DATABASE MANAGEMENT SYSTEM

1 Department COMPUTER SCIENCE AND ENGINEERING

2 Course Name INFORMATION RETRIEVAL SYSTEM
3 Course Code CS523PE
4 Year/Semester III/I
5 Regulation MLRS-R19
7 Structure of the Theory Practical
course Lecture Tutorials Practical Credit L T P C
3 0 0 3 0 0 0 3
8 Type of course BS HS ES PC PE OE CC MC
× × × × × × ×
9 Course Offered Odd Semester Even Semester ×
Total lecture, tutorial and practical hours for this course Offered
10 (16 weeks of teaching per semester)
Lectures: 48 Hours Tutorials: 0 hours Practical: 32 hours
11 Course Coordinator Dr.BASAVARAJ CHUNCHURE
12 Date Approved by BOS
13 Course Webpage www.mlritm.ac.in/
14 Level Course Code Semester Prerequisites
Prerequisites/
Data
Co-requisites UG- 950548 III-I
Structures

15.Course Overview:

This course provides a comprehensive foundation in IR systems, bridging traditional indexing

methods with advanced multimedia and web-based retrieval techniques.

16. Course Objectives:

The students will try to learn:

 To learn the important concepts and algorithms in IRS

  To understand the data/file structures that are necessary to design, and implement
information retrieval (IR) systems
17. Course Outcomes:
After successful completion of the course, students should be able to:

CO1 Ability to apply IR principles to locate relevant information large collections of data
CO2 Ability to design different document clustering algorithms
CO3 Implement retrieval systems for web search tasks.
CO4 Design an Information Retrieval System for web search tasks.
CO5 Ability to apply IR principles to locate relevant information large collections of data

18. Course Learning Outcome (CLOs):

S. Topic Name CLO Course Learning Outcome Course Bloom's Level

No No Outcome
1 Definition of IR CLO 1 Understand the basic CO1 Understand
Systems definition and objectives of
IR systems.
2 Functional Overview CLO 2 Analyze the relationship of IR CO1 Analyze
systems with DBMS, Digital
Libraries, and Data
Warehouses.
3 IR System Capabilities CLO 3 Understand search, browse, CO1 Understand
and miscellaneous
capabilities of IR systems.
4 Indexing Process CLO 4 Explain the history, CO2 Understand
objectives, and automatic
indexing processes.
5 Data Structures for IR CLO 5 Apply suitable data CO2 Apply
Systems structures like inverted files,
N-Grams, and XML for IR
systems.
6 Automatic Indexing CLO 6 Understand and classify CO3 Understand
Techniques methods of automatic
indexing, including statistical
and natural language
approaches.
7 Document and Term CLO 7 Analyze clustering CO3 Analyze
Clustering techniques and hierarchy
formation in IR systems.
8 User Search CLO 8 Apply relevance feedback CO4 Apply
Techniques and weighted Boolean
searches in IR applications.
9 Information CLO 9 Develop visual CO4 Create
Visualization representations of
information to enhance
 cognition and perception.
10 Text Search Algorithms CLO Design text search CO5 Create
10 techniques using software
and hardware systems.
11 Multimedia CLO Apply retrieval techniques CO5 Apply
Information Retrieval 11 for audio, video, and image
data in multimedia systems.

19. Employability Skills:

Example: Communication skills / Programming skills / Project based skills/

IRS develops key employability skills, including communication skills for articulating IR processes,
programming skills for implementing indexing and search algorithms, and project-based skills
through system design. It also enhances analytical skills for problem-solving, technical expertise in
data structures and multimedia retrieval, and system design skills for creating efficient IR systems.
These competencies prepare students for careers in software development, data management,
and web technologies.

20. Content Delivery / Instructional Methodologies:

Chalk&Talk Assignments MOOC

PowerPointPresentation

Seminars MiniProject Videos

ALP

21. Evaluation Methodology:

The performance of a student in a course will be evaluated for 100 marks each, with 40
marks allotted for CIE (ContinuousInternalEvaluation) and 60 marks for SEE(SemesterEnd-
Examination).In CIE, for theory subjects, during a semester, there shall be two mid-term
examinations. Each Mid-Term examination consists of two parts i) Part – A for 10 marks,
ii) Part – B for 20 marks with a total durationof 2 hours as follows:
MidTermExaminationfor30marks:
a. Part-A:Objective/quiz/shortanswer typepaperfor 10marks.
b. Part-B:Descriptive paper for 20marks.
The average of two midterm examinations shall be taken as the final marks for mid
term examinations.
 The semester end examinations (SEE), will be conducted for
60marksconsistingoftwopartsviz.i) Part-Afor10marks,ii)Part-Bfor50marks.
a. Part-A is a compulsory question which consists of ten sub-questions from
all units carryingequalmarks.
b. Part-B consists of five questions (numbered from 2 to 6) carrying 10 marks
each. Each ofthese questions is from each unit and may contain sub-
questions.For each question therewill be an “either” “or” choice, which
means that there will be two questions from each unit and the student
should answer either of the two questions.
c. The duration of Semester End Examination is 3 hours.

Table 1: Outline for Continues Internal Evaluation (CIE-I and CIE-II) and SEE
Activities CIE-I CIE-II Average SEE Total
of CIE Marks
Continues Internal Evaluation (CIE) 20 20
Marks Marks
Objective / quiz / short answer 10 10
Questions Marks Marks Average
of CIE
Assignment 5 5 + SEE
Marks Marks
Viva-Voce/PPT/PosterPresentation/ 5 5
CaseStudy Marks Marks
Total Marks 40 40 40 60 100
Marks Marks Marks Marks Marks
22. Course content - Number of modules: Five:

Introduction to Information Retrieval Systems: Definition of Information

Retrieval System, Objectives of Information Retrieval Systems,
No. of
Functional Overview, Relationship to Database Management Systems,
MODULE 1 Lectures:
Digital Libraries and Data Warehouses Information Retrieval System
10
Capabilities: Search Capabilities, Browse Capabilities, Miscellaneous
Capabilities
Cataloguing and Indexing: History and Objectives of Indexing, Indexing
Process, Automatic Indexing, Information Extraction Data Structure: No. of
MODULE 2 Introduction to Data Structure, Stemming Algorithms, Inverted File Lectures:
Structure, N-Gram Data Structures, PAT Data Structure, Signature File 10
Structure, Hypertext and XML Data Structures, Hidden Markov Models
Automatic Indexing: Classes of Automatic Indexing, Statistical Indexing,
No. of
Natural Language, ConceptIndexing, Hypertext LinkagesDocument and
MODULE 3 Lectures:
Term Clustering: Introduction to Clustering, Thesaurus Generation, Item
10
Clustering, Hierarchy of Clusters
MODULE 4 User Search Techniques: Search Statements and Binding, Similarity No. of
 Measures and Ranking, Relevance Feedback, Selective Dissemination of
Information Search, Weighted Searches of Boolean Systems, Searching
Lectures:
the INTERNET and Hypertext Information Visualization: Introduction to
10
Information Visualization, Cognition and Perception, Information
Visualization Technologies
Text Search Algorithms: Introduction to Text Search Techniques, Software
No. of
Text Search Algorithms, Hardware Text Search Systems Multimedia
MODULE 5 Lectures:
Information Retrieval: Spoken Language Audio Retrieval, Non-Speech
8
Audio Retrieval, Graph Retrieval, Imagery Retrieval, Video Retrieval
TEXTBOOKS:
1. . Information Storage and Retrieval Systems – Theory and Implementation,
Second Edition,
Gerald J. Kowalski, Mark T. Maybury, Springer.

REFERENCE BOOKS:
.1. Frakes, W.B., Ricardo Baeza-Yates: Information Retrieval Data Structures and
Algorithms, Prentice Hall, 1992.
2. Information Storage & Retrieval By Robert Korfhage – John Wiley & Sons.
3. Modern Information Retrieval By Yates and Neto Pearson Education.

22.ELECTRONIC RESOURCES:
1. https://www.w3schools.com/dbms
2. https://www.geeksforgeeks.org/dbms/
3. https://www.javatpoint.com/dbms-tutorial

23. COURSE PLAN:

S.No Topics to be covered Cos Reference

.
0 Discussion on Outcome Based Education, CO, POs and - -
PSOs
1 Definition of Information Retrieval System CO1 T
2 Objectives of Information Retrieval Systems CO1 T
3 Functional Overview CO1 T
4 Relationship to Database Management Systems CO1 T
5 Relationship to Digital Libraries and Data Warehouses CO1 T
6 Search Capabilities of IR Systems CO1 T, R1
7 Browse Capabilities CO1 T, R1
8 Miscellaneous Capabilities CO1 T, R1
9 Summary and Q&A on Unit I CO1 T
10 History and Objectives of Indexing CO2 T, R2
11 Indexing Process CO2 T, R2
12 Automatic Indexing CO2 T, R2
13 Information Extraction CO2 T, R2
14 Introduction to Data Structures CO2 T
15 Stemming Algorithms CO2 T, R3
16 Inverted File Structure and N-Gram Data Structures CO2 T
17 PAT Data Structures and Signature File Structures CO2 T, R3
18 Hypertext, XML Data Structures, and Hidden Markov Models CO2 T, R3
19 Summary and Q&A on Unit II CO2 T
20 Classes of Automatic Indexing CO3 T, R2
21 Statistical Indexing CO3 T, R2
22 Natural Language and Concept Indexing CO3 T, R3
23 Hypertext Linkages CO3 T
24 Introduction to Clustering CO3 T
25 Thesaurus Generation CO3 T, R1
26 Item Clustering CO3 T, R1
27 Hierarchy of Clusters CO3 T
28 Summary and Q&A on Unit III CO3 T
29 Search Statements and Binding CO4 T
30 Similarity Measures and Ranking CO4 T
31 Relevance Feedback CO4 T, R1
32 Selective Dissemination of Information Search CO4 T, R1
33 Weighted Searches of Boolean Systems CO4 T, R3
34 Searching the Internet and Hypertext CO4 T
35 Introduction to Information Visualization CO4 T
36 Cognition and Perception in Visualization CO4 T
37 Information Visualization Technologies CO4 T
38 Summary and Q&A on Unit IV CO4 T
39 Introduction to Text Search Techniques CO5 T
40 Software Text Search Algorithms CO5 T, R2
41 Hardware Text Search Systems CO5 T
42 Spoken Language Audio Retrieval CO5 T, R3
43 Non-Speech Audio Retrieval CO5 T, R3
44 Graph Retrieval CO5 T, R3
45 Imagery Retrieval CO5 T, R3
46 Video Retrieval CO5 T, R3
47 Trends in Multimedia Information Retrieval CO5 T, R3
48 Summary and Q&A on Unit V CO5 T
24. PROGRAM OUTCOMES & PROGRAM SPECIFIC OUTCOMES:

PO 1: Engineering knowledge: Apply the knowledge of mathematics, science,

engineering fundamentals, and engg. specialization to the solution of complex
engineering problems.
PO 2: Problem analysis: Identify, formulate, research literature, and analyze engineering
problems to arrive at substantiated conclusions using first principles of mathematics,
natural, and engineering sciences.
PO 3: Design/development of solutions: Design solutions for complex engineering
problems and design system components, processes to meet the specifications with
consideration for the public health and safety, and the cultural, societal, and
environmental considerations.
PO 4:Conduct investigations of complex problems: Use research-based knowledge
including design of experiments, analysis and interpretation of data, and synthesis of
the information to provide valid conclusions.
PO 5:Modern tool usage: Create, select, and apply appropriate techniques, resources, and
modern engineering and IT tools including prediction and modeling to complex
engineering activities with an understanding of the limitations.
PO 6: The engineer and society: Apply reasoning informed by the contextual knowledge
to assess societal, health, safety, legal, and cultural issues and the consequent
responsibilities relevant to the professional engineering practice.
PO 7: Environment and sustainability: Understand the impact of the professional
engineering solutions in societal and environmental contexts, and demonstrate the
knowledge of, and need for sustainable development.
PO 8: Ethics: Apply ethical principles and commit to professional ethics and
responsibilities and norms of the engineering practice.
PO 9: Individual and team work: Function effectively as an individual, and as a member
or leader in teams, and in multidisciplinary settings.
PO 10: Communication: Communicate effectively with the engineering community and
with society at large. Be able to comprehend and write effective reports
documentation. Make effective presentations, and give and receive clear instructions.
PO 11: Project management and finance: Demonstrate knowledge and understanding of
engineering and management principles and apply these to one’s own work, as a
 member and leader in a team. Manage projects in multidisciplinary environments.
PO 12: Life-long learning: Recognize the need for, and have the preparation and ability to
engage in independent and life-long learning in the broadest context of technological
change.
Program Specific Outcomes
PSO 1: Applications of Computing: Ability to use knowledge in various domains to
provide solution to new ideas and innovations.
PSO 2:Programming Skills: Identify required data structures, design suitable algorithms,
develop and maintain software for real world problems.
PSO 3:Make use of computational and experimental tools for creating innovative career
paths, to be an entrepreneur and desire for higher studies.

25. HOW PROGRAM OUTCOMES ARE ASSESSED:

Program Outcomes Strength Proficiency

Assessed by
2
Engineering knowledge: Apply the knowledge of CIE/PPT/
mathematics, science, engineering fundamentals, and Objective /
PO1 engg. specialization to the solution of complex quiz /SEE/
engineering problems. Assignments/
Viva-Voce/
3
Problem analysis: Identify, formulate, research CIE/ PPT/
literature, and analyze engineering problems to arrive Objective /
PO2 at substantiated conclusions using first principles of quiz /SEE/
mathematics, natural, and engineering sciences. Assignments/
Viva-Voce/
1
Design/development of solutions: Design solutions CIE/ PPT/
for complex engineering problems and design system Objective /
PO3 components, processes to meet the specifications with quiz /SEE/
consideration for the public health and safety, and the Assignments/
cultural, societal, and environmental considerations. Viva-Voce/
1
PO 4 Conduct investigations of complex problems: Use CIE/ PPT/
research-based knowledge including design of Objective /
experiments, analysis and interpretation of data, and quiz /SEE/
synthesis of the information to provide valid Assignments/
conclusions. Viva-Voce/
2
PO 5 Modern tool usage: Create, select, and apply CIE/ PPT/
appropriate techniques, resources, and modern Objective /
engineering and IT tools including prediction and quiz /SEE/
modeling to complex engineering activities with an Assignments/
 understanding of the limitations. Viva-Voce/
1
PO 6 The engineer and society: Apply reasoning informed CIE/ PPT/
by the contextual knowledge to assess societal, health, Objective /
safety, legal, and cultural issues and the consequent quiz /SEE/
responsibilities relevant to the professional engineering Assignments/
practice. Viva-Voce/
2
PO 7 Environment and sustainability: Understand the CIE/ PPT/
impact of the professional engineering solutions in Objective /
societal and environmental contexts, and demonstrate quiz /SEE/
the knowledge of, and need for sustainable Assignments/
development. Viva-Voce/
2
PO 8 Ethics: Apply ethical principles and commit to CIE/ PPT/
professional ethics and responsibilities and norms of Objective /
the engineering practice. quiz /SEE/
Assignments/
Viva-Voce/
1
PO 9 Individual and team work: Function effectively as an CIE/ PPT/
individual, and as a member or leader in teams, and in Objective /
multidisciplinary settings. quiz /SEE/
Assignments/
Viva-Voce/
2
PO 10 Communication: Communicate effectively with the CIE/ PPT/
engineering community and with society at large. Be Objective /
able to comprehend and write effective reports quiz /SEE/
documentation. Make effective presentations, and give Assignments/
and receive clear instructions. Viva-Voce/
1
PO 11 Project management and finance: Demonstrate CIE/ PPT/
knowledge and understanding of engineering and Objective /
management principles and apply these to one’s own quiz /SEE/
work, as a member and leader in a team. Manage Assignments/
projects in multidisciplinary environments. Viva-Voce/
1
PO 12 Life-LongLearning:Recognizethe need forand Shorttermcour
havethepreparationandabilitytoengageinindependent ses
andlife-
longlearninginthebroadcastcontextoftechnologicalch
ange.
 6. HOW PROGRAM SPECIFIC OUTCOMES ARE ASSESSED:

Program Outcomes Strength Proficiency

Assessed by
Applications of Computing: Ability to use CIE/PPT/
knowledge in various domains to provide Objective /
PSO1 solution to new ideas and innovations. 3 quiz /SEE/
Assignments/
Viva-Voce/
Programming Skills: Identify required data CIE/PPT/
structures, design suitable algorithms, develop Objective /
PSO2 and maintain software for real world 3 quiz /SEE/
problems. Assignments/
Viva-Voce/
Make use of computational and experimental CIE/PPT/
tools for creating innovative career paths, to Objective /
PSO3 be an entrepreneur and desire for higher 2 quiz /SEE/
studies. Assignments/
Viva-Voce/
3 = High; 2 = Medium; 1 = Low
27. MAPPING OF EACH CO WITH PO(s),PSO(s):
COURSE PROGRAMOUTCOMES PSO’S
OUTCOMES 1 2 3 4 5 6 7 8 9 10 11 12 1 2 3
CO1 - - - - - - - -

CO2 - - - - - - - -

CO3 - - - - - - - - -

CO4 - - -
- - - - - -

CO5 - - - - - - - -

28. JUSTIFICATIONS FOR CO – PO / PSO MAPPING - DIRECT:

Course POs No. of key

Outcom Justification for mapping (Students will be able to)
/ competenc
es(COs) PSO ies
s
CO 1 PO 1 1. Scientific principles,
2. Mathematical principles. 2
 PO2 1. Problem identification,
2. Data collection, 3
3. Solution development.

PO5 Use of software tools..

1

PSO1 Ability to use knowledge in computing to locate relevant 1

information.

PSO2 Identifies and applies appropriate algorithms for information 1

retrieval.

CO 2 PO1 1. Mathematical principles 1

PO3 1. Use creativity for innovative solutions, 2

2. Manage design process.

PO5 Use of diagnostic/simulation tools. 1

PSO1 Applies knowledge of clustering techniques to provide innovative 1

solutions.

PSO2 Designs data structures and algorithms for document clustering. 1

CO3 PO3 1. Manage design process, 2
2. Evaluate outcomes.
PO4 1. Experimental design, 2
2. Data interpretation.
PO5 Use of simulation packages. 1
PSO1 Develops retrieval systems for web search leveraging domain 1
knowledge.

PSO2 Implements efficient algorithms for web-based retrieval tasks. 1

PSO3 Employs computational tools for building scalable retrieval systems. 1

CO 4 PO3 1. Define problems, 2

2. Optimize cost drivers.
PO7 1. Environmental considerations. 1
 PSO1 Designs comprehensive systems for web search using computing 1
knowledge.

PSO2 Develops advanced algorithms addressing real-world search tasks. 1

PSO3 Applies innovative methods and experimental tools for system 1
design.

PO1 1. Support engineering disciplines. 1

PO2 1. Data collection, 2
2. Result interpretation.
PO12 1. Keeping current with trends, 2
2. Personal development.
CO 5 PSO1 Applies knowledge to locate relevant data in large collections. 1
PSO2 Identifies suitable algorithms and structures for effective data 1
retrieval.

29. TOTAL COUNT OF KEY COMPETENCIES FOR CO – (PO, PSO) MAPPING:

Course PROGRAM OUTCOMES PSOs
Outcomes PO PO PO PO PO PO PO P PO PO PO PO PSO PSO PSO 3
9
1 2 3 4 5 6 7 O 10 11 12 1 2
8

CO1 2 3 - - 1 - - - - - - 1 1- -

CO2 1 - 2 - 1 - - - - - - 1 1- 1-

CO3 - - 2 2 1 - - - - - - 1 1- 1-
- -
CO4 2 - 1 - 1- - - - - 1 1 1-

CO5 1 2 - - - - - - - 2 - -

30. PERCENTAGE OF KEY COMPETENCIES FOR CO – (PO/ PSO):

Course PROGRAM OUTCOMES PSOs

Outcomes
PO PO PO PO PO PO PO PO PO PO PO PO PS PSO
1 2 3 4 5 6 7 8 9 10 11 12 O 1 2
CO1 66.6 30.0 - 100.0 - - - - - - 33 33
- - - - - - -
CO2 33 - 20 100 33 33
- - 20 18 100
- - - - - - - 33 33
CO3
- - 20
- - - 33
- - - - - 33 33
CO4
33 20
- - - - - - - - - 25 33 33
CO5
31. COURSE ARTICULATION MATRIX (PO – PSO MAPPING):
CO’S and PO’S, CO’S and PSO’S on the scale of 0 to 3, 0 being no correlation, 1 being the
low correlation, 2 being medium correlation and 3 being high correlation.
0 - 0≤ C≤ 5% – No correlation, 2 - 40 % <C < 60% –Moderate
1-5 <C≤ 40% – Low/ Slight 3 - 60% ≤ C < 100% – Substantial /High

Course PROGRAM OUTCOMES PSOs

Outcomes
PO PO PO PO PO PO PO PO PO PO PO PO PS PSO
1 2 3 4 5 6 7 8 9 10 11 12 O 1 2
- - - - - - - - -
CO1 3 1 3 1 1
- - - - - - -
CO2 1 - 1 - 3 1 1
- - - - - - - - -
CO3 1 1 3 1 1
- - - - - - - - - -
CO4 1 1 1 1
- - - - - - - - -
CO5 1 1 1 1 1
Total 5 2 3 - 9 - 1 - - - - 1 5 5
Average 1.6 1 1 - 3 - 1 - - - - 1 1 1

32. ASSESSMENT METHODOLOGY DIRECT:

CIE Exams SEE Seminars
Objective / quiz Viva-Voce/ MOOCS
-
PPT
Assignments � Project
-

33. ASSESSMENT METHODOLOGY INDIRECT:

Course End Survey (CES)

34. RELEVANCE TO SUSTAINABILITY GOALS:

IRS course often emphasizes techniques for organizing, storing, and retrieving information,
alongside applications in diverse domains.

X 1
X 2

3
X

Access to Knowledge: By making information easily

accessible, IRS supports lifelong learning opportunities and
quality education for all.
4 Open Educational Resources: IRS enables platforms like
digital libraries and repositories, improving accessibility to
educational content globally

X 5

X 6

X 7

Job Market Insights: Advanced retrieval systems can assist job

seekers in finding opportunities and help industries match talent
efficiently.
8 Digital Economy: By empowering the tech industry, IRS fosters
innovation and economic growth.

Data-Driven Decision Making: IRS provides a foundation for

innovative industries, enabling smarter decisions through quick
access to critical information.
9 Support for Research: Efficient retrieval systems accelerate
research in fields like engineering, medicine, and environmental
sciences.

X 10
 Smart Cities: IRS facilitates better urban planning by providing
accessible data for traffic management, waste management, and
citizen services.
11 Community Awareness: Platforms built using IRS spread
knowledge on sustainability practices among communities.

Data Reuse: Encourages the efficient use of existing data through

organized repositories and retrieval systems, reducing redundant
data creation.
12 Awareness Campaigns: Provides mechanisms for educating users
about responsible practices through curated, relevant content.

Environmental Data Analysis: IRS can retrieve climate-related

data for policymakers, enabling informed decisions on mitigation
strategies.
13 Research Support: Aids environmental researchers in finding
resources to combat climate change.

X 14

X 15

X 16

Collaboration Platforms: IRS enables shared knowledge systems

that foster international collaboration on sustainability initiatives.
Data Exchange: Promotes transparent sharing of global resources
17 and research data

Signature of Course Coordinator HOD

Name & Designation :
V.RAJASHREE
ASST.PROFESSOR