1. DSS & Warehousing Systems
Chapter 14
Efrem Mallach
Prepared by
Luvai Motiwalla
Irwin/McGraw-Hill
Copyright © 2000 by The McGraw-Hill Companies, Inc. All rights reserved.

2. Analyzing the Contents of the Data Warehouse
Active analysis: user queries
OLAP
OLAP software architecture
Web – based OLAP
General OLAP product characteristics
Automated analysis: data mining
Creating a decision tree
Neural networks
Nearest neighbor approaches
Putting the results to use

3. Active Analysis : User Queries
Many decisions can be made well on the basis of a series of questions and answers.

4. OLAP
Online analytical processing(OLAP) – is the mode of operation where the user chooses to go deeper into detail (drilling down) until the necessary decisions have been made.

5. OLAP Software Architecture
Most OLAP software whichever approach it follows, is designed to work in client/server mode.

6. Web – Based OLAP
These products allow users of web browsers or network computers to access and analyze data warehouse data.

7. General OLAP Product Characteristics
There are many OLAP products on the market but fortunately for the Prospective OLAP software buyers they all share some common characteristics.

8. Automated Analysis : Data Mining
In data mining , the user specifies what he or she wants to find out and on what he or she wants the answer to depend.
Advantages and disadvantages of data mining

9. Creating a Decision Tree
How is a decision tree created?
Correlation and other statistical analysis

10. Neural Networks
A neural network tries to mimic the way human and animal brains functions

11. Nearest Neighbor Approaches
This approach involves finding old cases that are close to each new one and assuming its outcome will match the majority of those neighbors.

12. Putting the Results to Use
A good data mining project will divide the business entities in the data warehouse database into sections that vary significantly along a dimension of value to the organization.