1. 1 Topics about Data Warehouses What is a data warehouse? How does a data warehouse differ from a transaction processing database? What are the characteristics of a data warehouse? What are the components of a data warehousing system? How is a data warehouse created? How is a data warehouse accessed?

2. TPS vs. DSS

3. Transaction vs. DSS databases

4. So, can one database support both transaction processing and decision support applications? Yes No

5. What is a data warehouse? A data warehouse is a database designed to support a decision support system. A data warehouse is: Integrated: It is a centralized, consolidated database integrating data from an entire organization. Subject-oriented: Data warehouse data are organized around key subjects. The data are usually arranged by topic, such as customers, products, suppliers, etc. Time-variant: Data in the warehouse contain a time dimension so that they may be used as a historical aggregation. Non-volatile: Once data enter, they seldom leave. Data are appended rather than overwritten. Data are updated in batches.

6. Data warehouse design example

7. 7 Issues in designing a data warehouse Must have a predefined subject focus. Has the potential to be very large – must define the “grain” or granularity level of storage. Will always have a dimension of time. Will contain derived data. Will be a summary of data, rather than each detailed transaction. Does not always adhere to standard normalization rules.

9. 9 Issues in creating a data warehouse How to get accurate and complete data? How to consolidate data? Differing data meanings. Differing storage mechanisms. Differing data formats.

10. 10 Components of a data warehousing system Data store. Extraction/filtering/transformation processes. End user query tools. End user visualization tools.

11. Two-tier data warehouse architecture

12. Three-tier data warehouse architecture

13. 13 Accessing a data warehouse Visualization tools. Graphical. Spreadsheet format - usually Excel or Lotus look-and- feel. Dashboard. Example: http://tomcat.corda.com/superstore/sr.jsp http://tomcat.corda.com/superstore/sr.jsp Query tools. OLAP: Online analytical processing. Data mining: Artificial intelligence based query methods.

14. 14 Online analytical processing Provides multi-dimensional data analysis techniques. Works primarily with data aggregation. Provides advanced statistical analysis. Provides advanced graphical output. Supports access to very large databases. Provides enhanced query optimization algorithms. Lots of acronyms: OLAP, ROLAP, MOLAP, HOLAP. Can be add-ons to existing products, example is Excel. Can have their own user interfaces.

15. OLAP vs. Data Mining questions

16. 16 Data mining Data mining tools: analyze the data; uncover patterns hidden in the data; form computer models based on the findings; and use the models to predict business behavior. Proactive tools. Based on artificial intelligence software such as decision trees, neural networks, fuzzy logic systems, inductive nets and classification networking.

17. 17 What are some applications of data warehousing? Customer relationship management. Business process management. Order management. Strategic decision analysis.