Model-oriented: Simulation Optimization Data-oriented OLAP (Online Analytical Processing) Data Mining Expert Syste

1. تکنیک های مورد استفاده در پشتیبانی تصمیم • Model-oriented: • Simulation • Optimization • Data-oriented • OLAP (Online Analytical Processing) • Data Mining • Expert Systems • Fuzzy Logic • Neural Systems • Case-based Reasoning

2. Simulation • Model-oriented: • Simulation • Manager’s Mental Model (Variables influence the profit) - Tentative assumptions • Formalized in DSS as a mathematical model • - Using What-if to try out different assumptions • - MATLAB

3. Optimization • Model-oriented: • Optimization • Starts with Manager’s optimization criteria • Using mathematical model to determine optimal decisions based on the criteria • - MATLAB

4. OLAP • Data-oriented: • OLAP • Exploring transaction data from transactional Databases • Dimensions of Data • OLAP raise from difficulties analyzing databases – Slowed down processing • Using data warehouse: Extraction, consolidation, and filtering • So: Transaction processing and OLAP without mutual interference • - Targit - Logixml

5. OLAP

6. Data Mining • Data-oriented: • Data mining • Using analytical tools to find patterns (Correlation) in transaction databases such as customer receipts • Wonderful for Marketing Research • Such as correlation between two different product sold in specific hours • Such as a bank finding that customers with multiple accounts were unprofitable • - Rapid Miner

7. Expert System • Expert Systems: • Supporting professionals engaging in design, diagnosis, or evaluation of complex situations requiring knowledge • Converting data into recommendations • Less experienced people doing similar task as experts do • Represent knowledge in an explicit form – So-called: knowledge-based systems • Representing knowledge as If-then rules • - CLIPS

8. Expert System • Example: Whether to grant a business loan: • If: The applicant is current on all debts, and the applicant has been profitable for two years, and the applicant has strong market position, • Then: The applicant is an excellent credit risk

9. Expert System • Four major components of an expert system: • Knowledge-base: Set of facts and if-then rules • Database of facts • The inference engine: Using rules in the knowledge-base and facts in the database to infer new facts • The interface: interacting with the user • Explanation module: how a particular fact wasinferred

10. Expert system

11. Expert System • Forward chaining: starting with data and try to draw conclusions • Backward chaining: Starting with tentative conclusions and then look for facts in the database • Risky to trust expert system: not truly experts, different situations • Unless used in the same situations

12. Fuzzy Logic • Minimizing problems with expert systems: just true or false • In expert systems: Trivial difference between 1$ profit and 1$ lost, affecting decision as much as difference between 100$ million profit and a 1$ lost • Combining a number of different rules based on conditions such as: • Very profitable • Profitable • slightly profitable • and so on... • - FuzzyTECH

13. Neural Network • Neural Networks: • Using Statistical models to find patterns in data • Model the way human brain works • Recognizes patterns based on examples that have been trained to it • Each training example is a set of characteristics and a result: such as whether or not a loan was repaid • Applying numerical weights to characteristics based on examples • - MATLAB

14. Neural Network