1. 1 CS318 Decision Support Systems Rationale: This course aims to provide students with fundamental knowledge on decision support systems for managers and IS developers. This discipline is a combination of several different disciplines: mathematical models, database systems, expert systems, neural networks, data mining, operations research, management science, user interface, graphics techniques and programming techniques.

2. 2 Course Outline PART A IData Warehousing  1. Basic concepts of data warehousing  2. Data warehouse architectures  3. Some characteristics of data warehouse data  5. The reconciled data layer  6. Data transformation  7. The derived data layer  8. The user interface IIData Warehouse Design  1. Requirement Specification  2. Conceptual Design  3. Logical Design  4. Conclusions  5. Case Study

3. 3 PART B Decision Support System IManagement support systems: An Overview  1. Managers and Computerized Support  2. Decision Support Systems (DSS)  3. Development process of a DSS IIDecision Making Systems, Models, and Support  1. Decision Making  2. Systems  3. Models  4. The Modeling Process  5. Decision making: the Intelligence phase  6. Decision making: the Design phase  7. Decision making: the Choice phase  8. Decision making: the Implementation phase  9. How decisions are supported  10. Human cognition and decision styles.

4. 4 IIIDecision Support Systems : An Overview  1. Introduction: What is a DSS;  2. Characteristic and Capabilities of DSS  3. Components of DSS  4. Data Management Subsystem  5. Model Management Subsystem  6. User Interface subsystem  7. Knowledge-based Management subsystem  8. DSS hardware  9. DSS classification IVModeling and Analysis  1. Modeling for MSS  2. Static and Dynamic Models  3. Treating Certainty, Uncertainty, and Risk  4. Influence diagrams  5. Modeling with Spreadsheets  6. MSS mathematical models  7. Search approaches  8. Simulation  5. Model Base Management System

5. 5 VData Management  1 Data, Information, Knowledge  2 Data collection problems and quality  3 Database management systems in DSS  4.Data Warehousing  5. OLAP  6. Data Mining  7. Data Visualization and Multidimensionality  8. GIS VIDecision Support System Development  1. The system development life cycle tools  2. Alternative development methodologies  3. DSS development methodologies  4. DSS technology level and tools  5. DSS development tool selection  6. Team-developed DSS  7. User developed DSS

6. 6 PART C: Data Mining I Classification  1. Classification with decision trees  2. Neural networks. II Time Series Forecasting (Part I)  What is a time series  Components of time series  Evaluation Methods of forecast  Smoothing methods of time series III Time series Forecasting (Part II)  Stationary and nonstationary process  Autocorrelation function  Autoregressive models AR  Moving Average models MA  ARMA models  ARIMA models  Estimating and checking ARIMA models(Box-Jenkins Methodology)

7. 7 IV Neural networks in business applications  Customer Loan Classification  Bankruptcy Prediction  Exchange rate Forecasting  Conclusions V DSS case study: A knowledge-based DSS for enterprise mergers and acquisitions  1. Introduction  2. Mergers and acquisition method and process  3. The architecture of the knowledge-based DSS.  4. Conclusions  References References [1] Turban, E., Aronson,J.E., Decision Support Systems and Intelligent Systems- 7th Edition, Prentice-Hall, 2005. Instructor: Assoc. Prof. Dr. Duong Tuan Anh

8. 8 Evaluation systems 40% (Midterm Exam) 40% (Final Exam) 20% (Term-paper)