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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.

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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

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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.

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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

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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

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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)

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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

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