B.E DEGREE PROGRAMME COMPUTER SCIENCE AND ENGINEERING
(Offered in Colleges affiliated to Anna University)
CURRICULUM AND SYLLABUS – REGULATIONS – 2004
B.E. COMPUTER SCIENCE AND ENGINEERING
LIST OF ELECTIVES FOR COMPUTER SCIENCE AND ENGINEERING
  CS1022        KNOWLEDGE BASED DECISION SUPPORT SYSTEM                          3  0  0  100
AIM
  There has been a radical shift in the management parlance.  Organizations can use Intranets and Internets to analyze various aspects  about the performance and predict the future. This course aims at  exposing the student to one of the important applications of the  computer.
OBJECTIVE
  The course has been so designed as to include.
  • Development of support system
  • Methods of managing knowledge
  • Intelligent decision system development 
UNIT I   INTRODUCTION                            9
  Decision making, Systems, Modeling, and support – Introduction and  Definition – Systems – Models – Modeling process – Decision making: The  intelligence phase – The design phase - The choice phase – Evaluation:  The implementation phase –Alternative Decision – Making models –  Decision support systems – Decision makers - Case applications.
  UNIT II  DECISION SUPPORT SYSTEM DEVELOPMENT                         9 
  Decision Support System Development: Introduction - Life cycle –  Methodologies – prototype – Technology Levels and Tools – Development  platforms – Tool selection – Developing DSS
  Enterprise systems: Concepts and Definition – Evolution of information  systems – Information needs – Characteristics and capabilities –  Comparing and Integrating EIS and DSS – EIS data access, Data Warehouse,  OLAP, Multidimensional analysis, Presentation and the web – Including  soft information enterprise on systems - Organizational DSS – supply and  value chains and decision support – supply chain problems and solutions  – computerized systems MRP, ERP, SCM – frontline decision support  systems.
UNIT III  KNOWLEDGE MANAGEMENT                            9
  Introduction – Organizational learning and memory – Knowledge  management –Development –methods, Technologies, and Tools – success  –Knowledge management and Artificial intelligence – Electronic document  management.
  Knowledge acquisition and validation:  Knowledge engineering – Scope –  Acquisition methods - Interviews – Tracking methods – Observation and  other methods – Grid analysis – Machine Learning: Rule induction,  case-based reasoning – Neural computing – Intelligent agents – Selection  of an appropriate knowledge acquisition methods – Multiple experts –  Validation and verification of the knowledge base – Analysis, coding,  documenting, and diagramming – Numeric and documented knowledge  acquisition – Knowledge acquisition and the Internet/Intranets.
  Knowledge representation: Introduction – Representation in logic and  other schemas – Semantic networks – Production rules – Frames – Multiple  knowledge representation – Experimental knowledge representations  -  Representing uncertainty. 
UNIT IV  INTELLIGENT SYSTEM DEVELOPMENT              9
  Inference Techniques: Reasoning in artificial intelligence – Inference  with rules: The Inference tree – Inference with frames – Model-based  and case-based reasoning - Explanation and Meta knowledge – Inference  with uncertainty – Representing uncertainty – Probabilities and related  approaches – Theory of certainty – Approximate reasoning using fuzzy  logic.
  Intelligent Systems Development: Prototyping: Project Initialization –  System analysis and design – Software classification: Building expert  systems with tools – Shells and environments – Software selection –  Hardware –Rapid prototyping and a demonstration prototype - System  development –Implementation – Post implementation.
 
  UNIT V  MANAGEMENT SUPPORT SYSTEMS                           9
  Implementing and integrating management support systems –  Implementation: The major issues  - Strategies – System integration –  Generic models MSS, DSS, ES – Integrating EIS, DSS and ES, and global  integration – Intelligent DSS – Intelligent modeling and model  management – Examples of integrated systems – Problems and issues in  integration.
  Impacts of Management Support Systems – Introduction – overview –  Organizational structure and related areas – MSS support to business  process reengineering – Personnel management issues – Impact on  individuals – Productivity, quality, and competitiveness – decision  making and the manager manager’s job – Issues of legality, privacy, and  ethics – Intelligent systems and employment levels – Internet  communication – other societal impacts – managerial implications and  social responsibilities –
 
  TOTAL : 45
TEXT BOOK
  1. Efrain Turban, Jay E.Aronson, “Decision Support Systems and  Intelligent Systems” 6th Edition, Pearson Education, 2001.
 
  REFERENCES
  1. Ganesh Natarajan, Sandhya Shekhar, “Knowledge management – Enabling  Business Growth”, Tata McGraw-Hill, 2002.
  2. George M.Marakas, “Decision Support System”, Prentice Hall, India,  2003.
  3. Efrem A.Mallach, “Decision Support and Data Warehouse Systems”,  Tata McGraw-Hill, 2002. 
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