • Who’s There… • What have they done... • Fat or Thin Client & Tiers • Stateless • How many developers understand OO? • Java, C++, Perl, … • Integration and Immaturity • Manageability

14. Demonstration Requirement Users of the data warehouse need to see: Studentenrollments by demographic data, by campus, school and majors for planning course offerings. The data will also include fee income data so the users can estimate future incomes (estimates based on five year trends). Demographic data would inlcude items such as residency, gender, GPA, credit hour loads per semester, etc. It is also of interest to differentiate between students in major taking courses vs. students out of major taking courses.

15. Dimensions Time (five years) Students (Demographics) Courses (Degree) Majors Location (Campus, School) Measures Enrollment (trend) Fee Income (trend) Demonstration Measures and Dimensions

16. Demonstration Information Package Diagram

17. Demonstration Multiple Access Paths/Time

18. Demonstration Multiple Access Paths/Student

19. Demonstration Multiple Access Paths/Staff

20. Demonstration Star Schema Diagram

21. User Interface Presentation Logic Processing Logic Data Access Live Demonstration Netscape Navigator NetFactory NetCharts Sybase NetImpact Dynamo Sybase SQL Anywhere

22. Closure Knowledge in the form of an informational commodity indispensable to productive power is already, and will continue to be, a major -- perhaps the major -- stake in the worldwide competition for power. It is conceivable that the nation states will one day fight for control of information, just as they battled in the past for control over territory, and afterwards for control over access to and exploitation of raw materials and cheap labor. - Jean Francois Lyotard