CS318 Decision Support Systems
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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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Course Outline
PART A
 I
Data 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
 II
Data Warehouse Design
 1. Requirement Specification
 2. Conceptual Design
 3. Logical Design
 4. Conclusions
 5. Case Study
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PART B Decision Support System
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Management support systems: An Overview
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II
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1. Managers and Computerized Support
2. Decision Support Systems (DSS)
3. Development process of a DSS
Decision 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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III
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IV
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Decision 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
Modeling 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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V
Data 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
VI Decision 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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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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IV Neural networks in business applications
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Customer Loan Classification
Bankruptcy Prediction
Exchange rate Forecasting
Conclusions
V DSS case study: A knowledge-based DSS for enterprise
mergers and acquisitions
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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 Systems7th Edition, Prentice-Hall, 2005.
Instructor:
Assoc. Prof. Dr. Duong Tuan Anh
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Evaluation systems
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40% (Midterm Exam)
40% (Final Exam)
20% (Term-paper)
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