Course Details

1. Describe the generic structure of decision support systems, identify their categories and classes and discuss the behavioural and normative theories of decision making.
2. Recognize the value of decision support systems to individuals and organisations and report current practices in the use of decision support systems.
3. Explain decision analytic techniques and apply these techniques in solving simple decision problems.
4. Define and analyse the concepts and structure of expert systems and data warehouses, and propose ways of dealing with the issues involved in their design and development.
5. Identify, appraise and discuss the main concepts, techniques, technologies and applications of data mining and data visualisation.
6. Design and evaluate a decision support system for supporting a given decision-making process, by analysing and comparing the suitability of the available technologies.

Decision Support Systems: What are decision support systems. Ingredients of a DSS. Data and model management. DSS knowledge base. The concept of knowledge. User interfaces. The DSS user. Categories and classes of DSS systems.

Decisions: What are decisions. Why are decisions so hard. How can DSS help in making decisions. Rational decision-making. Bounded rationality. The process of choice. Cognitive processes. Biases and heuristics in decision-making. Effectiveness and efficiency.

Modelling Decision Processes: Defining the problem and its structure. Decision Models. Types of probability. Techniques for forecasting probabilities. Calibration and sensitivity.

Data Warehouses: Data warehousing concepts. Data warehousing benefits and problems. Stores, warehouses, and marts. The data warehouse architecture. Tools and technologies. The metadata. Data warehouse design. Design methodology. Criteria for assessing dimensionality. Analysis and design tools. Data warehouse implementation.

Data Mining: What is data mining. Online analytical processing and its purpose. Key features of OLAP applications. Multidimensional OLAP. Relational OLAP. Data mining techniques, technologies and applications. Market basket analysis. Limitations and challenges to data mining. The relationship between data mining and data warehousing.

Data Visualisation: What is data visualisation. Human visual perception and data visualisation. Geographical information systems. Data visualisation applications. Data visualisation technologies.

• George Marakas, Decision Support Systems: In the 21st Century, 2nd edition, Prentice Hall, 2002.

• Bernard W. Taylor, Introduction to Management Science, 6th edition, Prentice Hall, 1999.
• Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Second Edition, Morgan Kaufmann, 2005.
• Claudio Carpineto and Giovanni Romano, Concept Data Analysis: Theory and Applications, Wiley, 2004.

The course is delivered through three hours of lectures per week, which include presentation of new material and demonstration of the core concepts and techniques. Lectures also include the discussion and analysis of case studies to provide students with practical understanding of the application of decision support technologies.

Furthermore, a lot of the work is done through homework and private study by the exploration and experimentation with software tools for decision support, such as the Weka data mining platform.

All lecture notes and other material is available to students through the course homepage.