(SEM VI) EVEN SEMESTER THEORY EXAMINATION 2017-18 KNOWLEDGE BASED & DECISION SUPPORT SYSTEM

Knowledge-Based & Decision Support System (NIT-064)

Complete Section-Wise Explanation – B.Tech Semester VI

Introduction to the Subject

Knowledge-Based & Decision Support System (KBDSS) is an interdisciplinary subject that combines information systems, artificial intelligence, data management, and decision science. The main objective of this subject is to understand how computers can assist humans in making better, faster, and more informed decisions, especially in complex and uncertain situations.

This subject explains:

Decision making and decision processes                 Decision Support Systems (DSS)

Knowledge-based systems and expert systems       Simulation models

Group decision support systems                              Data warehousing and data cleansing

Intelligent decision-making tools

The paper is divided into three sections: A, B, and C, and students must attempt questions according to the given instructions.

SECTION A – Basic Concepts & Definitions

Pattern:
Attempt all questions
10 questions × 2 marks = 20 marks

Nature of Section A

Section A checks your conceptual clarity and definitions. Answers should be short, precise, and to the point, usually written in two or three lines. This section is highly scoring if concepts are well prepared.

Explanation of Section A Questions

A Decision Support System (DSS) is a computer-based system that helps decision-makers use data, models, and analytical tools to solve semi-structured or unstructured problems.

The eight categories of information systems include transaction processing systems, management information systems, decision support systems, executive information systems, expert systems, office automation systems, knowledge management systems, and enterprise systems.

A structured decision is one where the decision-making procedure is well defined and repetitive, such as payroll processing or inventory reordering.

The two fundamental operations involved in converting data into information are data processing and data analysis. Processing organizes raw data, while analysis gives meaning and insight.

Individual, multi-individual, and group decisions differ in participation. Individual decisions are made by one person, multi-individual decisions involve several independent decision-makers, and group decisions involve collective discussion and consensus.

An open system interacts with its environment by exchanging information, energy, or resources, unlike a closed system.

A data warehouse is a centralized repository that stores integrated, historical data from multiple sources to support analysis and decision-making.

Hypertext is a system that allows non-linear navigation of information using links, commonly used on the web.

Data cleansing is the process of removing errors, inconsistencies, duplicates, and missing values from data. It is necessary to improve data quality and decision accuracy.

Pseudo-random numbers are numbers generated by algorithms that appear random but follow a deterministic process, commonly used in simulation models.

SECTION B – Conceptual & Application-Based Questions

Pattern:
Attempt any one part of each question
(Questions carry 10 marks each)

Nature of Section B

Section B tests your understanding of decision models, data concepts, and simulation basics. Answers should be written in paragraph form with proper explanation, sometimes supported by diagrams or examples.

Explanation of Section B Topics

Static Simulation Model

A static simulation model represents a system at a particular point in time and does not consider changes over time. It is used where time is not a significant factor, such as estimating demand or capacity under fixed conditions.

Basic Subsets of Artificial Intelligence

The basic subsets of AI include expert systems, machine learning, natural language processing, robotics, and computer vision. These subsets contribute to intelligent decision-making systems.

Metadata

Metadata is “data about data.” It describes the structure, source, format, and meaning of stored data. Metadata improves data understanding, integration, and retrieval in data warehouses.

SECTION C – Knowledge-Based Systems & DSS Applications

Pattern:
Attempt any one part of each question
(Questions carry 10 marks each)

This section has long descriptive questions and plays a major role in scoring high marks.

Question 7 – Expert Systems & Group Decision Support

Expert System Shell

An expert system shell is a software framework that includes inference engine, user interface, and explanation facility but does not contain domain-specific knowledge. Knowledge can be added later to build a complete expert system.

Electronic Meeting System (EMS)

An electronic meeting system supports group decision-making by allowing participants to share ideas, vote, brainstorm, and evaluate alternatives using computers and networks. EMS improves participation, reduces dominance, and speeds up decision-making.

Question 6 – Simulation & Modeling

Simulation models imitate real-world systems to analyze behavior under different conditions. Simulation is widely used in decision support when real-world experimentation is expensive, risky, or impractical.

Question 5 – Decision Making & DSS Models

Decision-making involves identifying problems, generating alternatives, evaluating options, and selecting the best solution. DSS models help decision-makers analyze alternatives using quantitative and qualitative data.

Question 4 – Data & Knowledge Management

Data warehousing, data cleansing, and knowledge management play a crucial role in supporting intelligent decision systems by ensuring accurate, integrated, and meaningful data.

Question 3 – DSS Architecture & Components

A DSS typically consists of:

Database management system

Model management system

Knowledge base

User interface

These components work together to assist managers in decision-making.