1. Introduction of Week 9 Return assignment 5-2
Collect assignment and 8-1
Review of week 8
Trigger
SDLC vs. DBLC
Conceptual design -> logical -> physical
Database design topics: security, backup and recovery, top-down vs. bottom-up, centralized vs. decentralized
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2. Module 4 Database Warehousing and Data Mining

3. Data Warehouse-Based Solutions
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4. Decision Support
It is a methodology (or series of methodologies) designed to extract information from data and to use such information as a basis for decision making
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5. Decision Support Systems
Arrangement of computerized tools used to assist managerial decision making within a business
Usually requires extensive data “massaging” to produce information
Used at all levels within an organization
Often tailored to focus on specific business areas
Provides ad hoc query tools to retrieve data and to display data in different formats
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6. Main Components of DSS Data store component
Basically a DSS database
Data extraction and filtering component
Used to extract and validate data taken from operational database and external data sources
End-user query tool
Used to create queries that access database
End-user presentation tool
Used to organize and present data
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7. Main Components of DSS
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8. Transforming Operational Data
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9. DSS Data Characteristics
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10. Example 1 of Sales History
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11. Example 2 of Sales Summaries
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12. The Data Warehouse
Integrated, subject-oriented, time-variant, nonvolatile database that provides support for decision making
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13. Data Warehouse Characteristics
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14. Creating a Data Warehouse
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15. DSS Architectural Styles
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16. Online Analytical Processing
Advanced data analysis environment that supports decision making, business modeling, and operations research
OLAP systems share four main characteristics:
Use multidimensional data analysis techniques
Provide advanced database support
Provide easy-to-use end-user interfaces
Support client/server architecture
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17. Comparison - View of Sales
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18. OLAP Server Arrangement
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19. OLAP Server Arrangement (2)
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20. Typical ROLAP C/S Architecture
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21. MOLAP C/S Architecture
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22. Star Schemas
Data modeling technique used to map multidimensional decision support data into a relational database
Creates the near equivalent of a multidimensional database schema from the existing relational database
Yield an easily implemented model for multidimensional data analysis, while still preserving the relational structures on which the operational database is built
Has four components: facts, dimensions, attributes, and attribute hierarchies
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23. Simple Star Schema
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24. Possible Attributes for Sales Dimensions
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25. 3-D View of Sales
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26. Slice and Dice View of Sales
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27. Location Attribute Hierarchy
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28. Attribute Hierarchies In Multidimensional Analysis
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29. Star Schema for Sales
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30. Orders Star Schema
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31. Normalized Dimension tables
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32. Data Warehouse Project
Numerous constraints:
Available funding
Management’s view of the role played by an IS department and of the extent and depth of the information requirements
Corporate culture
No single formula can describe perfect data warehouse development
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33. Data Warehouse Implementation Road Map
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34. Data Mining Problem: too much data and not enough information
Tools that:
analyze data
uncover problems or opportunities hidden in data relationships,
form computer models based on their findings, and then
use the models to predict business behavior
Require minimal end-user intervention
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35. Knowledge From Data
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36. Data-Mining Phases
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37. Sample of DW Vendors
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38. Wrap Assignment 9-1: SQL Lab 4 OLAP Question 16 on page 603
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