1. Building Knowledge-Driven DSS and Mining Data
We can organize knowledge and make it available for business uses

2. Building KDSS and Mining Data, D. J. Power
Knowledge driven DSS
Knowledge-driven DSS, suggestion DSS, rule-based DSS and intelligent DSS are overlapping terms for DSS built using artificial intelligence technologies
KDSS provide suggestions for business decision makers
Building KDSS and Mining Data, D. J. Power

3. Building KDSS and Mining Data, D. J. Power
KDSS/Expert Systems
Three Components
Knowledge of symptoms and indicators related to a particular topic or domain
Understanding of the relations among symptoms and of problems and solutions within that domain
“Skill” or methods for solving some of the problems
A KDSS/ES can give the reasoning behind a conclusion it has reached
Building KDSS and Mining Data, D. J. Power

4. Data Mining and Knowledge Discovery
Helps companies discover hidden relationships and patterns in their data
Some feel Data Mining can help a company gain a competitive advantage
Knowledge Discovery is the broader term
Building KDSS and Mining Data, D. J. Power

5. Building KDSS and Mining Data, D. J. Power
Al Vocabulary
Knowledge Acquisition – The extraction and formulation of knowledge derived from various sources, especially from experts
Development environment – software for creating and maintaining a knowledge base and software called an inference engine
Inference engine processes a set of rules
Domain expert – person who has expertise in the task for which a specific system is being developed
Building KDSS and Mining Data, D. J. Power

6. Building KDSS and Mining Data, D. J. Power
Knowledge Base
A knowledge base is a collection of organized facts, rules, and procedures
Knowledge base has a description of the elements in the process along with their characteristics, functions, and relationships
Also contains rules about actions to implement as a result of certain events
Building KDSS and Mining Data, D. J. Power

7. Characteristics of KDSS
Aids/assists managers in problem solving
Use knowledge stored as rules, frames, or probabilities
People interact with a Knowledge-Driven DSS when performing a specific task
Knowledge-Driven DSS end Expert Systems base recommendations on human knowledge and assist in performing very limited tasks
KDSS does NOT “think”
Building KDSS and Mining Data, D. J. Power

8. Components of a KDSS Communications Component User interface component
Database or Knowledge base Component
Inference Engine Model Component
Communications Component
User interface component
Building KDSS and Mining Data, D. J. Power

9. Knowledge-Driven vs. Model-Driven DSS
Knowledge-Driven DSS based on Expert System technologies attempts to reason about an input using its knowledge base and logical rules for problem solving
Model-Driven DSS has a sequence of predefined instructions (a model) for responding to a change in inputs
Building KDSS and Mining Data, D. J. Power

10. Building KDSS and Mining Data, D. J. Power
Comparing KDSS to MDSS
Knowledge-Driven DSS = Knowledge base + Inference Engine (rule processor)
Model-Driven DSS = Structured Data + Quantitative Models
Building KDSS and Mining Data, D. J. Power

11. Knowledge-Driven DSS Design and Development
Involves a development team and a domain expert creating a knowledge base for a target user group
Waterman proposed a widely accepted approach:
Identification of domain
Conceptualization
Formalization
Implementation
Testing
Building KDSS and Mining Data, D. J. Power

12. Choosing a project – Telephone test
Can a domain expert solve the problem and support decision making using a telephone exchange with a decision maker?
Ask structured rather than open-ended questions
“Yes” the telephone exchange works; then a KDSS can be developed
“No” development of a KDSS will likely be unsatisfactory
Building KDSS and Mining Data, D. J. Power

13. Rules Interconnected and nested IF-THEN statements
IF INCOME > $45,000 (condition)
AND IF SEX = “M” (condition)
THEN ADD to Target list (action)
A rule is a formal way of specifying a recommendation, directive, or strategy, expressed in an IF premise THEN conclusion structure
Rules are relationships rather than instructions

14. Chaining
Forward – start from known facts looks at IF statements to find matches to user’s responses
Backward – start with a hypothesized solution looks at THEN statements works backwards to find the starting conditions

15. Chaining You want to find a flight from CC to Beijing, China: Forward
Start by listing all flights leaving CC and mark intermediary cities. Look for flights out of intermediaries until you find all paths to Beijing. You are working forward toward your goal. This is a data-driven
Backward
Start with flights that arrive in Beijing and work backwards to eventually find a chain to CC, TX. This is a goal-driven

16. Advantages and Limitations of Rules
Rules are easy to understand
Explanations are easy to provide when knowledge is stored as rules
Modification and maintenance are relatively easy
Can easily combine uncertainty with knowledge
Major limitation
The most important complex knowledge is difficult to represent using rules
Using rules does not work for all applications

17. Knowledge-Driven DSS Examples
TAXADVISOR
Designed to assist with large estate planning
XCON
Developed to configure VAX 11/780 computer system
Customer Support Intelligent System
Designed by Compaq to provide expert diagnosis and recommendations to computer users who were having computer problems
Building KDSS and Mining Data, D. J. Power

18. Data Mining Predictive Descriptive
Can be used to forecast explicit values, based on patterns determined from known results
For example, from a database of customers who have already responded to a particular offer, a model can be built that predicts which prospects are likeliest to respond to the same offer.
Descriptive
Describe patterns in existing data and are generally used to create meaningful subgroups such as demographic clusters
Once a descriptive model is identified it may be used for target marketing or other decision support tasks

19. Building KDSS and Mining Data, D. J. Power
Data Mining Tools
Case-Based Reasoning - similarity
Data Visualization – graphical presentation
Fuzzy Query and Analysis – “close”
Genetic Algorithms – optimization
Neural Networks – learn patterns from data
Data Mining techniques are NOT fundamentally different from earlier quantitative and statistical analysis techniques – they are still used
Building KDSS and Mining Data, D. J. Power

20. Building KDSS and Mining Data, D. J. Power
Data Mining Process
As defined by the Gartner Group
Select and prepare the data to be mined
Qualify the data via cluster and feature analysis
Select one or more data mining tools
Apply the data mining tool
Apply the knowledge discovered to the company’s specific line of business to achieve a business goal
Building KDSS and Mining Data, D. J. Power

21. Building KDSS and Mining Data, D. J. Power
Data Mining Examples
Credit risk analysis
Siemens Technical Customer support – case based reasoning
Building KDSS and Mining Data, D. J. Power

22. Evaluating Development Packages
Cost
Scalability
Security
Development Features
Ease of Installation and Use
Building KDSS and Mining Data, D. J. Power