Building Knowledge-Driven DSS and Mining Data

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

2. 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. 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. 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. 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 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. 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 • 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. 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. 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. 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