KNOWLEDGE BASED DECISION SUPPORT SYSTEM (NIT064)
SECTION – A
(10 × 2 = 20 Marks | Short Answers)
(a) What do you mean by Knowledge?
Knowledge is processed and structured information combined with experience, context, and understanding, which helps in decision-making and problem solving.
(b) Knowledge Modeling
Knowledge modeling is the process of representing human knowledge in a formal structure such as rules, frames, semantic networks, or ontologies so that it can be used by computer systems.
(c) Difference between Modeling and Analysis
Modeling focuses on building representations of real-world systems, while analysis focuses on studying and interpreting models to draw conclusions or support decisions.
(d) Data Acquisition
Data acquisition is the process of collecting raw data from various sources such as databases, sensors, surveys, or transactions for further processing and analysis.
(e) Business Intelligence
Business Intelligence (BI) refers to tools and techniques used to collect, analyze, and present business data to support better strategic and operational decisions.
(f) Major Components of DSS
Major components include: Database management system
Model base management system Knowledge base
User interface
(g) Text Mining vs Data Mining
Text mining extracts useful information from unstructured text data, while data mining discovers patterns and relationships from structured databases.
(h) DSS Integrated Tools and Objectives
DSS integrated tools include databases, models, analytics, and visualization tools. Their objective is to improve decision quality, speed, and effectiveness.
(i) Role of Internet in Management Support System
The internet enables real-time data access, collaboration, cloud-based DSS, and global connectivity, enhancing managerial decision support.
(j) Application Domains of KBDSS
Applications include healthcare diagnosis, financial planning, supply chain management, risk analysis, marketing strategy, and expert systems.
SECTION – B
(Attempt Any Five | 5 × 10 = 50 Marks)
(a) Decision Making vs Problem Solving
Decision Making involves selecting the best alternative from available options.
Problem Solving involves identifying a problem, analyzing it, and finding a solution.
Example:
Choosing a supplier is decision making, while resolving low production efficiency is problem solving.
(b) DBMS vs Model Based Management System
A DBMS manages structured data and supports data storage, retrieval, and updates.
A Model Based Management System manages mathematical and analytical models used for simulations and forecasting.
DBMS answers what happened, while model systems answer what will happen.
(c) Normative vs Descriptive Decision Making
Normative approach describes how decisions should be made logically and optimally.
Descriptive approach explains how decisions are actually made by humans, often influenced by bias and experience.
(d) OLAP and Spreadsheet-Based Models
OLAP provides multidimensional data analysis using operations like roll-up, drill-down, and slicing.
Its spreadsheet-like structure allows managers to analyze large datasets easily and interactively.
(e) Business Analysis and Visualization in DSS
Business analysis identifies trends, patterns, and insights from data.
Visualization uses charts, dashboards, and graphs to present complex data in an understandable form, supporting faster decisions.
(f) Collaborative Computing Technologies
These technologies support group decision-making and teamwork. Examples include:
Group DSS Video conferencing
Shared databases Workflow systems
They improve communication and coordination among decision makers.
(g) Agile Development and Extreme Programming
Agile development focuses on iterative development, flexibility, and customer feedback.
Extreme Programming (XP) emphasizes small releases, continuous testing, pair programming, and simplicity.
(h) E-Business and Its Impact on Future DSS
E-business uses digital platforms for business processes.
Integration of e-commerce with DSS enables real-time analytics, customer behavior analysis, and global decision support, shaping future management systems.
SECTION – C
(Attempt Any Two | 2 × 15 = 30 Marks)
3) Explanation Subsystem of Rule-Based Expert Systems
The explanation subsystem answers questions such as:
Why a conclusion was reached
How a decision was derived
What rules were used
Role:
It increases user trust, transparency, and understanding of system decisions.
How questions are answered:
The system traces rule firing sequences and inference paths from the knowledge base to explain results.
4) Case-Based Reasoning (CBR)
Case-Based Reasoning solves new problems by adapting solutions from past similar cases.
Main Components of CBR:
Case library (past cases)
Retrieval mechanism
Adaptation module
Revision and learning module
CBR is widely used in medical diagnosis, legal reasoning, and customer support systems.
5) Simulation Methodology vs Simon’s Four-Phase Model
Simon’s Four Phases:
Intelligence – problem identification
Design – develop alternatives
Choice – select best alternative
Implementation – apply decision
Simulation Methodology:
Simulation mainly supports the design and choice phases by testing alternatives under different conditions.
Conclusion:
Simulation does not map directly to all four phases but strongly supports decision evaluation and choice.
