Outcomes Based Education Curricula (Academic Year 2015 – 2016)

M. S. RAMAIAH INSTITUTE OF TECHNOLOGY

BANGALORE-54

(Autonomous Institute, Affiliated to VTU)

Computer Science and Engineering

Outcomes Based Education Curricula

(Academic Year 2015 – 2016)

VII & VIII Semester

History of the Institute

M. S. Ramaiah Institute of Technology was started in 1962 by the late Dr. M.S. Ramaiah, our

Founder Chairman who was a renowned visionary, philanthropist, and a pioneer in creating several landmark infrastructure projects in India. Noticing the shortage of talented engineering professionals required to build a modern India, Dr. M.S. Ramaiah envisioned MSRIT as an institute of excellence imparting quality and affordable education. Part of Gokula Education

Foundation, MSRIT has grown over the years with significant contributions from various professionals in different capacities, ably led by Dr. M.S. Ramaiah himself, whose personal commitment has seen the institution through its formative years. Today, MSRIT stands tall as one of India’s finest names in Engineering Education and has produced around 35,000 engineering professionals who occupy responsible positions across the globe.

History of the Department of Computer Science

Year of Establishment

Names of the Programmes offered

1984

1.

UG: B.E. in Computer science and Engineering

2.

PG: M.Tech. in Computer Science and Engineering

3.

PG: M.Tech. in Computer Networks and Engineering

4.

Ph.D

5.

M.Sc(Engg.) by research

Sl. No.

17.

18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

31.

32.

33.

34.

1.

2.

3.

4.

5.

6.

7.

8.

9.

10.

11.

12.

13.

14.

15.

16.

35.

36.

37.

38.

Faculty

Name

Dr. K G Srinivasa

Dr. R. Srinivasan

Dr .S Ramani

Nagabhushan A.M

Dr. Anita Kanavalli

Dr. Seema S

Dr. Annapurna P. Patil

Jagadish S Kallimani

Jayalakshmi D S

Dr. Monica R Mundada

Sanjeetha R

A Parkavi

Veena G S

J Geetha

Dr. T N R Kumar

Mamatha Jadav V

Chethan C T

Sini Anna Alex

Vandana Sardar

Meera Devi

Mallegowda M

Divakar Harekal

Chandrika Prasad

S Rajarajeswari

Sowmyarani C N

Qualification

M.E, Ph.D

D.Sc.

Ph.D

M.Tech

M.E., Ph.D

M.S., Ph.D

M. Tech, Ph.D

M.Tech, (Ph.D)

M.Sc(Engg), (Ph.D)

M.Tech, Ph.D

M.Tech

M.E. (Ph.D)

M.Tech (Ph.D)

M.Tech, (Ph.D)

M. Tech Ph.D

M.Tech

B.E.

M.E, (Ph.D)

M.E.

M.Tech

M.Tech

M.E.

M.Tech

M.E, (Ph.D)

M.E. (Ph.D)

Pramod S Sunagar

Sowmya B J

Pradeep Kumar D

Ganeshayya I Shidaganti

Chetan

Darshana A Naik

Srinidhi H

Aparna R

Hanumantha Raju R

M.Tech

M.Tech

M.Tech

M.Tech

M.Tech

M.Tech

M.Tech

B.E, M.Tech

B.E, M.Tech

Visiting Faculty Members from Industry

Dr. Ramamurthy Badrinath

N. Pramod

Jayasimha Rao

Sriram Kashyap

Ph.D

B.E.

M.S. in Machine Learning and

Data Mining from Aalto

University School of Science

MTech from IIT Madras

AICTE-INAE distinguished

Visiting Professor

Application Engineering at

Thoughtworks Pvt. Ltd.

Entrepreneur

Intel, Bangalore

Designation

HOD, Professor

Emeritus Professor

Emeritus Professor

Emeritus Professor

Professor

Associate Professor

Associate Professor

Associate Professor

Associate Professor

Associate Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Assistant Professor

Vision and Mission of the Institute

Vision

To evolve into an autonomous institution of International standards for imparting quality

Technical Education

Mission

MSRIT shall deliver global quality technical education by nurturing a conducive learning environment for a better tomorrow through continuous improvement and customization.

Quality Policy

“We at M. S. Ramaiah Institute of Technology, Bangalore strive to deliver comprehensive, continually enhanced, global quality technical and management education through an established

Quality Management system complemented by the synergistic interaction of the stake holders concerned”.

Vision and Mission of the Department

Vision

To build a strong learning and research environment in the field of Computer Science and

Engineering that responds to the challenges of 21 st

century.

Mission

To produce computer science graduates who, trained in design and implementation of computational systems through competitive curriculum and research in collaboration with industry and other organizations.

To educate students in technology competencies by providing professionally committed faculty and staff.

To inculcate strong ethical values, leadership abilities and research capabilities in the minds of students so as to work towards the progress of the society.

Process for Defining the Vision and the Mission of the Department

Programme Educational Objectives (PEOs)

A B.E. (Computer Science & Engineering) graduate of M. S. Ramaiah Institute of Technology should, within three to five years of graduation

1.

Pursue a successful career in the field of Computer Science & Engineering or a related field utilizing his/her education and contribute to the profession as an excellent employee, or as an entrepreneur

2.

Be aware of the developments in the field of Computer Science & Engineering, continuously enhance their knowledge informally or by pursuing graduate studies

3.

Be able to work effectively in multidisciplinary environments and be responsible members/leaders of their communities

PEO Derivation Process

Programme Outcomes (POs)

The outcomes of the Bachelor of Engineering in Computer Science & Engineering Programme are as follows:

A B.E. (Computer Science & Engineering) graduate must demonstrate

1.

An ability to apply knowledge of mathematics, science, and engineering as it applies to

Computer Science & Engineering

2.

An ability to identify, formulate, study, and analyze and solve complex computing problems

3.

An ability to design a computer-based system, component, software or process to meet the desired needs

4.

An ability to design and conduct experiments, evaluate results and provide valid conclusions

5.

An ability to use modern computing techniques, technologies and tools necessary for computing engineering practice.

6.

An ability to understand and assess the societal, legal and security issues related to the practice of computer science and engineering.

7.

An ability to understand the impact of computing solutions in an economic, environmental and societal context.

8.

An understanding of professional and ethical responsibilities in professional engineering practice.

9.

An ability to function effectively individually and in team, and in multi-disciplinary environment.

10.

An ability to communicate effectively.

11.

An understanding of the engineering and management principles required for project and finance management.

12.

Recognition of the need for, and an ability to engage in life-long learning.

PO Derivation Process

Sl.

No.

1

2

3

Programme

Educational

Objectives

Excel in career

Life-long learning

Work in diverse teams and show

Leadership

Mapping of PEOs and POs

Programme Outcomes

PO1 PO2 PO3 PO4 PO5 PO6 PO7 PO8 PO9 PO10 PO11 PO12 x x x x x x x x x x x x x x x x x x x x x x x x x x x x

Sl. No.

Curriculum Breakdown Distribution

Courses Weightage

1 Basic Science Core Courses 13%

2 Basic Engineering Science Core Courses 13%

3

4

5

6

Humanities and Social Science Core Courses

Professional Courses and Electives

Major Project

Mandatory Learning Courses

3%

62%

9%

0%

1.

Head of the Department concerned:

2.

At least five faculty members at different levels covering different specializations constituting nominated by the Academic

Council

3.

Special invitees

4.

Two experts in the subject from outside the college

5.

One expert from outside the college, nominated by the Vice

Chancellor

6.

One representative from industry/corporate sector allied area relating to placement nominated by the Academic

Council

7.

One postgraduate meritorious alumnus to be nominated by the

Principal

Board of Studies for the Term 2015-2016

Dr. K G Srinivasa

Dr. Anita Kanavalli

Prof. Jagadish S Kallimani

Prof. Jayalakshmi D S

Prof. H V Divakar

Prof. Sanjeetha R

Prof. Parkavi

Prof. Chandrika Prasaad

Dr. R. Srinivasan

Dr. S. Ramani

Prof. Nagabhushan A M

Dr. Kavi Mahesh, Professor, PESIT

Dr. G Varaprasad Associate

Professor, BMSCE

Dr. N.K. Srinath, Professor, RVCE

Mr. Rajesh Vijayarajan, Hewlett-

Packard

Sriram Kashyap, Intel Corporation

Member

Member

Member

Chairperson

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Department Advisory Board for the term 2015-2016

1.

Head of the Department concerned

2.

Experts from other organizations for

Department Advisory Board

Dr. K G Srinivasa

Dr. Satish Vadhiyar, SERC, IISC Bangalore

Dr. Srinivasaraghavan, IIIT Bangalore

Dr. K Sangeeta Iyer

Industry Advisory Board for the Term 2015-2016

1.

Head of the Department concerned Dr. K G Srinivasa

2.

Experts from industry constituting the Industry Advisory Board

Dr. Badrinath Ramamurthy, HP Labs, India

Dr. N.C. Narendra, CTS

Dr. Yogesh Simhan, SERC, IISC

Mr. Sreekanth Iyer, IBM

Mr. Nishant Kulkarni, IBM

Mr. Muthuraman Ranganath, SAP Technologies

Mr. K Murali, Amazon

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Member

Scheme of Studies for Fourth Year B.E. (CSE) for the batch 2011-2015

VII Semester

Code Subject

Total Credits: 25

L T P Credit

CS721 Advanced Computer Architecture 3 0 0 3

3 CS723 Project Management & Engineering

Economics

3 0 0

CS724 Cryptography and Network Security 3 1 0

CS725 Computer Graphics & Visualization 3 0 0

Elective Group III

Elective Group IV

Open Elective

CSL712 Computer Graphics & Visualization

Laboratory

CSL716 High Performance Computing

Laboratory

VIII Semester

* * *

* * *

* * *

0 0 1

0 0 1

4

3

3

4

3

1

1

Code

CS812

Subject

Elective Group IV

Project

Total Credits: 24

L T P Credit

* * * 4

- - 18 18

CS813 Seminar (for Regular Students)

CS8T1 Technical Writing & Content

Development (for Lateral Entry students)

List of electives:

Elective Group III (3 Credits)

1 CSPE711 Pattern Recognition (3:0:0)

-

-

-

-

2

1

2

1

Elective Group IV (4 Credits)

1 CSPE712 Distributed Systems (4:0:0)

2 CSPE715 Data Mining (4:0:0)

2 CSPE717 Service Oriented Architecture

(3:0:0)

3 CSPE720 Business Intelligence &

Applications (3:0:0)

4 CSPE730 Parallel Programming using

CUDA (3:0:0)

5 CSPE734 Project Based Learning :

Internet of Things/ Data

Analytics (0:1:2)

3

4

5

6

7

8

9

CSPE718 Information Storage and

Management (4:0:0)

CSPE719 Wireless Networks and

Mobile Computing (4:0:0)

CSPE721 Software Testing (3:0:1)

CSPE725 Software Architecture (4:0:0)

CSPE727 Machine Learning

Techniques (3:0:1)

CSPE731 Cloud Computing ( 4:0:0)

CSPE733 Big Data and Data Science

(3:0:1)

11

Course Title: Advanced Computer Architecture Course Code: CS721

Credits (L:T:P) : 3:0:0

Type of course: Lecture

Core/ Elective: Core

Total Contact Hours: 42

Prerequisites: Computer Organization, Introduction to Microprocessors

Course Contents:

Unit 1

Fundamentals of Quantitative Design and Analysis: Classes of Computers, Defining Computer Architecture, Trends in

Technology, Trends in Cost, Dependability, Measuring Reporting and Summarizing Performance, Quantitative Principles of Computer Design, Introduction to Pipelining and Pipeline Hazards.

Unit 2

Instruction–Level Parallelism: Concepts and Challenges, Basic Compiler Techniques for Exposing ILP, Reducing

Branch cost with Advanced branch Prediction, Overcoming Data Hazards with Dynamic Scheduling examples and the

Algorithm, Exploiting ILP Using Multiple Issue and Static Scheduling and Dynamic Scheduling, Case study-The Intel

Core i7.

Unit 3

Thread–Level Parallelism: Introduction, Centralized Shared-Memory Architectures, Performance of symmetric shared memory Multiprocessors, Distributed Shared Memory and Directory-Based Coherence, Synchronization: The Basics,

Models of Memory Consistency.

Unit 4

Memory Hierarchy Design: Introduction, Ten Advanced Optimizations of Cache Performance, Memory Technology and Optimizations, Protection: Virtual Memory and Virtual Machines, Memory Hierarchies in the ARM Cortex-A8.

Unit 5

Data Level Parallelism in Vector, SIMD Architectures and Warehouse-Scale Computers: Introduction, Vector

Architecture, SIMD Instruction set Extensions for Multimedia, Introduction to Warehouse-scale Computers,

Programming Models and Workloads for Warehouse-scale Computers, Computer Architecture for Warehouse-scale

Computers. Case Study : Google Warehouse Scale Computer.

Text Book:

1.

John L. Hennessey and David A. Patterson: Computer Architecture, A Quantitative Approach, 5th Edition,

Elsevier, 2012.

Reference Books:

1.

Kai Hwang, Naresh Jotwani: Advanced Computer Architecture - Parallelism, Scalability, Programmability , 2 th

Edition, Tata McGraw Hill, 2011.

2.

David E. Culler, Jaswinder Pal Singh: Parallel Computer Architecture, A Hardware / Software Approach,

Morgan Kauffman, 1 st

edition, 2010.

Course Delivery: The course will be delivered through lectures, presentations, classroom discussions, and practical implementations. Questions for CIE and SEE are designed in accordance with the Bloom’s taxonomy.

12

Course Assessment and Evaluation:

What

Internal

Assessment

Tests

Quiz

To Whom

When/ Where

(Frequency in the course)

Thrice(Average of the best two will be computed)

Twice

Max

Marks

30

20

Evidence

Collected

Blue Books

Contribution to

Course Outcomes

1,2,3,4 & 5

Test Data

Sheets

1,2,3,4 & 5

Standard

Examination

Students

End of Course

(Answering

5 of 10 questions)

100

Answer scripts

1,2,3,4 & 5

End of Course

Survey

End of the course - Questionnaire

1, 2,3,4 & 5

Effectiveness of

Delivery of instructions &

Assessment Methods

Course Outcomes:

At the end of the course students should be able to:

1.

Interpret the growth in processor performance, development of IC for higher reliability and availability

2.

Explain the concept of parallelism and challenges associated with instruction level parallelism.

3.

Organize the complexity of different types of memory architectures.

4.

Find the techniques to optimize the cache, and virtual machines.

5.

Examine the different architectures under data level parallelism and warehouse scale computers.

Mapping Course Outcomes with program Outcomes:

Course Outcomes Program Outcomes

1 2 3 4 5 6 7 8 9 10 11 12

Interpret the growth in processor performance, development of IC for higher reliability and availability

Explain the concept of parallelism and challenges associated with instruction level parallelism.

Organize the complexity of different types of memory architectures

Find the techniques to optimize the cache, and virtual machines

Examinethe different architectures under data level parallelism and warehouse scale computers x x x x x x x x x x x x x x x x x x x x x x x x x x

13

Course Title: Project Management & Engineering Economics


Type of Course: Lecture

Course Code: CS723


Total Contact Hours: 42

Prerequisites: NIL

Unit 1

Introduction to Engineering Economics: Engineering Decision Makers, Engineering and Economics, Economics: A

Capsule View, Problem Solving and Decision Making.

Time Value of Money: Interest and the Time Value of Money, Reasons for Interest, Simple Interest, Compound Interest,

Time Value Equivalence, Compound Interest Factors, Cash Flow Diagrams, Calculation of Time Value Equivalences.

Present Worth Comparisons : Conditions for Present Worth Comparisons, Basic Present Worth Comparison Patterns,

Comparison of Assets that have unequal lives, Comparison of Assets assumed to have infinite lives.

Unit 2

Present Worth Comparisons : Comparison of deferred investments, Future worth comparisons, Valuation, Payback

Comparison Method. Equivalent Annual Worth Comparisons: Utilization of Equivalent Annual Worth Comparisons,

Consideration of Asset Life, Use of a sinking fund, Equivalent uniform payments when interest rates vary, Annuity contract for a guaranteed income.

Unit 3

Rate of Return Calculations : Rate of Return, Minimum Acceptable rate of return, internal rate of return, Consistency of

IRR with other economic comparison methods, IRR Misconceptions, Final comments on theory and practice behind interest rates.

Introduction to Project Management : What is project and project management? Role of project manager, A system view of project management, project phases and project cycle, Context of IT projects.

Strategic Planning and Project Selection: Preliminary scope statements, project management plans, project execution, monitoring and control of project work,

Unit 4

Project scope management : what is project scope management? Scope planning and scope management plan, scope definition and project scope statement, creating work breakdown structure, scope verification.

Project time management : importance of project schedules, activity definition, sequencing, resource estimation, duration estimation, schedule development, schedule control

Project cost management : Cost estimation, budgeting, control.

Unit 5

Project quality management: Importance of quality management, what is quality management, planning, assurance, control, tools and techniques for quality control.

Project communication management : Importance, communication planning, information distribution.

Project risk management: what is risk management, risk management planning, common source of risk in IT, risk identification, risk monitoring and control .

Text Books:

1.

James L Riggs, David D Bedworth, Sabah U Randhawa: Engineering Economics, Fourth Edition, TMH, 1996.

2.

Kathy Schwalbe: Project Management in IT, India edition, Cengage Learning, 2007.


1.

R. Panneerselvam: Engineering Economics, PHI Learning Pvt. Ltd., 2001.

2.

Bob Hughes, Mike Cotterell: Software Project Management, Tata McGraw Hill, 2006.

3.

Pankaj Jalote: Software Project Management in Practice, Pearson, 2006.

Course Delivery: The course will be delivered through lectures, class room interaction, group discussion and exercises.

14

Course Assessment and Evaluation Scheme:

CIE

SEE

What

Internal

Assessment

Tests

Quiz/

Case study

Standard

Examination

To

Whom

Students

When/ Where



Once

End of Course

(Answering

5 of 10 questions)

Max

Marks

30

20

100

Evidence

Collected

Blue Books

Quiz Answers/

Reports

Answer scripts

Contribution to

Course Outcomes

1,2,3,4 &5

1-5

1,2,3,4 &5

End of Course

Survey


1, 2, 3, 4 & 5

Effectiveness of

Delivery of instructions

& Assessment Methods



1.

Describe the basic concepts of engineering economics and Time Value Equivalence of money.

2.

Calculate present worth, future worth and equivalent annual worth of investments and compare investment alternatives.

3.

Identify the various rates of returns

4.

Estimate the time,scope and cost of a software project.

5.

Identify various quality issues, communication issues and risks in a software project.

Mapping Course Outcomes with Programme Outcomes:


Course Outcomes

1 2 3 4 5 6 7 8 9 10 11 12

Describe the basic concepts of engineering economics and time value equivalence of money

Calculate present worth, future worth and equivalent annual worth of investments and compare investment alternatives.

Identify the various rates of returns.

Estimate the time and cost of a software project.

Identify various quality issues, communication issues and risks in a software project. x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

15

Course Title: Cryptography & Network Security




Type of Course: Lecture, Tutorial Total Contact Hours: 70

Prerequisites: Knowledge of Computer Networks.


Unit 1

Introduction : Security Goals, Cryptographic Attacks, Services and Mechanism, Techniques. Mathematics of

Cryptography : Integer Arithmetic, Modular Arithmetic, Matrices, Linear Congruence.

Unit 2

Traditional Symmetric-Key Ciphers : Introduction, Substitution Ciphers, Transpositionl Ciphers, Stream and Block

Ciphers. Data Encryption Standard (DES): Introduction, DES Structure, DES Analysis, Security of DES, Multiple DES,

Examples of Block Ciphers influenced by DES.

Advanced Encryption Standard : Introduction, Transformations, Key Expansion, The AES Ciphers, Examples, Analysis of AES.

Unit 3

Encipherment using Modern Symmetric-Key Ciphers: Use of Modern Block Ciphers, Use of Stream Ciphers, Other

Issues.

Asymmetric Key Cryptography : Introduction, RSA Cryptosystem, Rabin Cryptosystem, Elgamal Cryptosystem,

Elliptic Curve Cryptosystems

Unit 4

Message authentication : Authentication Requirements, Authentication Functions, Message Authentication Codes,

Security of MACs, MACs based on Hash Functions: MAC.

Digital signatures : Digital Signatures, Digital Signature Standard.

Key management and distribution : Symmetric Key distribution using symmetric encryption, Symmetric Key distribution using Asymmetric encryption, Distribution of public keys, X.509 certificates, Kerberos.

Unit 5

Transport level security : Web Security considerations, Secure Sockets Layer and Transport Layer Security. Transport

Layer Security, HTTPS, Secure Shell.

Internet security : Electronic Mail Security: Pretty Good Privacy, S/MIME. IP Security: Overview, IP Security Policy.

System security : Intruders: Intruders, Intrusion detection.

Malicious Software : Types of Malicious Software, Viruses. Firewalls: The need for Firewalls, Firewall Characteristics,

Types of Firewalls.

Text Books:

1.

Behrouz A. Forouzan, Debdeep Mukhopadhyay: Cryptography and Network Security, 2nd Edition, Special

Indian Edition, Tata McGraw-Hill, 2011.

2.

William Stallings, Cryptography and Network Security, Fifth Edition, Prentice Hall of India, 2005


1.

Josef Pieprzyk, Thomas Hardjono, Jennifer Serberry Fundamentals of Computer Security, Springer.

Course Delivery: The course will be delivered through lectures, class room interaction, group discussion and exercises and self-study cases.

16


What

CIE

CIE

SEE

Internal

Assessment

Test

Cryptography and Network

Security

Tutorials

Standard

Examination

To

Whom

Student

When/ Where


Thrice (Average of the best two will be computed)

Max

Marks

30

Daily Evaluation based on the

Performance(20M)

End of Course

(Answering

5 of 10 questions)

20

100

Evidence

Collected

Blue Books

Report

Answer scripts

Contribution to

Course

Outcomes

1, 2, 3, 4 & 5

1,2,3,4 & 5

1,2,3,4 & 5

End of Course

Survey


1, 2, 3, 4 & 5

Effectiveness of


Assessment

Methods


At the end of the course the students should be able to:

1.

Interpret the security goals and the threats to security

2.

Identify and formulate the type of encryption method DES or AES depending on the need and security threat perception

3.

Compare and Contrast the need of Symmetric-key and Asymmetric-key Ciphers.

4.

Summarize the fundamentals of Key Management and Identity need for Digital Signatures and its utility

5.

Identify security goals achieved at application layer, and appraise the need for firewalls.


Course Outcomes


1 2 3 4 5 6 7 8 9 10 11 12

Interpret the security goals and the threats to security x x x x x x x

Identify and formulate the type of encryption method

DES or AES depending on the need and security threat perception

Compare and Contrast the need of Symmetric-key and

Asymmetric-key Ciphers

Summarize the fundamentals of Key Management and

Identity need for Digital Signatures and its utility

Identify security goals achieved at application layer, and

Appraise the need for firewalls. x x x x x x x x x x x x x x x x x x x x x x x x x x x

17

Course Title: Computer Graphics and Visualization

Credits (L:T:P) : 3:0:0


Course Code: CS725

Core/Elective: Core


Prerequisites: Nil


Unit 1

Introduction : Applications of computer graphics, A graphics system, Images: Physical and synthetic, Imaging Systems,

The synthetic camera model, The programmer’s interface, Graphics architectures, Programmable Pipelines, Performance

Characteristics, Graphics Programming: The OpenGL: The OpenGL API, Primitives and attributes, Color, Viewing,

Control functions

Unit 2

Input and Interaction: Interaction, Input devices, Clients and Servers, Display Lists, Display Lists and Modeling,

Programming Event Driven Input, Menus, Picking, A simple CAD program, Building Interactive Models, Animating

Interactive Programs, Design of Interactive Programs, Logic Operations.

Geometric Objects and Transformations: Scalars, Points, and Vectors, Three-dimensional Primitives, Coordinate

Systems and Frames, Modeling a Colored Cube, Affine Transformations, Rotation, Translation and Scaling.

Unit 3

Transformations: Geometric Objects and Transformations, Transformation in Homogeneous Coordinates, Concatenation of Transformations, OpenGL Transformation Matrices, Interfaces to three-dimensional applications, Quaternion’s.

Implementation: Basic Implementation Strategies, Four major tasks, Clipping, Line-segment clipping, Polygon clipping,

Clipping of other primitives. Clipping in three dimensions, Rasterization, Bresenham’s algorithm, Polygon Rasterization,

Hidden-surface removal, Antialiasing, Display considerations.

Unit 4

Viewing : Classical and computer viewing, Viewing with a Computer, Positioning of the camera, Simple projections,

Projections in OpenGL, Hidden-surface removal, Interactive Mesh Displays, Parallel-projection matrices, Perspectiveprojection matrices, Projections and Shadows.

Unit 5

Rendering and Shading: Overview of Programmable graphics pipeline, Vertex shader and its applications, Pixel shaders and its applications, Texture mapping.

Text Book:

1.

Edward Angel: Interactive Computer Graphics - A Top-Down Approach with OpenGL, 5 th

Edition, Pearson

Education, 2011.


1.

Donald Hearn and Pauline Baker: Computer Graphics with OpenGL, 3 rd

Edition, Pearson Education, 2011.

2.

F.S. Hill Jr.: Computer Graphics Using OpenGL, 3 rd


3.

James D Foley, Andries Van Dam, Steven K Feiner, John F Hughes: Computer Graphics, 2 nd

Edition, Pearson

Education, 2011

Course Delivery:

The course will be delivered through lectures, OpenGL programming exercises and group project in laboratory. . Topics for lab exercises are input interaction with mouse and keyboard, picking, display lists, hierarchical menus, scan conversion algorithms for lines and polygons, clipping, hidden surface removal, lighting and shading. A group project to create an interactive graphics application using OpenGL must be done.

18


What

Internal Assessment

Tests

To Whom

When/ Where



Max

Marks

30

Evidence

Collected

Blue Books

Contribution to

Course Outcomes

1,2,3,4,5,6,7

Assignment/Quiz/Online

Course

Once 20 Quiz Papers 1,2,3,4,5,6,7

Standard Examination

Students

End of Course

(Answering

5 of 10 questions)

100

Answer scripts

1,2,3,4,5,6,7

End of Course

Survey

End of the course

- Questionnaire

1, 2 ,3,4,5,6,7

Effectiveness of


Assessment

Methods


1.

Explain the image formation process and the pipeline architecture of computer graphics

2.

Describe the software and hardware components of a computer graphics system and basics of OpenGL API’s

3.

Derive the geometrical transformations used in interactive computer graphics in different coordinate systems

4.

Discuss the different algorithms for clipping and rasterization of lines and polygons, and for hidden surface removal.

5.

Derive matrix formulations for different types of viewing and projections.

6.

Explain different lighting and shading models.

7.

Write 3D computer graphics applications in OpenGL using knowledge of display systems, image synthesis, and interactive control.


Course Outcomes


1 2 3 4 5 6 7 8 9 10 11 12 x

Explain the image formation process and the pipeline architecture of computer graphics.

Describe the software and hardware components of a computer graphics system and basics of OpenGL

API’s.

Derive the geometrical transformations used in interactive computer graphics in different coordinate systems.

Discuss the different algorithms for clipping and rasterization of lines and polygons, and for hidden surface removal.

Derive matrix formulations for different types of viewing and projections.

Explain different lighting and shading models.

Write 3D computer graphics applications in OpenGL using knowledge of display systems, image synthesis, and interactive control. x x x x x x x x x x x x x x x x

19

Course Title: Graphics and Visualization Laboratory

Credits (L:T:P) : 0:0:1

Type of Course: Practical

Course Code: CSL712

Core/Elective: Core


Prerequisites: Nil


Part A: Using C++ and OpenGL API’s, students are required to write programs on the following topics:

1.

Input Interactions

2.

Menu driven programs, programs showing the use of display lists and picking.

3.

Programs on animation effect.

4.

Programs on scan converting line, circle and polygon.

5.

Programs on clipping lines.

6.

Modeling 3d objects.

7.

Applying transformation and viewing to 3D graphics.

8.

Applying rendering and Shading to objects.

Part B:

1.

Students in groups are required to develop a graphics application demonstrating the concept of transformation, viewing, rendering and shading.

Text Book:

1.

Edward Angel: Interactive Computer Graphics - A Top-Down Approach with OpenGL, 5 th

Edition, Pearson

Education, 2011.


1.

Donald Hearn and Pauline Baker: Computer Graphics with OpenGL, 3 rd


2.

F.S. Hill Jr.: Computer Graphics Using OpenGL, 3 rd


3.

James D Foley, Andries Van Dam, Steven K Feiner, John F Hughes: Computer Graphics, 2 nd

Edition, Pearson

Education, 2011


The course will be delivered by conducting OpenGL programming exercises and group project in laboratory. Topics for lab exercises are input interaction with mouse and keyboard, picking, display lists, hierarchical menus, scan conversion algorithms for lines and polygons, clipping, hidden surface removal, lighting and shading. Three to four students must work in group to develop an interactive graphics application using OpenGL.

20


What

Internal

Assessment

Tests

Project

Demonstration

To Whom

When/ Where


Once

Once

Max

Marks

30

20

Evidence

Collected

Contribution to

Course Outcomes

Lab Test and

Data sheets

Presentation and code demo

1,2 & 3

1,2,3 & 4

Standard

Examination

Students

End of Course

(Executing a given program)

100

Answer scripts

1,2 & 3

End of Course

Survey

End of the course -


1.

Use OpenGL to model , transfrom and view 3D objects

2.

Implement basic scan converting and clipping algorithms using OpenGL.

3.

Use different lighting and shading techniques to render 3D graphics.

4.

Develop a graphics application for rendering and shading 3D interactive graphics

Questionnaire

1, 2 ,3 & 4

Effectiveness of


Assessment Methods

Mapping Course Outcomes with Program Outcomes:

Course Outcomes

Program Outcomes

1 2 3 4 5 6 7 8 9 10 11

Use OpenGL to model , transform and view 3D objects.

Implement basic scan converting and clipping algorithms using OpenGL x x x x x x x x

Use different lighting and shading techniques to render 3D graphics. x x x x

Develop a graphics application for rendering and shading 3D interactive graphics. x x x x x x

21 x

12 x x x

Course Title: High performance laboratory


Course Code: CSL716


Type of course: Practical

Prerequisites: FOC, OOPs and Operating System


Experiments that are to be conducted as a part of the course

1.

programs on #pragrma using C

2.

Programs using Sections, omp for and omp single

3.

Programs using thread private directives.

4.

Programs on scheduling.

5.

Programs using last private reduction, copyin and shared.

6.

Programs for Point to Point MPI calls

7.

Programs for Message passing MPI calls

8.

programs on CUDA


Text Book:

1.

Parallel Programming in OpenMP ,Rohit Chandra , Leo Dagum , DrorMaydan , David Kohr, Jeff

McDonald , Ramesh Menon.

2.

Multi-core programming,Increase performance through software multi-yhreading by Shameem Akhter and Jason

Roberts.

Course Delivery: The course will be delivered through, presentations, discussions, and practical implementations.

Course Assessment and Evaluation :

What

To

Whom

When/ Where


Max

Marks

Contribution to

Course Outcomes

CIE

SEE

Internal

Assessment Test1

Internal

Assessment Test2

Standard

Examination Students once once

Lab test

20

30

50

Evidence

Collected

Observation sheets

Observation sheets

Answer scripts

1,2 3

1,2,3,4,5

End of Course

Survey

End of the course

- Questionnaire

1,2,3,4 &5

1, 2 & 3,

Effectiveness of


Assessment Methods



1.

Propose HPC solutions to real time problem.

2.

Develop the programs using OPENMP, MPI and CUDA.

3.

Examine the performance of OPENMP, MPI and CUDA programs

22


Course Outcomes


1 2 3 4 5 6 7 8 9 10 11 12

Propose HPC solutions to real time problem. x x x x x

Develop the programs using OPENMP, MPI and

CUDA

Examine the performance of OPENMP, MPI and

CUDA programs x x x x x x x x x x

23

Course Title: Project

Credits (L:T:P) : 0:0:18



Type of course: Practical

Prerequisites: Nil

Total Contact Hours: 36 Hours/week


As a part of project, all the eligible final year students must carry out the following activities:

1.

Students should form a group to carry out their project. The minimum group size is 2 and maximum group size is 4.

2.

The groups will be attached to one Internal Guide (and Co-guide if necessary) by the Department.

3.

Students can carry out their project in-house or in a reputed organization (to be approved by Internal

Guide and HOD).

4.

Identify the problem statement based on the current state of Art and trends in the area of interest.

5.

Based on the survey, identify the project requirements and do feasibility study.

6.

Identify and draw a system level architecture by showing subsystems and their input/output need.

7.

Implement the programs using step by step for each module.

8.

Integrate and examine the implementation and test the project scope and the requirements.

9.

Prepare Project document and the demonstrating their work.

10.

The evaluation is based on presentation and report.

 The evaluation will be done by the internal guide and a co-examiner twice during the semester. o Mid-semester evaluation: Students must do a group presentation and produce documents of system requirements, and system design (during 6 th

week) o Final Evaluation: At the End of the semester students must do a group presentation, demonstrate the project work and submit the complete report. (during 13 th

week)


At the end of the course the students should be able to:

1.

Review the current state of Art and trends in their area of interest and identify a suitable problem in their chosen subject domain with justification.

2.

Survey the available research literature/documents for the tools and techniques to be used.

3.

Examine the functional, non-functional, and performance requirements of their chosen problem definition.

4.

Design system architecture and different components and develop all the system components using appropriate tools and techniques.

5.

Work effectively in a team and use good project management practices.

6.

Defend the project work carried out in teams orally and in writing.



Course Outcomes

1 2 3 4 5 6 7 8 9 10 11 12

Review the current state of Art and trends in their area of interest and identify a suitable problem in their chosen subject domain with justification

Survey the available research literature/documents for the tools and techniques to be used.

Examine the functional, non-functional, and performance requirements of their chosen problem definition. x x x x x x x x x x x x x x x x x x x x x x

24

Design system architecture and develop all the system components using appropriate tools and techniques. x x x x x x x x x x x

Work effectively in a team and use good project management practices.

Defend the project work carried out in teams orally and in writing.

Project Evaluation Rubrics:

Criteria for

Evaluation

Data Elicitation

Phase

Has

Level A(10)

90-100 investigated new trends in their area of interest, Review the challenges in that area.

Data elicitation should include new concepts.

Problem

Definition

Has investigated problem domain extensively

Planning

Project management

System Design

Precise Schedule and

Effort Estimation using tools.

Has taken leadership role in the project and monitored the progress of the project. In addition has completed all tasks assigned to him.

Literature Survey Has read more than 10 papers from reputed journals OR 15 papers

Requirements

Specification from conference AND 3 books in the area of the project.

Complete functional,

Non-functional,

Performance, Security related, Clear and

Measurable(in terms of

SMART Matrix)

Has played the role of main Architect in the project , Designed all the components of the project

Has investigated new trends in their area of interest, Review the some challenges in that area

Problem domain well understood, clear and specific description of problem, relevance well identified

Precise Schedule and

Effort

Partial project

90% x

Manually. the project.

, of x

Level B(8)

75-90

Estimation

Has monitored the progress of the project and completed all tasks assigned to him.

Has read more than 7 papers from reputed journals OR 12 papers from conference AND

2 books in the area of functional,

Non-functional,


Measurable(in terms of SMART Matrix) x

Has played the role of main Architect in the

Designed the

Has x x interest. context x

Estimation. the project. functional, functional,

In-Complete

Level C(6)

50-75 x x

Has investigated new trends in their area of

Moderately awareness of problem domain, clear description, broad idea about relevance to current technical and social

Schedule and Effort completed all the tasks assigned to him.

Has read more than 5 papers from reputed journals OR 8 papers from conference AND

1 books in the area of

Non-


Measurable(in terms of SMART Matrix)

Has played the role of main Architect in the project , Designed

75% of the x x x x

Has idea not investigated much on new trends in their area of interest

Minimal awareness of problem domain,

Vague description, little relevance to current technical and social context

Inappropriate

Schedule and

Effort Estimation.

Has not completed all the tasks assigned to him. from sources, x x

Level D(5)

Upto 50 x x about

Minimal, mostly incomplete general without focused study.

Few, narrow and requirements

Has not played the role of main

Architect in the project , Designed

25

Implementation Has decided the relevant tools and platforms required for the project by evaluating the alternatives. Has coded all the components designed by him by following the standard coding guidelines.

Testing and Results Meets all the requirements, Optimized

Report Writing

Presentation

Viva-voce and

Solution, Proper Test

Plan, Has performed

Integration Testing,

Performance Testing

Excellent Organization,

No technical or Grammar errors, Concise and

Precise, Complete documentation, done on

Latex

Excellent Professional and Technical communication, Effective

Presentations, able to analyze technically and clarify views in viva-voce components of the project components of the project

Has coded all the components designed by him by following the standard coding guidelines.

Barely meets all the requirements, Not

Optimized Solution,

Poor Test Plan, Has not performed


Performance Testing

Good Organization,

No technical or

Grammar errors,

Concise and Precise,

Incomplete documentation, done on Latex

Good Professional and

Technical communication,

Effective

Presentations.

Has coded all the components designed by him by not following the standard coding guidelines.

Barely meets all the requirements, done on Latex and communication,

Effective

Not

Optimized Solution,

Poor Test Plan, Has not performed


Performance Testing

Average Organization,

No technical or

Grammar errors,

Concise and Precise,

Incomplete documentation, Not

Average Professional

Technical

Presentations, Unable to analyze technically and clarify views in viva-voce

50% of the components of the project

Has not coded all the components designed by him by not following the standard coding guidelines.

Haphazard testing, barely results. meets requirements, unable to infer

Poor Clarity in technical contents and organization, error in grammar, not done in Latex

Poor Professional and Technical communication,

Not a Effective

Presentations,

Unable to analyze technically viva-voce and clarify views in

26

Course Title: Seminar

Course Code: CS813

Credits (L:T:P) : 0:0:1

Type of course: Practical

Assessment Criteria

% Marks to be awarded

Literature survey

Core/ Elective: Core


Seminars are used as course delivery modes to encourage students to gather current trends in technology, research literature, and self-learn topics of their interest. Seminars require students to research a technical topic, make presentations and write a detailed document on their findings individually under the guidance of faculty.

The student is expected to:

1.

Identify seminar topics based on contemporary technical, societal and environmental issues.

2.

Conduct literature survey on complex issues in the selected domain

3.

Explore advanced technologies

4.

Make good oral and written technical presentations

Course outcomes:

The student is expected to:

1.


2.


3.


4.

Make good oral and written technical presentations


Course Outcomes


1 2 3 4 5 6 7 8 9 10 11 12




Make good oral and written technical presentations x x x x x x x x x x x x x x x x x x x x

Level C

50-75

Level B

75-90

Level A

90-100

Few sources, aware of quality of resources and relevance to problem at hand

Multiple sources of high quality, good judgment of the information,, identification of gaps in knowledge

Multiple sources of high quality, well researched and analyzed, continuous efforts at acquiring information

27

Assessment Criteria

% Marks to be awarded

Report Writing

Level C

50-75

Level B

75-90

Level A

90-100

Reasonably good organization and lacks clarity in few topics, complete, few omissions, grammatically correct, lacks style

Sound organization and structure, clear, very few errors, complete, reasonably good style

Excellent organization, no technical or grammar errors, concise and precise, complete documentation

Presentation and viva voce

Reasonably good communication and presentation, able to give technical answers to some

Good , professional communication, good visual aids, able to give technical answers

Excellent professional and technical communication, effective presentations, able to analyze technically and clarify

Technical paper presentation in reputed Journals or

Conferences extent

Accepted in any National Level

Conferences/ Journals

Accepted and Presented in any International

Conferences/Journals held in India. views in viva-voce

Accepted in any International

Conferences/Journals held in outside India Standards as

IEEE/ACM.

Evaluate the student’s presentation employing the following range-scored criteria

Barely acceptable

(0 – 2 pts)

Basic

(3 Pts)

Good

(4 Pts)

Very Good

(5 Pts)

Background content

Methods

Material not clearly related to topic OR background dominated seminar

Material sufficient for clear understanding but not clearly presented

Material sufficient for clear understanding

AND effectively presented

Sufficient for

Material sufficient for clear understanding

AND exceptionally presented

Sufficient for Methods too brief or insufficient for adequate understanding OR too

Sufficient for understanding but not clearly presented understanding

AND effectively presented understanding

AND exceptionally presented

Results

(figures, graphs, tables, etc.) detailed

 Some figures hard to Read

 Some in inappropriate

Format

 Some explanations lacking

 Majority

Formatted

 appropriately

Reasonably explained

 Significance mentioned





Most figures clear

Most appropriately

Formatted

 Well explained







All figures clear

All appropriately formatted

Exceptionally explained

5

5

5

Total

Possible Earned

28

Graphics

(use of

Powerpoint)

Eye contact and Length and Pace

Conclusions

Uses graphics that rarely support text and presentation

 Reads most slides; no or just occasional eye contact

 Short; less than

30 min

 Rushed OR dragging throughout conclusions not supported by evidence; no discussion of implications and future work

Uses graphics that relate to text and presentation

 contact

Refers to slides to make points; occasional eye

 Short 40 min

OR long >50

 Rushed OR dragging in parts conclusions could be supported by stronger evidence; minimal discussion of implications and future work

Uses graphics that explain text and presentation

 Refers to slides to make points; eye contact majority of time

 Adequate 40-

45 min

 Most of the seminar well pace conclusions supported by evidence; some discussion of implications and future direction

Uses graphics that explain

And reinforce text and presentation

 Refers to slides to make points; engaged with

Audience

 Appropriate

(45-50 min)

 Well-paced throughout insightful conclusions supported by evidence; discusses implications and application; recommends future directions for research

29

5

5

5

Course Title: Technical Writing and Content Development (for lateral Entry students)

Course Code: CS8T1

Credits (L:T:P) : 0:0:1 Core/ Elective: Core

Type of course: Practicals Total Contact Hours: 28


This course is offered for lateral entry diploma students. Course consists of writing assignments to be done by individual students, who register for the course should

1.

Find topic with relevance to the current trends in the field of computer science & engineering.

2.

Get their topic approved by their internal guide assigned by the department.

3.

Carry out literature survey by referring IEEE research papers and may refer white paper

4.

Explore the features of Latex tool which will be used for documentation.

5.

Do thorough plan to organize the topics according to standard template given by the department.

6.

Aware of key ethical issues affecting computer science and their responsibilities as computer science professionals.

Rubric for Written Report:

Task Description: (Teacher may explain specific assignment in this space.)

Criteria

Exemplary

4

Yes

Accomplished

3

Yes, but

Developing

2

No, but

Beginning

1

No

10%

Directly relevant Somewhat relevant Remotely related Totally unrelated

Topic

Organization 10%

Good organization; points are logically ordered; sharp sense of beginning and end

Organized; points are somewhat jumpy; sense of beginning and ending

Quality of

Information

25%

Supporting details specific to subject

Some details are nonsupporting to the subject

Only one or two errors

Grammar,

Usage,

Mechanics,

Spelling

25%

No errors

Interest Level 10%

Vocabulary is varied; supporting details vivid

Neatness 10%

Typed; clean; neatly bound in a report cover; illustrations provided

Some organization; points jump around; beginning and ending are unclear

Details are somewhat sketchy. Do not support topic

Poorly organized; no logical progression; beginning and ending are vague

Unable to find specific details

More than two errors Numerous errors distract from understanding

Vocabulary is varied; supporting details useful

Legible writing, wellformed characters; clean and neatly bound in a report cover

Vocabulary is unimaginative; details lack “color”

Legible writing, some ill-formed letters, print too small or too large; papers stapled together

Basic vocabulary; needs descriptive words

Illegible writing; loose pages

30

Timeliness 10%

Report on time Report one class period late

Report two class periods late

Report more than one week late

References:

1.

http://www.writingassist.com/resources/links/

2.

http://ocw.mit.edu/courses/comparative-media-studies-writing

3.

http://toefl.uobabylon.edu.iq/papers/itp_2015_41931767.pdf

4.

http://www.docustar.co.il/Upload/links/Administrator%20Guide%20Samples.pdf

Course Assessment and Evaluation :

What

To

Whom

When/ Where


Max

Marks

Evidence

Collected

Contribution to

Course Outcomes

C

I

E

Technical

Report

Once 50 Reports 1,2 ,3 & 4

End of Course

Survey

Students

End of the course -

Questionnair e

1,2 ,3 & 4

Effectiveness of


Assessment

Methods


At the end of the course, the student should be able to

1.

Identify technical articles of good quality and relevance to a chosen topic.

2.

Organize information collected from literature survey in logical order.

3.

Produce document free from grammatical and typographical errors.

4.

Write a technical article in IEEE format in chosen topic.


Course Outcomes Program Outcomes

Identify technical articles of good quality and relevance to a chosen topic.

1 2 3 4 5 6 7 8 9 10 11 12 x x x x

Organize information collected from literature survey in logical order.

Produce document free from grammatical and typographical errors.

Write a technical article in IEEE format in chosen topic. x x x x x x x x x x x x x x

31

Course Title: Pattern Recognition


Course Code: CSPE711

Core/ Elective: Elective

Type of course: Lecture Total Contact Hours: 42 Hrs



Unit 1

Introduction: Machine perception, an example, Pattern Recognition System, The Design Cycle, Learning and

Adaptation. Bayesian Decision Theory: Introduction, Bayesian Decision Theory, Continuous Features, Minimum error rate, classification, classifiers, discriminant functions, and decision surfaces, the normal density, Discriminant functions for the normal density.

Unit 2

Maximum-likelihood and Bayesian Parameter Estimation: Introduction, Maximum-likelihood estimation, Bayesian

Estimation, Bayesian parameter estimation: Gaussian Case, general theory, Hidden Markov Models. Non-parametric

Techniques: Introduction, Density Estimation, Parzen windows, KN – Nearest- Neighbor Estimation, The Nearest-

Neighbor Rule, Metrics and Nearest-Neighbor Classification.

Unit 3

Linear Discriminant Functions: Introduction, Linear Discriminant Functions and Decision Surfaces, Generalized Linear

Discriminant Functions, The Two-Category Linearly Separable case, Minimizing the Perception Criterion Functions,

Relaxation Procedures, Non-separable Behavior, Minimum Squared-Error procedures, The Ho-Kashyap procedures.

Stochastic Methods: Introduction, Stochastic Search, Boltzmann Learning, Boltzmann Networks and Graphical Models,

Evolutionary Methods.

Unit 4

Non-Metric Methods: Introduction, Decision Trees, CART, Other Tree Methods, Recognition with Strings, Grammatical

Methods.

Unit 5

Unsupervised Learning and Clustering: Introduction, Mixture Densities and Identifiability, Maximum-Likelihood

Estimates, Application to Normal Mixtures, Unsupervised Bayesian Learning, Data Description and Clustering, Criterion

Functions for Clustering.

Text Book:

1.

Richard O. Duda, Peter E. Hart, and David G. Stork: Pattern Classification, 2nd Edition, Wiley-Interscience,

2012.

Reference Book:

1.

Earl Gose, Richard Johnsonbaugh, Steve Jost: Pattern Recognition and Image Analysis, HAR/DSK Edition,

Pearson Education, 2007.


PPT presentations, Black Board approach


At the end of the course the student should be able to

1.

Analyse using Top-down approach the pattern recognition System

2.

Interpret Bayesian decision theorem

3.

Analyse the bayesian estimation, Density estimation

4.

Explain linear discriminant functions

32

5.

Use different stochastic methods for linear discriminant functions.

6.

Check different non-parametric methods.

7.

Assess the criteria for unsupervised learning and clustering


Course Outcomes


1 2 3 4 5 6 7 8 9 10 11 12

Analyse using Top-down approach the pattern recognition System

Interpret Bayesian decision theorem

Analyse the bayesian estimation, Density estimation

Explain linear discriminant functions x x x x x x x

Use different stochastic methods for linear discriminant functions.

Check different non-parametric methods.

Assess the criteria for unsupervised learning and clustering x x x x x x

33

Course Title: Service Oriented Architecture




Type of course: Lecture Total Contact Hours: 42

Prerequisites: Internet Technology


Unit 1

Introduction to SOA, Evolution of SOA: Fundamental SOA; Common Characteristics of contemporary SOA; Common tangible benefits of SOA; An SOA timeline (from XML to Web services to SOA); The continuing evolution of SOA

(Standards organizations and Contributing vendors); The roots of SOA (comparing SOA to Past architectures). Web

Services and Primitive SOA: The Web services framework; services (as Web services); Service descriptions (with

WSDL); Messaging (with SOAP).

Unit 2

Web Services and Contemporary SOA: Message exchange patterns; Service activity; Coordination; Atomic Transactions;

Business activities; Orchestration; Choreography. Addressing; Reliable messaging; Correlation; Polices; Metadata exchange; Security; Notification and eventing

Web Services and Contemporary SOA: Message exchange patterns; Service activity; Coordination; Atomic Transactions;

Business activities; Orchestration; Choreography. Addressing; Reliable messaging; Correlation; Polices; Metadata exchange; Security; Notification and eventing

Unit 3

Principles of Service – Orientation: Services-orientation and the enterprise; Anatomy of a service-oriented architecture;

Common Principles of Service-orientation; How service orientation principles inter relate; Service-orientation and objectorientation; Native Web service support for service- orientation principles

Unit 4

Service Layers: Service-orientation and contemporary SOA; Service layer abstraction; Application service layer, Business service layer, Orchestration service layer; Agnostic services; Service layer configuration scenarios. Business Process

Design: WS-BPEL language basics; WS-Coordination overview; Service-oriented business process design; WSaddressing language basics; WS-Reliable Messaging language basics

Unit 5

SOA Platforms: SOA platform basics; SOA support in J2EE; SOA support in .NET; Integration considerations

Text Book :

1.

Thomas Erl: Service-Oriented Architecture – Concepts, Technology, and Design, Pearson Education, 2005

Reference Book :

2.

Eric Newcomer, Greg Lomow: Understanding SOA with Web Services, Pearson education, 2005

Course Delivery: The course will be delivered through lectures, presentations and classroom discussions.

34


What

To

Whom

CIE

Internal

Assessment

Tests

Announced quiz

Surprise Quiz

SEE

Standard

Examination

Students

When/ Where



Once

Once

End of Course

(Answering

5 of 10 questions)

Max

Marks

30

10

10

100

Evidence

Collected

Blue Books

Quiz Answers

Quiz Answers

Answer scripts

Contribution to Course

Outcomes

1,2,3 & 4

1, 2 ,3 & 4

Recollection

Skills

1,2,3 & 4

End of Course

Survey


1, 2 ,3, 4, 5

Effectiveness of Delivery of instructions &

Assessment Methods



1.

Summarize Web Service and Service oriented Architecture.

2.

Illustrate the principles of contemporary SOA and web service.

3.

Appraise the principles the layers of Service Oriented Architecture.

4.

Estimate the service oriented principles

5.

Categorize SOA support in J2EE and SOA support in .NET focusing on platform overview.



Course Outcomes

1 2 3 4 5 6 7 8 9 10 11 12

Summarize Web Service and Service oriented

Architecture

Illustrate the principles of contemporary SOA and web service

Appraise the principles the layers of Service

Oriented Architecture x x x x x x x x x x x x x x x x x x

Estimate the service oriented principles x x x x x x

Categorize SOA support in J2EE and SOA support in .NET focusing on platform overview x x x x x x

35

Course Title: Business Intelligence & Applications




Type of Course: Lecture Total Contact Hours: 42



Unit 1

Introduction to Business Intelligence: Types of digital data, Introduction to OLTP, OLAP and Data Mining, BI definitions and Concepts, Business Applications of BI, BI Framework, Role of Data Warehousing in BI, BI Infrastructure

Components – BI Process, BI Technology, BI Roles and Responsibilities.

Basics of Data Integration: Basics of Data Integration(ETL), Concepts of Data Integration, Need and advantages of using data integration, introduction to common data integration approaches, introduction to data quality, data profiling concepts and applications.

Unit 2

Unit 3

Introduction to Data Integration: Introduction to SSIS Architecture, Introduction to ETL using SSIS, Integration

Services objects, Data flow components – Sources, Transformations and Destinations, Working with transformations, containers, tasks, precedence constraints and event handlers.

Unit 4

Introduction to Multi-Dimensional Data Modeling: Introduction to data and dimension modeling, multidimensional data model, ER Modeling vs. multi dimensional modeling, Concepts of dimensions, facts, cubes, attribute, hierarchies, star and snowflake schema, introduction to business metrics and KPIs, Creating cubes using SSAS.

Unit 5

Basics of Enterprise Reporting: Introduction to enterprise reporting, Concepts of dashboards, balanced scorecards,

Project: Data warehouse creation and designing reports, Introduction to SSRS Architecture, Enterprise reporting using

SSRS, and Use of Business Intelligence Development Studio (BIDS).

Text Books:

1.

Prasad Rn, Seema Acharya: Fundamentals of Business Analytics, First Edition, Wiley India Pvt. Ltd, 2012.

2.

William H. Inmon: Building the Data Warehouse, 4th Edition, Wiley India Ed., Reprint 2012.

3.

Infosys Reference Book on Business Intelligence


1.

David Loshin: Business Intelligence, First Edition, Elsevier Science, 2003.

2.

Mike Biere: Business Intelligence for the Enterprise, First Edition, IBM Press, 2003

3.

Larissa T. Moss and Shaku Atre: Business Intelligence Roadmap, Addison-Wesley Professional, 2003.


At the end of the course, a student should be able to

1.

Differentiate types of data based on their characteristics

2.

Identify role of data warehouse and infrastructure components.

3.

Ilustrate the basics of data integration including data quality and data profiling.

4.

Implement different data integration approaches.

5.

Demonstrate the different methods of multi-dimensional modeling.

6.

Describe basics of Enterprise Reporting including data warehouse creation, designing reports and enterprise reporting using SSRS.

36

Course Title: Parallel Programming using CUDA








Unit 1

Introduction: GPUs as Parallel Computers, Architecture of a Model GPU, Why More Speed or Parallelism? Parallel

Programming Languages and Models, Overarching Goals.

History of GPU Computing: Evolution of Graphics Pipelines, GPU Computing.

Introduction to CUDA: Data Parallelism, CUDA Program Structure, A Matrix-Matrix Multiplication Example, Device

Memories and Data Transfer, Kernel Functions and Threading.

Unit 2

CUDA Threads: CUDA Thread Organization, Using blockIdx and threadIdx, Synchronization and Transparent

Scalability, Thread Assignment, Thread Scheduling and Latency Tolerance.

CUDA Memories: Importance of Memory Access Efficiency, CUDA Device Memory Types, A Strategy for Reducing

Global Memory Traffic, Memory as a limiting Factor to Parallelism.

Performance Considerations: More on Thread Execution, Global Memory Bandwidth, Dynamic Partitioning of SM

Resources, Data Perfecting, Instruction Mix, Thread Granularity, Measured Performance and Summary.

Unit 3

Floating Point Considerations: Floating Point Format, Representable Numbers, Special Bit Patterns and Precision,

Arithmetic Accuracy and Rounding, Algorithm Considerations.

Parallel Programming and Computational Thinking: Goals of Parallel Programming, Problem Decomposition,

Algorithm Selection, Computational Thinking.

Unit 4

Introduction to OPENCL: Background, Data Parallelism Model, Device Architecture, Kernel Functions, Device

Management and Kernel Launch, Electrostatic Potential Map in OpenCL.

Goals of Programming GPUs, Memory Architecture Evolution, Kernel Execution Control Evolution, Core Performance,

Programming Environment

Unit 5

Application Case Study - Advanced MRI Reconstruction: Application Background, Iterative Reconstruction, Computing

F

H d, Final Evaluation.

Application Case Study – Molecular Visualization and Analysis: Application Background, A Simple Kernel

Implementation, Instruction Execution Efficiency, Memory Coalescing, Additional Performance Comparisons, Using

Multiple GPUs.

Text Book:

1.

David B Kirk, Wen-mei W. Hwu, “Programming Massively Parallel Processors – A Hands-on Approach”, First

Edition, Elsevier and nvidia Publishers, 2010.


1.

Kai Hwang and Naresh Jotwani “Advanced Computer Architecture – Parallelism, Scalability, and

Programmability, Second Edition, TMH, 2011.

2.

Mattson, Sanders, Massingill: Patterns for Parallel Programming, Addison Wesley,2005, ISBN0-321-22811-1.


37

The course will be delivered through lectures, presentations, classroom discussions, practice exercises and practical sessions.


What

Internal

Assessment

Tests

To

Whom

When/ Where



Max

Marks

15

Evidence

Collected

Blue Books

Contribution to

Course

Outcomes

1,2,3,4 & 5

CIE

SEE

Mini Projects

Semester End

Examination

Students

Will be carried out by a batch of two students. Evaluation is at the end of the

Semester

End of Course

(Answering

5 of 10 questions)

35

100

Project

Report, Code

Repository

Answer scripts

1,2,3,4 & 5

1,2,3,4 & 5

End of Course

Survey


1,2,3,4 & 5

Effectiveness of


Assessment

Methods

Course Outcomes


1.

Identify the advantages and need of GPUs as an emerging technology

2.

Design the programs using CUDA-C/OPENCL

3.

Demonstrate Heterogeneous Computing on CPUs and GPUs

4.

Analyze the speedup of programs on GPUs when compared to CPUs

5.

Illustrate the usage of different programming abstractions using CUDA-C on GPUs



Course Outcomes

1 2 3 4 5 6 7 8 9 10 11 12

Identify the advantages and need of GPUs as an emerging technology

Design the programs using CUDA-C/OPENCL

Demonstrate Heterogeneous Computing on CPUs and GPUs

Analyze the speedup of programs on GPUs when compared to

CPUs

Illustrate the usage of different programming abstractions using

CUDA-C on GPUs x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

38

Course Title: Project based learning : Internet of Things / Data Analytics Course Code: CSPE734


Type of Course: Tutorials, Practical





In Project Based Learning students gain knowledge and skills by working for an extended period of time to investigate and respond to a complex question, problem, or challenge. Essential Project Design Elements include:

 Key Knowledge, Understanding, and Success Skills - The project is focused on student learning goals, including standards-based content and skills such as critical thinking/problem solving, collaboration, and selfmanagement.

 Challenging Problem or Question - The project is framed by a meaningful problem to solve or a question to answer, at the appropriate level of challenge.

 Sustained Inquiry - Students engage in a rigorous, extended process of asking questions, finding resources, and applying information.

 Authenticity - The project features real-world context, tasks and tools, quality standards, or impact – or speaks to students’ personal concerns, interests, and issues in their lives.

 Student Voice & Choice - Students make some decisions about the project, including how they work and what they create.

 Reflection - Students and teachers reflect on learning, the effectiveness of their inquiry and project activities, the quality of student work, obstacles and how to overcome them.

 Critique & Revision - Students give, receive, and use feedback to improve their process and products.

 Public Product Students make their project work public by explaining, displaying and/or presenting it to people beyond the classroom.

Students will form a team of 3-4 members and execute a project on either Internet of Things or Data Analytics

Internet of Things

As a part of this project Students

 Should demonstrate the usage of any available SoC like Raspberrby Pi, Intel Edison, Beagle Bone etc.

 Should implement data collection from the attached/independent sensors by some means of communication at the SoC.

 Should use minimum of two sensors in their projects

 Should demonstrate the parsing and serializing of raw data from the sensors.

 Should demonstrate the storage of the collected data on a locally created server or on any cloud service

 Should implement the storage in any database of choice.

 Should demonstrate analysis of the data

 Should develop a web/mobile application for the data stored/analyzed at the server

Data Analytics

As a part of this project Students

 Should acquire data from publicly available resources or create a means for collecting this large amount of data.

 The dataset should have atleast 100,000 data points

 Should demonstrate the filtering of data in order to keep only required data, or to bring raw data to a standard format

 Should demonstrate the storage of this large amount of data in a database of choice

39

 Database should be used as a means of storing the data across various stages of process

 Should use parallel processing methods by means of Hadoop, spark etc. or analytical methods like clustering, classification etc. by means of languages like R, Python etc. or both

 Should derive meaningful insights/conclusions from the dataset.

Course Outcomes for Project Based Learning on Internet of Things:

1.

Develop sensor connected wold

2.

Use different hardware components like Arduino, Raspberry pi and Beagle bone

3.

Demonstrate the interfacing of various I/O devices and senosors.

Course Outcomes for Project Based Learning on Data Analytics:

1.

Identify different techniques of statistical modeling

2.

Develop solid data analysis skills

3.

Identify properties of good quality data

4.

Use relevant tools and make predictions about sets of data.

5.

Design and implement a database, and use managements systems effectively


Course Outcomes

Develop sensor connected wold

1 2 3


4 5 6 7 8 9 10 11 12 x x x x x x x x x x

Use different hardware components like Arduino, Raspberry pi and

Beagle bone

Demonstrate the interfacing of various I/O devices and senosors.

Identify different techniques of statistical modeling

Develop solid data analysis skills

Identify properties of good quality data

Use relevant tools and make predictions about sets of data. x x x x x x x x x x x x x x x x x x x x x

Design and implement a database, and use managements systems effectively x x x x x

40

Course Title: Distributed Systems



Prerequisites: Internet Technologies





Unit 1

Introduction: Distributed Systems - Need and motivation, Challenges(Fallacies); Architecture: System Models, common terminologies; Distributed System Kinds: Storage, Compute, Coordination

Unit 2

Distributed Storage Systems - 1: Shared Memory: Model, Distributed Shared Memory, Linearizability; Data Aspects :

Partitioning, Sharding, Fault-Tolerance, Consistency, ACID; CAP Theorem , BASE, Brewer’s Conjecture explained;

Unit 3

Distributed Storage Systems - 2: Distributed Replication: Consistency and Paxos Algorithm; Access Patterns and

Data Models: Key-Value, Document, Columnar, Files, Graph, Inverted Index; DynamoDb: Distributed Key-Value

Store, Architecture, CAP Implications HDFS: Distributed File System, Architecture, CAP Implications; Elasticsearch :

Distributed search solution, CAP Implications; Other distributed storage solution as Case Study and assignment

Unit 4

Distributed Compute Systems: Synchronous and Asynchronous Processing, Data and Task Parallel systems, Data locality; Fault Tolerance in Processing; Pipelines and Workflows; MapReduce: Programming paradigm, Google

MapReduce - Architecture and phases. Stream Processing : Introduction and Concepts, Comparative study of S4, Storm and Borealis. [Additional Reading - Split Query processing, other forms of distributed query processing]

Unit 5

Distributed Coordination: Why coordination in Distributed systems?, Zookeeper: Architecture and applications; Paxos revisited; Leader election, Byzantine Problem simple description, Quorum Systems

Reading Material:

Main References:

 Coulouris et.al. Distributed Systems Concepts and Design

 Links: http://azmuri.files.wordpress.com/2013/09/george-coulouris-distributed-systems-concepts-and-design-

5th-edition.pdf

 https://drive.google.com/file/d/0B3XfLh7sFg6xdmVRdE1ZcWcwbHM/preview (Just save locally)

 Notes on Theory of Distributed Systems,James Apnes

 http://cs-www.cs.yale.edu/homes/aspnes/classes/465/notes.pdf

 Distributed algorithms Nancy Lynch

Unit1: Recommended Reading -

 https://gist.github.com/jboner/2841832

 Building a distributed system requires a methodical approach to requirements - https://queue.acm.org/detail.cfm?id=2482856

 Distributed Systems Fallacies - http://www.rgoarchitects.com/Files/fallacies.pdf

 Chapter 1 and 2 of Coulouris et.al Distributed Systems Concepts and Design Coulouris.pdf

 Lecture Notes: Introduction to DistSys

 http://www.cs.helsinki.fi/u/jakangas/Teaching/DistSys/DistSys-08f-1.pdf

 www.cis.upenn.edu/~lee/00cse380/lectures/ln13-ds.ppt

 http://www0.cs.ucl.ac.uk/staff/ucacwxe/lectures/ds98-99/dsee3.pdf

41

 For discussion on Common Terminologies, refer Coulouris. Section 1.5 but use terminologies in the above presentations

Unit2:

 Notes on Theory of Distributed Systems,James Apnes: Chapter 15 and 16 http://cswww.cs.yale.edu/homes/aspnes/classes/465/notes.pdf

 CAP Theorem - http://www.cs.berkeley.edu/~brewer/cs262b-2004/PODC-keynote.pdf

 Proof/Explanation for Brewer's CAP Conjecture - https://github.com/machinelearner/distSysTheory/blob/master/literature/brewers-conjecture/BrewersConjecture-

SigAct.pdf?raw=true

 http://webpages.cs.luc.edu/~pld/353/gilbert_lynch_brewer_proof.pdf

 Distributed Databases: http://pcbunn.cithep.caltech.edu/DistributedDatabasesPakistan.pdf(Partitioning),

 Sharding ,Replication and CAP implications - http://www.dia.uniroma3.it/~torlone/bigdata/L7-NoSQL.pdf

 http://www.inf.unibz.it/dis/teaching/DDB/ln/ddb03.pdf(Explained partitioning as fragmentation)

 Linearizability: https://github.com/machinelearner/distSysTheory/blob/master/literature/linearizability/p463herlihy.pdf?raw=true(Can leave out the axioms and theorems - not in scope for under-grad class)

 Consistency - http://www.cs.uoi.gr/~pitoura/courses/ds04_gr/cons-repl.pdf

 Failures and distributed Systems http://www.cse.psu.edu/~gcao/teach/513-00/c7.pdf

Unit3:

 Replication: http://www.cs.cmu.edu/~dga/15-440/F12/lectures/15-replication.pdf

 Coulouris Chapter 18 and http://book.mixu.net/distsys/replication.html

 Paxos Lecture - https://www.youtube.com/watch?v=WX4gjowx45E

 Paxos Algorithm Explained - http://research.microsoft.com/en-us/um/people/lamport/pubs/paxos-simple.pdf

 http://the-paper-trail.org/blog/consensus-protocols-paxos/

 DynamoDb http://www.allthingsdistributed.com/files/amazon-dynamo-sosp2007.pdf

 HDFS Architecture: http://hadoop.apache.org/docs/r1.2.1/hdfs_design.html ,http://aosabook.org/en/hdfs.html

 https://github.com/machinelearner/distSysTheory/blob/master/literature/hdfs/hdfs.pdf?raw=true

Unit4:

 Parallel Computing Paradigm http://www.buyya.com/cluster/v2chap1.pdf,

 http://www.cs.unc.edu/~dewan/242/s07/notes/ipc/node9.html

 http://www.cse.iitd.ernet.in/~dheerajb/parallel_paradigms.pdf

 MapReduce Google http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduceosdi04.pdf

 S4 - Stream Processing http://www.4lunas.org/pub/2010-s4.pdf

 Borealis - http://www.cs.harvard.edu/~mdw/course/cs260r/papers/borealis-cidr05.pdf

 http://grids.ucs.indiana.edu/ptliupages/publications/survey_stream_processing.pdf

 Distributed Algorithms by Nancy Lynch - Chapters on Asynchronous and Synchronous Networks

Unit5:

 Refer to Notes - James Apnes 6,7,10,14

 Consensus Survey - http://courses.csail.mit.edu/6.897/fall04/papers/Fischer/fischer-survey.ps

 Refer: Distributed algorithms Nancy Lynch for further reading

 Zookeeper Overview: http://zookeeper.apache.org/doc/trunk/zookeeperOver.pdf

 Zookeeper - Yahoo Research https://github.com/machinelearner/distSysTheory/blob/master/literature/zookeeper/Hunt.pdf?raw=true

42

 Byzantine generals problem: http://research.microsoft.com/en-us/um/people/lamport/pubs/byz.pdf

Course Outcomes :

At the end of the course the students will be able to:

1.

Describe the architecture models of the distributed systems

2.

Survey the available distributed storage systems through case study and assignment

3.

Defend the use of various techniques in distributed compute systems

4.

Explain the modern architectures of co-ordintation in distributed systems



Course Outcomes

1 2 3 4 5 6 7 8 9 10 11 12

Describe the architecture models of the distributed systems

Survey the available distributed storage systems through case study and assignment x x x x x x x x x x x

Defend the use of various techniques in distributed compute systems

Explain the modern architectures of co-ordintation in distributed systems x x x x x x x x x x x

43

Course Title: Data Mining




Type of course: Lecture Total Contact Hours: 56 Hrs



Unit 1

DATA MINING Introduction – Data – Types of Data – Data Mining Functionalities –Classification of Data Mining

Systems – Issues –Data Preprocessing- C4.5 Algorithm Description- C4.5 Features - Two Illustrative Examples.

Unit 2

ASSOCIATION RULE MINING Mining Frequent Patterns – Apriori Algorithm Description, . - Two Illustrative

Examples- Mining various Kinds of Association Rules – Correlation Analysis – Constraint Based Association Mining.

Unit 3

CLASSIFICATION AND PREDICTION Basic Concepts - Decision Tree Induction - Bayesian Classification – Rule

Based Classification – Classification by Back propagation – Support Vector Machines – Associative Classification – Lazy

Learners – Other Classification Methods – Prediction.

Unit 4

CLUSTERING AND TRENDS IN DATA MINING Cluster Analysis - Types of Data – Categorization of Major

Clustering Methods – K-means– Partitioning Methods – Hierarchical Methods - Density-Based Methods –Grid Based

Methods – Model-Based Clustering Methods – Clustering High Dimensional Data - Constraint – Based Cluster Analysis

– Outlier Analysis – Data Mining Applications.

Unit 5

MINING Mining the World Wide Web - Page Rank Algorithm, Text mining, Mining Time Series Data, The CART

Algorithm Briefly Stated, Ensemble methods-Increasing the Accuracy, Mining genomic data.

References:

1.

Jiawei Han and Micheline Kamber: Data Mining Concepts and Techniques, Elsevier, 2 nd

Edition, 2009.

2.

Xindong Wu and Vipin Kumar: The top ten Algorithms in Data Mining, Chapman and Hall/CRC press.

3.

Pang-Ning Tan, Michael Steinbach and Vipin Kumar, “Introduction to Data Mining”, Person Education, 2007.

4.

K.P. Soman, Shyam Diwakar and V. Aja, “Insight into Data Mining Theory and Practice”, Eastern Economy

5.

Edition, Prentice Hall of India, 2006.

6.

G. K. Gupta, “Introduction to Data Mining with Case Studies”, Eastern Economy Edition, Prentice Hall of India, 2006.

7.

Daniel T.Larose, “Data Mining Methods and Models”, Wiley-Interscience, 2006.



1.

Understand ways in which the methods of data mining differ from the more classical statistical approaches to data analysis, the rationale for data mining methods.

2.

Illustrate the kinds of patterns that can be discovered by association rule mining.

3.

Implement different classification and prediction techniques in numerous applications.

4.

Compute dissimilarities between objects represented by various attributes or variable types and examine several clustering techniques.

5.

Analyze the results generated from the constructed artifact to determine if patterns of clusters were detected in the data sets.

6.

Assess time series data from web and use it for prediction.

7.

Perform data Preprocessing, Classification algorithms using WEKA, R.

44


Course Outcomes

Understand ways in which the methods of data mining differ from the more classical statistical approaches to data analysis, the rationale for data mining methods.

Illustrate the kinds of patterns that can be discovered by association rule mining.

Implement different classification and prediction techniques in numerous applications.

Compute dissimilarities between objects represented by various attributes or variable types and examine several clustering techniques.

Analyze the results generated from the constructed artifact to determine if patterns of clusters were detected in the data sets.

Assess time series data from web and use it for prediction.

Perform data Preprocessing, Classification algorithms using WEKA, R.


1 2 3 4 5 6 7 8 9 10 11 12 x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

45

Course Title: Information Storage and Management




Type of course: Lecture Total Contact Hours: 56

Prerequisites: Computer Networks, Operating System, Computer Organization


Unit 1

Introduction: Information Storage, Evolution of Storage Architecture, Data Centre Infrastructure, Virtualization and

Cloud Computing. Data Centre Environment: Application, DBMS, Host, Connectivity, Storage, Disk Drive Components,

Disk Drive Performance, Host Access to Data, Direct-Attached Storage, Storage Design Based on Application, Disk

Native Command Queuing, Introduction to Flash Drives.

Unit 2

Data Protection: RAID Implementation Methods, Array Components, Techniques, Levels, Impact on Disk Performance,

Comparison, Hot Spares. Intelligent Storage System: Components, Storage Provisioning, Types.

Unit 3

Fiber Channel Storage Area Networks: FC Overview, Evolution, Components, FC Connectivity, Ports, FC Architecture,

Fabric Services, Login Types, Zoning, FC Topologies, Virtualization in SAN.IP SAN and FCoE: iSCSI, FCIP, FCoE.

Unit 4

Network-Attached Storage: Benefits, Components, NAS I/O Operation, Implementations, File Sharing Protocols, I/O

Operations, Factors Affecting NAS Performance, File-Level Virtualization. Object Based and Unified Storage: Object

Based Storage Devices, Content Addressed Storage, CAS Use Cases, Unified Storage. Backup methods and recovery

Unit 5

Business Continuity: Information Availability, Terminology, Planning Lifecycle, Failure Analysis, Impact Analysis,

Solutions. Cloud Computing: Cloud Enabling Technologies, Characteristics, Benefits, Service Models, Deployment

Models, Infrastructure, Challenges, Adoption Considerations. Securing the Storage Infrastructure: Framework, Risk

Triad, Domains Managing the Storage Infrastructure: Monitoring, Management Activities, Management Challenges,

Information Lifecycle Management, Storage Tiering.

Text Books:

1.

Somasundaram G, Alok Shrivastava, (EMC Education Services) “Information Storage and Management”; 2e,

Wiley India, 2012, ISBN 9788126537501


1.

Ulf Troppens, Rainer Erkens and Wolfgang Muller: Storage Networks Explained, 1st Edition, Wiley India, 2012.

2.

Robert Spalding: Storage Networks, the Complete Reference, 1 st

Edition, Tata McGraw Hill, 2011.


The course will be delivered through lectures class room interaction group discussion and exercise.

46


CIE

What

Internal

Assessment Tests

Class-room

Surprise Quiz

To

Whom

When/ Where



Twice(Summation of the two will be computed)

Max

Marks

30

20

Evidence

Collected

Blue Books

Quiz papers

Contribution to

Course Outcomes

1,2 3,4,5,6,7,8

1,2 3,4,5,6,7,8

SEE

Standard

Examination

Students

End of Course

(Answering

5 of 10 questions)

100 Answer scripts 1,2 3,4,5,6,7 & 8

End of Course

Survey



At the end of the course the students should be able to

1.

Appreciate need for storage centric architecture with its advantages

2.

Calculate the no of disk required considering the IOPS requirements of an application

3.

Examine the various types of zoning and FC topologies before implementation

4.

Select IP protocols and hardware required enterprise spread over geographical locations

5.

Examine NAS storage for enterprise that require fast deployment

6.

Identify the Business continuity plan depending on the criticality of the business

7.

Formulate backup recovery depending on the business objectives

8.

Suggest the storage life cycle management depending on value of data depending on its antiquity

1,2 3,4,5,6,7,8

Effectiveness of


Assessment

Methods


Course Outcomes


1 2 3 4 5 6 7 8 9 10 11 12

Appreciate need for storage centric architecture with its advantages x x x x x

Calculate the no of disk required considering the IOPS requirements of an application

Examine the various types of zoning and FC topologies before implementation x x x x x x

Select IP protocols and hardware required enterprise spread over geographical locations

Examine NAS storage for enterprise that require fast deployment

Identify the Business continuity plan depending on the criticality of the business

Formulate backup recovery depending on the business objectives

Suggest the storage life cycle management depending on value of data depending on its antiquity x x x x x x x x x x x x x x x x x x x x x x x x x x x x x x

47

Course Title: Wireless Networks and Mobile Computing





Prerequisites: Computer Networks


Unit 1

Mobile Computing Architecture: Types of Networks, Architecture for Mobile Computing, 3-tier Architecture, Design

Considerations for Mobile Computing, Wireless Networks – 1: GSM and SMS: Global Systems for Mobile

Communication GSM and Short Service Messages ( SMS): GSM Architecture, Entities, Call routing in GSM, PLMN

Interface, GSM Addresses and Identities, Network Aspects in GSM, Mobility Management, GSM Frequency allocation,

Introduction to SMS, SMS Architecture, SM MT, SM MO, SMS as Information bearer, applications

Unit 2

Wireless Networks – 2: GPRS : GPRS and Packet Data Network, GPRS Network Architecture, GPRS Network

Operations, Data Services in GPRS, Applications for GPRS, Billing and Charging in GPRS,Wireless Networks – 3:

CDMA, 3G and WiMAX: Spread Spectrum technology, IS-95, CDMA versus GSM, Wireless Data, Third Generation

Networks, Applications on 3G, Introduction to WiMAX.

Unit 3

Mobile Client: Moving beyond desktop, Mobile handset overview, Mobile phones and their features, PDA, Design

Constraints in applications for handheld devices. Mobile IP: Introduction, discovery, Registration, Tunneling, Cellular

IP, Mobile IP with IPv6

Unit 4

Mobile OS and Computing Environment: Smart Client Architecture, The Client: User Interface, Data Storage,

Performance, Data Synchronization, Messaging. The Server: Data Synchronization, Enterprise Data Source, Messaging.

Mobile Operating Systems: WinCE, Palm OS, Symbian OS, Linux, Proprietary OS Client Development: The development process, Need analysis phase, Design phase, Implementation and Testing phase, Deployment phase,

Development Tools, Device Emulators.

Unit 5

Building, Mobile Internet Applications: Thin client: Architecture, the client, Middleware, messaging Servers,

Processing a Wireless request, Wireless Applications Protocol (WAP) Overview, Wireless Languages: Markup

Languages, HDML, WML, HTML, cHTML, XHTML, VoiceXML. J2ME: Introduction, CDC, CLDC, MIDP,

Programming for CLDC, MIDlet model, Provisioning, MIDlet life-cycle, Creating new application, MIDlet event handling, GUI in MIDP, Low level GUI Components, Multimedia APIs, Communication in MIDP, Security

Considerations in MIDP.

Text Books:

1.

Dr. Ashok Talukder, Ms Roopa Yavagal, Mr. Hasan Ahmed: Mobile Computing, Technology, Applications and

Service Creation, 2nd Edition, Tata McGraw Hill, 2010.

2.

Martyn Mallik: Mobile and Wireless Design Essentials, First Edition, Wiley, 2011.


1.

2.

Raj Kamal: Mobile Computing, 2 st

Edition, Oxford University Press, 2012. st

Iti Saha Misra: Wireless Communications and Networks, 3G and Beyond,1 Edition, Tata McGraw Hill, 2011.



1.

Identify, by inspection the different types of networks and their design considerations like GSM and SMS

2.

Analyze the architecture of the GPRS networks and study of different wireless networks like, CDMA, 3G and

WiMAX: Spread Spectrum technology, IS-95, CDMA versus GSM,

3.

Analyze the design of mobile hand phones and their features, and study of Mobile IP: discovery, Registration,

Tunneling, Cellular IP, Mobile IP with IPV6

4.

Describe mobile OS and computing environment

5.

Design and build mobile internet applications

48

Course Title: Software Testing




Type of course: Lecture, Practicals Total Contact Hours: 70



Unit 1

A Perspective on Testing, Examples: Basic definitions, Test cases, Insights from a Venn diagram, Identifying test cases,

Error and fault taxonomies, Levels of testing. Examples: Generalized pseudo code, the triangle problem, The Next Date function, The commission problem, The SATM (Simple Automatic Teller Machine) problem, The currency converter,

Saturn windshield wiper.

Unit 2

Boundary Value Testing, Equivalence Class Testing, Decision Table-Based Testing: Boundary value analysis, Robustness testing, Worst-case testing, Special value testing, Examples, Random testing, Equivalence classes, Equivalence test cases for the triangle problem, Next Date function, and the commission problem, Guidelines and observations. Decision tables,

Test cases for the triangle problem, Next Date function, and the commission problem, Guidelines and observations. Path

Testing, Data Flow Testing:DD paths, Test coverage metrics, Basis path testing, guidelines and observations, Definition-

Use testing, Slice-based testing, Guidelines and observations.

Unit 3

Levels of Testing, Integration Testing: Traditional view of testing levels, Alternative life-cycle models, The SATM system, Separating integration and system testing. A closer look at the SATM system, Decomposition-based, call graphbased, Path-based integrations, System Testing, Interaction Testing:Threads, Basic concepts for requirements specification, Finding threads, Structural strategies and functional strategies for thread testing, SATM test threads, System testing guidelines, ASF (Atomic System Functions) testing example. Context of interaction, A taxonomy of interactions,

Interaction, composition, and determinism, Client/Server Testing.

Unit 4

Process Framework: Validation and verification, Degrees of freedom, Varieties of software. Basic principles:Sensitivity, redundancy, restriction, partition, visibility, Feedback. The quality process, Planning and monitoring, Quality goals,Dependability properties, Analysis, Testing, Improving the process, Organizational factors, Fault-Based

Testing,Test Execution:Overview, Assumptions in fault-based testing, Mutation analysis, Fault-based adequacy criteria,Variations on mutation analysis. Test Execution: Overview, from test case specifications to test cases, Scaffolding,

Generic versus specific scaffolding, Test oracles, Self-checks as oracles, Capture and replay.

Unit 5

Planning and Monitoring the Process, Documenting Analysis and Test: Quality and process, Test and analysis strategies and plans, Risk planning, Monitoring the process, Improving the process, The quality team, Organizing documents, Test strategy document, Analysis and test plan, Test design specifications documents, Test and analysis reports.

Text Books:

1.

Paul C. Jorgensen: Software Testing, A Craftsman’s Approach, 3rdEdition, Auerbach Publications, 2012.

2.

Mauro Pezze, Michal Young: Software Testing and Analysis –Process, Principles and Techniques, 1stEdition,

WileyIndia, 2011.


1.

Aditya P Mathur:Foundations of Software Testing, 1stEdition,Pearson Education, 2008.

2.

Srinivasan Desikan, Gopalaswamy Ramesh: Software testing Principles and Practices, 2ndEdition, Pearson

Education, 2007

49


The course will be delivered through lectures, presentations, classroom discussions, and practical implementations.



1.

Identify Test cases, Error and fault taxonomies, Levels of testing.

2.

Classify different types of testing (Boundary Value Testing, Equivalence Class Testing and Decision Table-Based

Testing).

3.

Recognize Alternative life - cycle models, recognize Basic concepts for requirements specification, assess context of interaction.

4.

Recognize approaches for Test Execution: from test case specifications to test cases, Scaffolding, Generic versus specific scaffolding.

5.

Identify and plan strategies to test design specifications document.


Course Outcomes


1 2 3 4 5 6 7 8 9 10 11 12

Identify Test cases, Error and fault taxonomies, Levels of testing.

Classify Boundary Value Testing, Equivalence Class

Testing and Decision Table-Based Testing.

Recognize Alternative life - cycle models, recognize

Basic concepts for requirements specification, assess context of interaction.

Recognize approaches for Test Execution: from test case specifications to test cases, Scaffolding, Generic versus specific scaffolding.

Identify analysis strategies and plans, to Test design specifications documents, to Test and analysis reports. x x x x x x x x x x x x x x x x x x x x x

50

Course Title: Software Architecture







Unit 1

Introduction: The Architecture Business Cycle: Where do architectures come from? Software processes and the architecture business cycle, What makes a “good” architecture? What software architecture is and what it is not, Other points of view, Architectural patterns, reference models and reference architectures, Importance of software architecture,

Architectural structures and views.

Unit 2

Architectural Styles and Case Studies: Architectural styles, Pipes and filters, Data abstraction and object-oriented organization, Event-based, implicit invocation, Layered systems, Repositories, Interpreters, Process control, Other familiar architectures, Heterogeneous architectures. Case Studies: Keyword in Context, Instrumentation software, Mobile robotics, Cruise control, Three vignettes in mixed style.

Unit 3

Quality: Functionality and architecture, Architecture and quality attributes, System quality attributes, Quality attribute scenarios in practice, Other system quality attributes, Business qualities, Architecture qualities. Achieving Quality:

Introducing tactics, Availability tactics, Modifiability tactics, Performance tactics, Security tactics, Testability tactics,

Usability tactics, Relationship of tactics to architectural patterns, Architectural patterns and styles.

Unit 4

Architectural Patterns: Introduction, From mud to structure: Layers, Pipes and Filters, Blackboard. Distributed Systems:

Broker, Interactive Systems: MVC, Presentation-Abstraction- Control. Adaptable Systems: Microkernel, Reflection.

Unit 5

Some Design Patterns: Structural decomposition: Whole – Part, Organization of work: Master – Slave, Access Control:

Proxy. Designing and Documenting Software Architecture: Architecture in the life cycle, Designing the architecture,

Forming the team structure, Creating a skeletal system, Uses of architectural documentation, Views, Choosing the relevant views, Documenting a view, Documentation across views.

Text Books:

1.

Len Bass, Paul Clements, Rick Kazman: Software Architecture in Practice, 2nd Edition, Pearson Education,

2011.

2.

Frank Buschmann, Regine Meunier, Hans Rohnert, Peter Sommerlad, Michael Stal: Pattern-Oriented Software

Architecture, A System of Patterns, Volume 1, John Wiley and Sons, 2011.

3.

Mary Shaw and David Garlan: Software Architecture- Perspectives on an Emerging Discipline, Prentice-Hall of

India, 2007.


1.

E. Gamma, R. Helm, R. Johnson, J. Vlissides: Design Patterns- Elements of Reusable Object-Oriented Software,

1 st


2.

Web site for Patterns: http://www.hillside.net/patterns/



1.

Discuss the fundamentals of software architecture.

2.

Identify the advantages and disadvantages for various architectural choices.

3.

Ilustrate Software architecture and quality requirements of a software system.

4.

Differentiate different architecture styles

5.

Identify the Methods, techniques, and tools for describing software architecture and documenting design patterns.

51

Course Title: Machine Learning Techniques




Type of Course: Lecture, Practicals Total Contact Hours: 70



Unit 1

Introduction: Probability theory (Bishop ch-1 & Appendix B,C); What is machine learning, example machine learning applications (Alpaydin ch-1) Supervised Learning: Learning a Class from Examples, VC-dimension, PAC learning,

Noise, Learning multiple classes, Regression, Model selection and generalisation. (Alpaydin ch-2)

Unit 2

Bayesian Learning: Classification, losses and risks, utility theory (Alpaydin ch3 (3.1, 3.2, 3.3, 3.5)) MLE, Evaluating an estimator, bayes estimator, parametric classificaion (Alpaydin ch4 - 4.1-4.5) (Bishop 4.2); Discriminant functions

Introduction, Discriminant functions, Least squares classification, Fisher’s linear discriminant, fixed basis functions, logistic regression (Bishop 4.1,4.3.1,4.3.2)

Unit 3

Multivariate methods: Multivariate Data,Parameter Estimation,Estimation of Missing Values,Multivariate Normal

Distribution,Multivariate Classification,Tuning Complexity,Discrete Features,Multivariate Regression (Alpaydin ch-5)

Nonparametric methods: Nearest Neighbor Classifier, Nonparametric Density Estimation (Alpaydin ch-8 selected topics)

Unit 4

Maximum margin classifiers: SVM, Introduction to kernel methods, Overlapping class distributions, Relation to logistic regression,Multiclass SVMs, SVMs for regression (Bishop ch 6 and 7 only covered topics). Mixture models and EM Kmeans clustering, Mixture of gaussians, Hierarchical Clustering, Choosing the Number of Clusters (Bishop 9.1,9.2,

Alpaydin 7.7,7.8)

Unit 5

Dimensionality reduction - (Alpaydin ch6) 10. Combining Models (Bishop ch-14)

Text Books:

1.

Ethem Alpaydin "Introduction To Machine Learning" 2nd Edition PHI Learning Pvt. Ltd-New Delhi

2.

Christopher Bishop "Pattern Recognition and Machine Learning" CBS Publishers & Distributors-New Delhi



1.

Explain the concepts and issues of learning systems.

2.

Evaluate decision tree based learning algorithm.

3.

Evaluate Bayesian learning algorithm.

4.

Determine sample complexity for infinite hypothesis spaces

5.

Evaluate rule- based learning algorithm.

52

Course Title: Cloud Computing


Type of course: Lecture






Unit 1

Introduction: Definition, characteristics, Benefits, challenges of cloud computing, cloud models : service-IaaS-PaaS-

SaaS, deployment-Public,Private,hybrid,Community, cloud services : Amazon, Google, Azure, online services, open source private clouds, SLA. Applications : Healthcare, Energy systems, transportation, manufacturing, Education,

Government, mobile communication, application development.

Cloud Application Development: Amazon web Services: EC2 Instances, Connecting Clients, Security Rules, Launch an

EC2 Linux Instance and connect it, create EC2 placement Group, to use S3 in java, to manage SQS services in C#, to install simple notification service on Ubuntu 10.04.

Unit 2

Cloud Architecture, programming model: NIST reference architecture, Architectural styles of cloud applications , single , multi ,hybrid cloud site, redundant, non redundant , 3 tier, multi tier architectures , programming model : compute and data intensive , Compute intensive model : parallel computation – BSP , Workflows , coordination of multiple activities- zoo keeper, data intensive model : big data- map reduce programming model ,map reduce in cloud, map reduce applications – hadoop distributed file system, Grep the Web, graph processing- SSSP, SSSP in mapreduce,

Pregel programming model , other big data programming models.

Unit 3

Cloud Resource Virtualization: basics of virtualization, types of virtualization techniques, merits and demerits of virtualization, full vs para-virtualization – virtual machine monitor/hypervisor - virtual machine basics, taxonomy of virtual machines, process vs system virtual machines – emulation: interpretation and binary translation – HLL, virtual machines , storage, desktop and application virtualization, applying virtualization.

Unit 4

Cloud Resource Management and Scheduling: Policies and mechanisms for resource management, Resource bundling, combinatorial , fair queuing, Start time fair queuing, borrowed virtual time, Cloud scheduling subject to deadlines,

Scheduling map reduce applications subject to deadlines, Resource management and application scaling.

Unit 5

Cloud Security : Risks, privacy and privacy impacts assessments, Multi-tenancy Issues , Security in VM, OS,

Virtualization system security issues and vulnerabilities, Virtualization System-Specific Attacks: Technologies for

Virtualization-Based Security Enhancement, Legal , Compliance Issues: Responsibility, ownership of data, right to penetration test, local law where data is held, examination of modern Security Standards (eg PCIDSS), how standards deal with cloud services and virtualization, compliance for the cloud provider vs. compliance for the customer.

References:

1.

Cloud Computing: Theory and Practice, Dan Marinescu, 1 st

edition, MK Publishers, 2013.

2.

Distributed and Cloud Computing, From Parallel Processing to the Internet of Things, Kai Hwang, Jack Dongarra,

Geoffrey Fox. MK Publishers.

3.

Cloud Computing: A Practical Approach, Anthony T. Velte, Toby J. Velte, Robert Elsenpeter, McGraw Fill, 2010

4.

Cloud computing A Hands on Approach Arshdeep Bahga,Vijay Madisetti Universities Publications

5.

Online Readings : http://www.pds.ewi.tudelft.nl/ , http://csrc.nist.gov/publications/nistpubs


Lecture Notes, Presentations, Demo/ Practicals

53



1.

To derive the best practice model for developing and deploying cloud based applications

2.

Develop cloud applications by integrating commercial cloud services as saas provider

3.

Propose an Enterprise/private cloud by designing own cloud architecture and provide Iaas

4.

Compose services in a distributed computing environment to achieve tasks relevant to a knowledge-based business or public service.

5.

Deploy programme using the APIs of Cloud Computing , create Virtual Machine images and to deploy them on a

Cloud.

6.

Evaluate the security issues related to multi-tenancy and Appraise compliance issues that arise from cloud computing

7.

Performance evaluations and critical evaluations of a small scale virtual environment developed in the lab.


Program Outcomes*

Course Outcomes

1 2 3 4 5 6 7 8 9 10 11 12

To derive the best practice model for developing and deploying cloud based applications

Develop cloud applications by integrating commercial cloud services as saas provider

Propose an Enterprise/private cloud by designing own cloud architecture and provide Iaas as service x x x x x x x x x x x x x

Compose services in a distributed computing environment to achieve tasks relevant to a knowledge-based business or public service.

Deploy programme using the APIs of Cloud Computing , create Virtual Machine images and to deploy them on a

Cloud.

Evaluate the security issues related to multi-tenancy and

Appraise compliance issues that arise from cloud computing

Performance evaluations and critical evaluations of a small scale virtual environment developed in the lab. x x x x x x x x x x x x x x x x x x

54

Course Title: Big Data and Data Science




Type of Course: Lecture, Practical Total Contact Hours: 70

Prerequisites: Nil


Unit 1

Introduction to Big Data and related technologies: Dawn of the Big Data Era, Definition and Features of Big

Data, Big Data Value, The Development of Big Data, Challenges of Big Data.

Cloud Computing, Relationship Between Cloud Computing and Big Data, IoT, IoT Preliminaries, Relationship Between

IoT and Big Data, Data Center, Hadoop Preliminaries, Relationship between Hadoop and Big Data

Unit 2

Big Data Acquisition and Analysis: Enterprise Data, IoT Data, Bio-medical Data, Data Generation from Other Fields.

Big Data Acquisition, Data Collection, Data Transportation, Data Pre-processing , Storage Mechanism for Big Data Big

Data Analysis: Traditional Data Analysis, Big Data Analytic Methods, Architecture for Big Data Analysis, Real-Time vs.

Offline Analysis, Analysis at Different Levels, Analysis with Different Complexity, Tools for Big Data Mining and

Analysis.

Unit 3

Big Data Analysis Using Hadoop: Hadoop: MapReduce Basics: Functional Programming Roots, Mappers and

Reducers, The Execution Framework, Partitioners and Combiners, The Distributed File System, Hadoop Cluster

Architecture. Graph Algorithms: Graph Representations, Parallel Breadth-First Search, PageRank, Issues with Graph

Processing.

Unit 4

Big data analysis using R: Introduction to R: Understanding datasets, Data structures: Vectors, Matrices, Arrays, Data frames, Factors, Lists Modeling Methods: Mapping problems to machine learning tasks : Solving classification problems, scoring Problems, Working without known targets, Problem-to-method mapping.

Unit 5

Big Data applications: Application Evolution, Big Data Analysis Fields, Structured Data Analysis, Text Data Analysis,

Web Data Analysis, Multimedia Data Analysis, Network Data Analysis, Mobile Traffic Analysis.

Key Applications, Application of Big Data in Enterprises, Application of IoT Based Big Data, Application of Online

Social Network-Oriented Big Data, Applications of Healthcare and Medical Big Data, Collective Intelligence, Smart Grid.

Reading Material: (In no particular order of precedence)

1.

Principles of Big Data: Preparing, Sharing and Analyzing Complex Information, Jules J Berman, First Edition,

MK Publishers, 2013.

2.

The Field Guide to Data Science:http://www.boozallen.com/media/file/TheFieldGuidetoDataScience.pdf

3.

Understanding Big Data: ftp://129.35.224.12/software/tw/Defining_Big_Data_through_3V_v.pdf

4.

Ghemawat et.al Google, MapReduce: Simplied Data Processing on Large Clusters http://static.googleusercontent.com/media/research.google.com/en//archive/mapreduceosdi04.pdf

5.

Hadoop Tutorial: http://developer.yahoo.com/hadoop/tutorial/

Text Books

1.

Chen, M., Mao, S., Zhang, Y., & Leung, V. C.“Big Data-Related Technologies, Challenges and Future

Prospect”s , 2014, Springer (Chapters: 1-6)

2.

Lin, J., & Dyer, C. “Data-intensive text processing with MapReduce”,2010, (Chapters: 2and 5)

3.

Robert I. Kabacoff, “R in Action”, 2011,(Chapter 2 Pages:21-32)

4.

Zumel, N., & Mount, J. “Practical data science with R”, 2014, (Chapters: 5, Pages 81-91 )

Reference Books

1.

O. R. Team. Big Data Now: Current Perspectives from O’Reilly Radar. O’Reilly Media, 2011.

2.

Adler, Joseph. R in a Nutshell, Second Edition. O’Reilly Media, 2012.

55

Course Delivery

The course will be delivered through lectures, presentations, classroom discussions, practice exercises and practical sessions.

Course Assessment and evaluation:

What

To

Whom

When/ Where


Max

Marks

Evidence

Collected

Contribution to Course

Outcomes

CIE

Internal

Assessment

Tests


25 Blue Books 1-5

SEE

Lab test

Semester

End

Examination

End of Course

Survey

Students

Once

End of Course

(Answering

5 of 10 questions)

End of the course

25

50

-

Data Sheets

Answer scripts

1,2,4-5

1-5

Questionnaire

1-5, Relevance of the course

Course Outcomes


1.

Review the features of Big Data and its types

2.

Identify the relationship of big data to Cloud Computing and IoT.

3.

Ilustrate different steps involved in acquisition of Big data, pre-processing

4.

Identify the different storage systems for Big data.

5.

Examine different types of analysis techniques involved in processing of Big Data.

6.

Illustrate different types of analysis through evaluation models for Big data.

7.

Estimate the need for Big data applications.



Course Outcomes

1 2 3 4 5 6 7 8 9 10 11 12

Review the features of Big Data and its types

Identify the relationship of big data to Cloud Computing and IoT.

Ilustrate different steps involved in acquisition of Big data, pre-processing x x x x x x x x x x x x x x x x x x Identify the different storage systems for Big data.

Examine different types of analysis techniques involved in processing of Big Data.

Illustrate different types of analysis through evaluation models for Big data.

Estimate the need for Big data applications. x x x x x x x x x x x x x x

56

Name & USN of the student:

Contact details:

Sl

No.

Question

1.

Quality of the course content

2.

For the number of credits, the course workload was

3.

Relevance of the textbook to this course

Ideas/Concepts that you have found difficult to

4.

grasp

Concepts/topics that should be removed from the

5.

syllabus

6.

New inclusions in the syllabus

7.

Were the lectures clear/well organized and presented at a reasonable pace?

8.

Did the lectures stimulate you intellectually?

9.

10.

What approaches/aids would facilitate your learning? You can check multiple options.

Did the problems worked out in the classroom help you to understand how to solve questions on your own?

11.

12.

Is the grading scheme clearly outlined and reasonable/fair?

Are the assignment/lab experiment procedures clearly explained?

13.

Attainment level of CO1

14.


15.


16.


17.

Attainment level of CO5....COn

Course Exit Survey Form

Dept of CSE, MSRIT, Bangalore

Excellent Very Good

Responses

Good

List

Course code:

Course name:

Satisfactory Poor

List

List

Yes/No

Yes/No

Lectures/ Programming Assignments/ Presentations/ Tutorials/ Demonstrations/ Practical Exercises/

Mini projects/ Group discussions/ Student seminars/ Expert guest lectures

Yes/No

Yes/No

Yes/No

Signature of the student with date

57

Outcomes Based Education Curricula (Academic Year 2015 – 2016)

M. S. RAMAIAH INSTITUTE OF TECHNOLOGY

(Autonomous Institute, Affiliated to VTU)

Computer Science and Engineering

Outcomes Based Education Curricula

(Academic Year 2015 – 2016)

VII & VIII Semester

History of the Institute

History of the Department of Computer Science

Faculty

Vision and Mission of the Institute

Vision

Mission

Quality Policy

Vision and Mission of the Department

Vision

Mission

Process for Defining the Vision and the Mission of the Department

Programme Educational Objectives (PEOs)

PEO Derivation Process

Programme Outcomes (POs)

PO Derivation Process

Mapping of PEOs and POs

Curriculum Breakdown Distribution

Board of Studies for the Term 2015-2016

Department Advisory Board for the term 2015-2016

Industry Advisory Board for the Term 2015-2016

Scheme of Studies for Fourth Year B.E. (CSE) for the batch 2011-2015

VII Semester

Code Subject

Total Credits: 25

L T P Credit

VIII Semester

Code

Subject

Total Credits: 24

L T P Credit

List of electives:

Elective Group III (3 Credits)

Elective Group IV (4 Credits)

Signature of the student with date

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