Decision Support System(DSS) Syllabus

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This page contains Syllabus of Decision Support System of CSIT.

Title Decision Support System
Short Name DSS
Course code CSC460
Nature of course Theory + Lab
Semester eighth-semester
Full marks 60 + 20 + 20
Pass marks 24 + 8 + 8
Credit Hrs 3
Elective/Compulsary Elective

Course Description

Course Synopsis: This course covers introduction to decision support systems; DSS

components; Decision making; DSS software and hardware; developing DSS;

DSS models; types of DSS; data mining; artificial intelligence and expert

Systems.


Goal: The course is devoted to introduce decision support systems; show their relationship to

other computer-based information systems, demonstrate DSS development approaches,

and show students how to utilize DSS capacities to support different types of decisions.


Units and Unit Content

1. Decision Making and Computerized Support
teaching hours: 8 hrs

1.1. Management Support Systems:

An OverviewManagers and Decision-Making; Managerial Decision-Making and Information Systems;

 Managers and Computer Support; Computerized Decision Support and the Supporting

Technologies; A Framework for Decision Support; The Concept of Decision Support Systems;

Group Support Systems; Enterprise Information Systems; Knowledge Management Systems;

Expert Systems; Artificial Neural Networks; Advanced Intelligent Decision Support Systems;

Hybrid Support Systems


1.2. Decision-Making Systems, Modeling, and Support Decision-Making:

Introduction and Definitions; Systems; Models; Phases of the Decision-

Making Process; Decision-Making: The Intelligence Phase; Decision-Making: The Design

Phase; Decision-Making: The Choice Phase; Decision-Making: The Implementation Phase; How

Decisions Are Supported; Personality Types, Gender, Human Cognition, and Decision Styles;

The Decision-Makers


2. Decision Support Systems
teaching hours: 14 hrs

2.1. Decision Support Systems: 

An Overview DSS Configurations; What Is a DSS?; Characteristics and Capabilities of DSS; Components of

DSS; The Data Management Subsystem; The Model Management Subsystem; The User

Interface (Dialog) Subsystem; The Knowledge-Based Management Subsystem; The User; DSS

Hardware; DSS Classifications

2.2. Modeling and Analysis
MSS Modeling; Static and Dynamic Models; Certainty, Uncertainty, and Risk; Influence
Diagrams; MSS Modeling with Spreadsheets; Decision Analysis of a Few Alternatives (Decision
Tables and Decision Trees); The Structure of MSS Mathematical Models; Mathematical
Programming Optimization; Multiple Goals, Sensitivity Analysis, What-If, and Goal Seeking;
Problem-Solving Search Methods; Heuristic Programming; Simulation; Visual Interactive
Modeling and Visual Interactive Simulation; Quantitative Software Packages; Model Base
Management

2.3.Business Intelligence: Data Warehousing, Data Acquisition, Data Mining, Business
Analytics, and Visualization
The Nature and Sources of Data; Data Collection, Problems, and Quality; The Web/Internet and
Commercial Database Services; Database Management Systems in Decision Support Systems/
Business Intelligence; Database Organization and Structures; Data Warehousing; Data Marts;
Business Intelligence/Business Analytics; Online Analytical Processing (OLAP); Data Mining;
Data Visualization, Multidimensionality, and Real-Time Analytics; Geographic Information
Systems; Business Intelligence and the Web: Web Intelligence/Web Analytics

2.4. Decision Support System Development
Introduction to DSS Development; The Traditional System Development Life Cycle; Alternative
Development Methodologies; Prototyping: The DSS Development Methodology; Change
Management; DSS Technology Levels and Tools; DSS Development Platforms; DSS
Development Tool Selection; Team-Developed DSS; End User Developed DSS; Putting The
DSS Together






3. Knowledge Management
teaching hours: 5 hrs

3.1. Knowledge Management

Introduction to Knowledge Management; Organizational Learning and Transformation;

Knowledge Management Initiatives; Approaches to Knowledge Management; Information

Technology in Knowledge Management; Knowledge Management Systems Implementation;

Roles of People in Knowledge Management; Ensuring Success of Knowledge Management


4. Intelligent Decision Support Systems
teaching hours: 18 hrs

4.1. Artificial Intelligence and Expert Systems:

Knowledge-Based Systems Concepts and Definitions of Artificial Intelligence; Evolution of Artificial Intelligence; TheArtificial Intelligence Field; Basic Concepts of Expert Systems; Applications of Expert Systems; Structure of Expert Systems; How Expert Systems Work; Problem Areas Suitable for Expert Systems; Benefits and Capabilities of Expert Systems; Problems and Limitations of Expert Systems; Expert System Success Factors; Types of Expert Systems; Expert Systems on the Web

4.2. Knowledge Acquisition, Representation, and Reasoning

Concepts of Knowledge Engineering; Scope and Types of Knowledge; Methods of Knowledge Acquisition from Experts; Knowledge Acquisition from Multiple Experts; Automated Knowledge Acquisition from Data and Documents; Knowledge Verification and Validation; Representation of Knowledge; Reasoning in Rule-Based Systems; Explanation and Metaknowledge; Inferencing with Uncertainty; Expert Systems Development; Knowledge Acquisition and the Internet

4.3. Advanced Intelligent Systems
Machine-Learning Techniques; Case-Based Reasoning; Basic Concept of Neural Computing;
Learning in Artificial Neural Networks; Developing Neural Network-Based Systems; Genetic
Algorithms Fundamentals; Developing Genetic Algorithm Applications; Fuzzy Logic
Fundamentals; Developing Integrated Advanced Systems

4.4. Intelligent Systems over the Internet
Web-Based Intelligent Systems; Intelligent Agents: An Overview; Characteristics of Agents;
Why Intelligent Agents?; Classification and Types of Agents; Internet-Based Software Agents;
DSS Agents and Multi-Agents; Semantic Web: Representing Knowledge for Intelligent Agents;
Web-Based Recommendation Systems; Managerial Issues of Intelligent Agents


Lab and Practical works

Laboratory Work:

The laboratory should contain the concepts of artificial intelligence that are

applicable to the development of decision support systems.