M.Phil. Programme in Computer Sciencesrnmcollege.ac.in/event_img/355d11968eb16df506a328b274f7ec35799fd739…Computer Science with not less than 55% of marks shall be eligible to register

SYLLABUS AND REGULATIONS

UNDER CHOICE BASED CREDIT SYSTEM (CBCS) (Those who joined in 2018-2019 and after)

M.Phil. Programme

in Computer Science

Regulations 2018

SRI S. RAMASAMY NAIDU MEMORIAL COLLEGE SATTUR- 626 203 (An Autonomous Institution Affiliated to Madurai Kamaraj University, Madurai)

(Re-Accredited with Grade ‘A’ by NAAC)

Placed at the meeting of Academic

Council held on 17.04.2018

SRNMC Regulations -2018 Syllabus

1

Objectives

The syllabus for M.Phil (Computer Science) under semester system has been

so designed that the students can have a clear idea on recent research developments in

the field of Computer Science and Information Technology.

The main objectives are:

To offer quality education and research for providing wide scope to conduct

substantial empirical research.

To upscale the quality of research education in a splendid and instrumental

manner.

To provide direction and guidance to enthusiastic graduate to convert their

creative ideas and plans into a successful career in research and development.

To enhance the knowledge and skill of students to meet the needs of innovative

industrial research and development projects and also providing future scope for

research oriented studies and work.

To get prior idea on preparing research articles and dissertation with the aid of

Software tools.

Eligibility

A Candidate who has obtained Master‟s Degree in Computer Science/

IT/Applications of Madurai Kamaraj University or of any other University

recognized by the Syndicate of Madurai Kamaraj University as equivalent there to

Computer Science with not less than 55% of marks shall be eligible to register for the

Degree of Master of Philosophy in Computer Science and the college shall admit

M.Phil students through an Entrance Test conducted by Madurai Kamaraj University.

The admissions will be made once in a year. The candidates for M.Phil shall be

admitted only in the regular (Full Time) mode and not in Part-time or distance

learning or any other mode.


2

Duration

The duration of the M.Phil course shall be of two semesters for the full time

programme.

Course of study

The course of study shall consist of

PART – I : Three Written Papers

PART – II : Dissertation.

The three papers under Part-I (First Semester) shall be:

Paper I : RESEARCH METHODOLOGY

Paper II : SOFT COMPUTING

Paper III : Optional Subject

LIST OF PAPERS

A DATA MINING AND DATA WAREHOUSING

B DIGITAL IMAGE PROCESSING

C MOBILE AD HOC NETWORKS

D BIG DATA ANALYTICS

E CLOUD COMPUTING

For Part-II (Second Semester):

Dissertation and Viva-voce


3

Scheme of Examination

First Semester

Subject Subject Code

Weekly

Contact

Hours

Library

Hours Credits

Exam

Hours

Marks

Int. Ext. Total

Paper I: Research

Methodology MP18CS11 6 4 5 3 25 75 100

Paper II:

Soft Computing MP18CS12 6 4 5 3 25 75 100

Paper III :

Optional Subjects

A. Data mining and

Data

Warehousing

MP18CSE11

6 4 5 3 25 75 100

B. Digital Image

Processing

MP18CSE12

C. Mobile Ad Hoc

Networks

MP18CSE13

D. Big Data

Analytics

MP18CSE14

E. Cloud

Computing

MP18CSE15

Second Semester

Subject

Subject

Codes Credits

Marks

Int. Ext. Total

Dissertation MP18CSDN 5 75 75 150

Viva voce MP18CSVV 5 - 50 50

Total 10 200


4

Pattern of the Question Paper

Part A

Five questions (either or type).

One questions from each unit. 5 x 6 = 30 Marks

Part B

Three questions out of five. 3 x 15 = 45 Marks

One question from each unit

-------------

TOTAL 75 Marks

-------------

Evaluation

1. Part I – Written papers

The performance of a scholar is evaluated in terms of percentage of marks.

Evaluation for each course shall be done by a Continuous Internal Assessment by the

concerned teacher as well as by an End Semester Examination of 3 hours duration

and will be consolidated at the end of the course. The ratio of the marks to be allotted

for Continuous Internal Assessment and End Semester Examination is 25:75.

a) Maximum marks for test 15 marks

(Two tests and their average)

b) Maximum marks for seminar 5 marks

Activities

c) Maximum marks for Assignment 5 marks

------------

Total 25 marks

------------


5

Passing Minimum

1. 50% of the aggregate (External+ Internal).

2. No separate pass minimum for internal.

3. 34 marks out of 75 is the pass minimum for the External.

2. Part II - Dissertation

To carry out the dissertation the mandatory requirement is strictly adhered to the

rules of the college as given below:

Requirement

Every student has to attend three reviews based on the following:

1. Problem Definition & Literature Survey

2. Implementation Techniques

3. Data Analysis & Result.

Submission

Every Candidate has to submit the Dissertations to the Controller of

Examinations within six months but not earlier than five months. The above said time

limit shall start from 1st of the month which follows after the month in which Part-I

Examination is conducted. If a candidate is not able to submit his/her Dissertation

within that period, he/she shall be given an extension of three months in the first

instance and another three months in the second instance with penalty. If a candidate

does not submit his Dissertation even after the two extensions, his registration shall

be treated as cancelled and he has to re-register for the course subject to the discretion

of the Principal. However, the candidate need not write once again the theory papers

if he / she has already passed these papers.

Every candidate has to publish their research work in a reputed journal on or

before the viva- voce.


6

Requirement for valuation of Dissertation

One External Examiner and the Research Adviser( Internal Examiner) have to

evaluate the Dissertation. The external examiner should be selected only from outside

the college and shall be within the colleges affiliated to Madurai Kamaraj University.

In case of non-availability, the panel can include examiners from other university /

colleges in Tamil Nadu. The external examiner shall be selected from a panel of

THREE experts suggested by the Research Adviser. However, the Controller of

Examinations may ask for another panel if he deems it necessary. Internal Evaluation

will be done by the Research Adviser and the External Dissertation evaluation is done

by the External Examiner. The viva-voce will be done by both of them.

Viva-voce

Both the External Examiner and the Research Adviser shall conduct the Viva-

Voce Examination for the candidate. A Candidate shall be declared to have passed in

the viva-voce if he secures not less than 50% of the maximum marks prescribed for

viva-voce test. A student can undertake project in the second semester whether or not

he /she has passed the first semester.


7

Internal Evaluation

External Evaluation

Dissertation Marks

Problem Definition & Literature

Review 30

Journal Publication 10

Dissertation Evaluation 35

Total 75

Marks

Dissertation Evaluation 75

Viva – Voce 50

Total 125


8

SRI S.RAMASAMY NAIDU MEMORIAL COLLEGE (An Autonomous Institution Re-accredited with „A‟ Grade by NAAC)

SATTUR - 626 203.

Department of Computer Science

(For those who are joining in 2018–2019 and after)

SYLLABUS

Programme: M.Phil (Computer Science) Subject Code : MP18CS11

Semester : I No. of Hours allotted : 6/Week

Subject : Core Paper I No. of Credits : 5

Title of the Paper: RESEARCH METHODOLOGY

Objectives:

To gain insights into how scientific research is conducted.

To help in critical review of literature and assessing the research trends, quality and

extension potential of research and equip students to undertake research.

To help in documentation of research results.

To incalculable knowledge on Data Structure concepts.

Unit I: RESEARCH METHODOLOGY AN INTRODUCTION

Meaning of Research: Objectives of Research – Type of Research – Research

Approaches – Significance of Research – Research Methods versus Methodology – Research

and Scientific Method – Research Process – Criteria of Good Research.

Defining the Research Problem: What is a Research Problem? - Selecting the

Problem – Necessity of Defining the Problem – Technique Involved in Defining a Problem.

Research Design: Meaning of Research Design – Need for Research Design –

Features of a Good Design – Important Concept Relating to Research Design – Different

Research Design – Basic Principle of Experimental Designs.

Unit II: RESEARCH DESIGN & DATA COLLECTION

Data Collection: Introduction – Experimental and Surveys – Collection of Primary

Data – Collection of Secondary Data – Selection of Appropriate Method for Data Collection


9

Data Preparation: Data Preparation Process – Missing Values and Outliers – Types

of Analysis – Statistics in Research.

Descriptive Statistics: Measures of Central Tendency – Measures of Dispersion –

Measures of Skewness – Kurtosis – Measures of Relationship – Association in case of

Attributes – Other measures.

Unit III: REPORT WRITING

Sampling and statistical Inference : Parameter and Statistic –Sampling and Non-

sampling Errors – Sampling Distribution –Degree of Freedom – Standard Error – Central

Limit Theorem- Finite Population Correction – Statistical Inference.

Interpretation and Report Writing: Meaning of Interpretation – Techniques of

Interpretation – Precaution in Interpretation – Significance of Report – Different Steps in

Writing Report – Layout of the Research Report – Types of Reports – Oral Presentation –

Mechanics of Writing a Research Report – Precautions for Writing Research Reports -

Conclusion

Unit IV: DATA STRUCTURES

Linked Lists – Definition-Single linked list-Circular linked list-Double linked lists –

Applications of Linked Lists- Polynomial Representation.

Tables – Rectangular Tables – jagged Tables – Inverted Tables –Hash tables.

Unit V: TREES & GRAPHS

Basic Terminologies – Definition and concepts – Representations of Binary Tree –

Operations on Binary tree – Types of Binary trees – Expression Tree – Binary Search Tree –

Heap Trees.

Graphs: Introduction – Graph Terminologies – Representation of Graphs–

Applications of Graph structures – Shortest path problem – Topological sorting – Minimum

Spanning Trees.

CASE STUDY:

Implementation of Research Techniques using SPSS tool.


10

Text Books:

1. C.R. Kothari, “Research Methodology Methods and Techniques”, New Age

International Publishers, Third Edition, 2014.

(Unit I, II & III)

2. Debasis Samanta “Classic Data Structures”, Second edition, 2008, PHI.

(Unit IV&V)

Reference Books:

1. R.Panneerselvam, “Design and Analysis of Experiments”, PHI Learning Private

Limited, 2012.

2. R.Panneerselvam, “Research Methodology”, PHI Learning Private Limited, 2014.

3. Alfred V Aho, John E Hopcroft, “Design & Analysis of Computer Algorithms”,

Pearson Education, 2002.

4. A.A.Puntambekar, “Design & Analysis of Computer Algorithms”, Technical

Publications, 2010.

5. Michael T. Goodrich and Roberto Tamassia, “Algorithm Design - Foundations,

Analysis & Internet Examples”, Wiley, 2002.

6. Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein,

“Introduction to Algorithms”, MIT Press, 2001.

Prepared By : Dr. A. RANICHITRA

Signature :

http://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22Alfred+V+Aho%22

http://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22John+E+Hopcroft%22

http://www.cs.brown.edu/~rt/

http://algorithmdesign.net/



http://supertech.lcs.mit.edu/~cel/

http://theory.lcs.mit.edu/~rivest/

http://www.cs.dartmouth.edu/~cliff/

http://theory.lcs.mit.edu/~clr/


11

SRI S. RAMASAMY NAIDU MEMORIAL COLLEGE

(An Autonomous Institution Re-accredited with „A‟ Grade by NAAC)

SATTUR - 626 203.



SYLLABUS

Programme : M.Phil (Computer Science) Subject Code : MP18CS12


Subject : Core Paper II No. of Credits : 5

Title of the Paper: SOFT COMPUTING

Objectives:

To familiarize with soft computing concepts.

To introduce the ideas of Neural Networks, Fuzzy Logic and use of heuristics

based on human experience.

To introduce the concepts of Genetic algorithm and its applications to soft

computing using some applications.

To impart the knowledge in Fuzzy Fundamentals.

To introduce some of the fundamental techniques and principles of neural

network systems and investigate some common models and their applications.

UNIT – I: FUZZY LOGIC

Introduction - Classical Sets - Fuzzy Sets –classical relations and Fuzzy relations -

crisp relations - fuzzy relations - Fuzzy tolerance and Equivalence relations.

UNIT – II:

Properties of Membership functions - Fuzzification – Defuzzification - Fuzzy Systems-

Development of Member ship functions.


12

UNIT – III: NEURAL NETWORKS

Introduction - Artificial Neural Networks - Historical developments of Neural

Networks – Biological Neural Networks –Comparison between the Brain and the Computer–

Basic Building blocks of Artificial Neural Networks- Artificial Neural Networks

Terminology-Fundamental Models of Artificial Neural Networks- Mc-Culloch-Pitts Neuron

Model- Learning Rules- Hebb Net-Perceptron Network- Back Propagation Network (BPN).

UNIT – IV: ARTIFICIAL INTELLIGENCE AND EXPERT SYSTEMS

Introduction : Problem Definition – Search Strategies – Characteristics – Game

Playing - Knowledge representation – Expert System – Roles of Expert System – Knowledge

acquisition, Meta knowledge – Heuristics knowledge – Interface : Backward and forward

chaining – Fuzzy reasoning – Learning – Adaptive Learning – Types of Expert System :

MYSIN, PIP, INTERNIST, DART, XOON, Expert Systems Shells.

UNIT – V: GENETIC ALGORITHM

Introduction – Basic Operators and Terminologies in GAs – Traditional Algorithm vs.

Genetic Algorithm – Simple GA – General Genetic Algorithm – The Schema Theorem –

Classification of Genetic Algorithm – Holland Classifier Systems – Genetic Programming –

Application of Genetic Algorithm.

Case Study: To apply research tools MATLAB and R Programming to implement the Soft

Computing techniques.

TEXT BOOKS:

1. Timothy J. Ross, ”Fuzzy Logic with Engineering Applications”,John Wiley & Sons,

Third Edition 2010.

2. S. N. Sivanandam, S. Sumathi, S.N. Deepa, “Introduction to Neural Networks using

MATLAB 6.0“, Tata McGraw-Hill, New Delhi, 2006.

3. Elaine Rich and Kevin Knight, “Artificial Intelligence”, McGraw-Hill, Second Edition,

1991.

http://www.google.co.in/search?tbo=p&tbm=bks&q=inauthor:%22Timothy+J.+Ross%22


13

4. Nildon, N.J. Springer Verlag, “Principles of Artificial Intelligence”, Morgan

Kaufmann Publishers, 1980.

5. S. N. Sivanandam, S.N. Deepa, “Principles of Soft Computing”, Wiley-India, 2008.

REFERENCE BOOKS:

1. Satish Kumar, “Neural Networks – A Classroom approach”, Tata McGraw-Hill, New

Delhi, 2007.

2. Martin T. Hagan, Howard B. Demuth, Mark Beale, “Neural Network Design”,

Thomson Learning, India, 2002.

3. B. Kosko, “Neural Network and fuzzy systems”, PHI Publications, 1996.

4. Klir & Yuan, “Fuzzy sets and fuzzy logic – theory and applications”, PHI Publications,

1996.

5. Melanie Mitchell, “An introduction to genetic algorithm”, PHI Publications, India,

1996.

6. David E.Goldberg, “Genetic Algorithms in Search Optimization and Machine

Learning”, Pearson Education, 2007.

7. N.P. Padhy, “Artificial Intelligence and Intelligent Systems”, Oxford University Press,

2005. Yegnanarayana, “ArtificialNeuralNetworks”, PHI Publications, 2008

8. Melanie Mitchell, “An Introduction to Genetic Algorithms”, MIT Press, First Edition.

1998.

Prepared By : Dr. K. KRISHNAVENI

Signature :


14

SRI S.RAMASAMY NAIDU MEMORIAL COLLEGE


SATTUR-626 203.



SYLLABUS

Programme: M.Phil (Computer Science) Subject Code : MP18CSE11


Subject : Optional - Paper III - A No. of Credits : 5

Title of the Paper: DATA MINING AND DATA WAREHOUSING

Objectives:

To analyze, design, develop and evaluate high-end computing systems.

To introduce the concepts and techniques of Data Mining.

To develop skills of using recent data mining software for solving practical problems

and Data Ware Housing

To gain experience of doing independent study and research in Data Mining.

To identify new trends and evaluate emerging technologies

UNIT- I INTRODUCTION

Why Data Mining? What is Data Mining? – What Kinds of Data can be mined? –

What Kind of Patterns Can Be Mined? – Which Technologies are used?– Which kinds of

Applications are Targeted? – Major issues in Data Mining.

Basic statistical Descriptions of Data – Data Visualization – Measuring Data

Similarity and Dissimilarity.

UNIT – II DATA PREPROCESSING

Data preprocessing : An overview – Data Cleaning – Data Integration – Data

Reduction – Data Transformation and Data Discretization.


15

UNIT - III DATA WAREHOUSE AND OLAP TECHNOLOGY

An Overview : Data Warehouse : Basic Concepts – data warehouse Modeling : Data

Cube and OLAP

ASSOCIATION RULE MINING : Introduction-Basics-The Task and a Naïve

Algorithm-The Apriori Algorithm - Improving the Efficiency of the Apriori Algorithm -

Apriori - TID

UNIT - IV CLASSIFICATION AND CLUSTER ANALYSIS

Preliminaries-General approach to solving the classification problem - Decision tree

Induction - Rule Based Classifier - Nearest Neighbour Classifier - Bayesian classifier

What is cluster analysis? - Desired features of Cluster Analysis - Types of Data-

Computing Distance - Types of cluster analysis methods- Partitional Methods - Hierarchical

Methods

UNIT-V WEB DATA MINING, DATA MINING TREANDS AND RESEARCH FRONTIERS

Web Data Mining: Introduction – Web Terminology and Characteristics –

Locality and Hierarchy in the web – Web Content Mining – Web Usage Mining – Web

Structure Mining

Mining Complex Data types – Other Methodologies of Data Mining – Data Mining

Applications – Data Mining and Society.

CASE STUDY:

Implementation of Data Mining Algorithms using Weka Tool / R Programming

TEXT BOOKS:

1. Jiawei Han and Micheline Kamber, “Data Mining: Concepts and Techniques”, Morgan

Kaufman Publishers (Elsevier Science), Third Edition,2012.

2. G.K.Gupta, “Introduction to Data Mining with Case Studies”, PHI Publications, 2014

3. Pang-Ning Tan, Michael Steinbach , Vipin Kumar, “Introduction to Data mining”,

Pearson Education, 2007


16

REFERENCE BOOKS:

1. Rajan Chattamvelli, “Data Mining Methods”, Narosa Publishers, Second Edition, 2010

2. K.P.Soman, Shyam Diwakar V.Ajay, “Data Mining Theory and Practice” PHI

3. Gopalan and Sivaselvam “Data Mining techniques and Trends”, PHI 2010

4. Agarwal, Jayant,Ramkumar singh and Amit, “ Data Mining and Data Warehousing”

International book house, 2014

5. Shmueli and Galit “Data Mining for Business Intelligence”, Widley Easter limited,

2010

6. Soumendra Mohanty “The Data warehousing”, Mc graw hill, 2006

7. Thareja, Reema “Data warehousing”, Oxford University press, 2013

8. D.Jannach, M Zanker, AFelfenig, G Friedrich, “Recommender Systems an

Introduction”, Cambridge, First Edition, 2011.

Prepared By : Dr. K. ARUNESH

Signature :


17



SATTUR-626 203.


(For those who are joining in 2018-2019 and after)

SYLLABUS

Programme : M.Phil (Computer Science) Subject Code : MP18CSE12


Subject : Optional Subject-Paper III-B No. of Credits : 5

Title of the Paper: DIGITAL IMAGE PROCESSING

Objectives:

To learn Digital Image fundamentals.

To evaluate the techniques for image enhancement and image restoration.

To categorize various compression techniques.

To interpret Image compression standards.

To interpret image segmentation and representation techniques.

To have knowledge on Color Models

UNIT-I

FUNDAMENTALS OF IMAGE PROCESSING

Introduction – Digital Image Processing – The origins of Digital Image Processing –

Fundamental Steps in Digital Image Processing Systems – Components of an Image

Processing System

Digital Image Fundamentals -Image Acquisition – Image Sampling and Quantization –

Basic Relationships between pixels – Image operations – Arithmetic operations - set and

Logical operations.


18

UNIT-II: IMAGE ENHANCEMENT

Intensity Transformation Functions - Histogram processing - Fundamentals of Spatial

Filtering - Smoothing Spatial Filters - Sharpening Spatial Filters- Image smoothing using

frequency domain Filters- Ideal Low Pass Filters- Butterworth Low Pass Filters - Gaussian

Low Pass Filters Image Sharpening using frequency domain Filters – Ideal High Pass Filters-

Gaussian High Pass Filters.

UNIT-III:

Image Restoration : A model of the Image Degradation/ Restoration Process - Noise

Models - mean filters - Order Static Filters - Band Reject Filters – Band pass Filters - Notch

Filters - Inverse Filtering- Minimum mean Square(Wiener) Filtering.

Color Image Processing: Color Fundamentals - Color Models – Pseudo color Image

Processing - Color Transformations - Color Image Smoothing - Color Image Sharpening.

UNIT-IV: IMAGE COMPRESSION

Fundamentals - Image compression Methods – Huffman Coding, Arithmetic Coding,

Run Length Coding, Bit-plane Coding - Block Transform Coding - Digital Image

Watermarking.

Morphological Image Processing: Morphological Operations – Algorithms

UNIT-V: IMAGE SEGMENTATION

Fundamentals-Point, Line and Edge detection – Detection of isolated points-Line

detection – edge Models – Image Gradient and its Properties - Thresholding – The basics of

Intensity Thresholding - Global Thresholding – Multiple Thresholds - Region based

segmentation – Region growing – Region splitting and merging – Segmentation using

morphological watersheds – basic concepts – Dam construction – Watershed segmentation

algorithm.

Case Study: Implementation of Digital Image Processing Techniques using MATLAB


19

TEXT BOOKS:

1. Rafael C. Gonzalez, Richard E. Woods, „Digital Image Processing‟, Pearson, Third

Edition, 2004.

REFERENCES BOOKS:

1. Kenneth R. Castleman, Digital Image Processing, Pearson, 2006.

2. Rafael C. Gonzalez, Richard E. Woods, Steven Eddins,' Digital Image Processing

using MATLAB', Pearson Education, Inc., 2004.

3. D.E. Dudgeon and RM. Mersereau, Multidimensional Digital Signal Processing,

Prentice Hall Professional Technical Reference, 1990.

4. William K. Pratt, , Digital Image Processing' , John Wiley, New York, 2002


Signature :


20



SATTUR-626 203.



SYLLABUS



Subject : Optional Subject-Paper III-C No. of Credits : 5

Title of the Paper: MOBILE AD HOC NETWORKS

Objectives:

To gain insights into what is Wireless Networks.

To Learn and understand the basis of Ad hoc Networks.

To understand the concepts and working principles of routing protocols in Ad hoc

Wireless networks.

To understand the Multi cast routing in Ad hoc networks.

To identify the mechanism to provide security to the protocol.

To gain knowledge about Key Management Approaches in Securing the Ad Hoc

Networks.

To understand the Method used to provide QOS to the Networks.

To learn the basis of Sensor Networks.

Unit I : WIRELESS INTERNET

Introduction - Characteristics of the Wireless Channel - Wireless LANS – Introduction

-Fundamentals of WLANS - IEEE 802.11 standard – Physical Layer - Basic MAC Layer

Mechanism - CSMA/CA Mechanism.

Wireless Internet: Introduction –What is Wireless Internet? - Mobile IP - Mobile IP –

Simultaneous Bindings - Route Optimization - Mobile IP Variations – The 4 X 4Approach –

Handoffs- IPV6 Advancements - IP for Wireless Domains - Security in Mobile IP.


21

Unit II: ROUTING PROTOCOLS IN AD HOC NETWORKS

Ad Hoc Wireless Networks: Introduction - Cellular and Ad hoc Wireless Networks-

Applications of Ad hoc Wireless Networks-Issues in Ad hoc Wireless Networks-Ad hoc

Wireless Internet.

Routing Protocols for Ad hoc Wireless Networks: Introduction - Issues in designing a

routing Protocol for Ad hoc Wireless Networks - Classifications of Routing Protocols -

Table-driven Routing Protocols - DSDV Routing Protocol - CGSR Protocol - On Demand

Routing Protocols - DSR Protocol – AODV Routing Protocol – Hybrid Routing Protocols –

ZRP.

Unit III: MULTICAST ROUTING AND TRANSPORT LAYER FOR AD HOC

WIRELESS NETWORKS.

Multicast Routing in Ad hoc Wireless Networks: Introduction- Issues in designing a

Multicast Routing Protocol-Operation of Multicast Routing Protocol-An Architecture

Reference model for multicast Routing protocols-Classification of Multicast Routing

Protocols- Tree-Based Multicast Routing Protocols-Multicast Ad Hoc On-Demand Distance

Vector Routing Protocols-Mesh Based Multicast Routing Protocols-On-Demand Multicast

Routing Protocol.

Introduction - Issues in Designing a Transport layer protocol for Ad hoc Wireless

Network – Design goals for a Transport layer protocol for Ad hoc Wireless Network –

Classification of Transport Layer Solutions – TCP over Ad hoc Wireless networks.

Unit IV: SECURITY PROTOCOLS AND QUALITY OF SERVICE IN AD HOC

WIRELESS NETWORKS

Security in Ad hoc Wireless Networks – Network Security Requirements – Issues and

Challenges in security Provisioning- Network Security attacks – Key Management- Secure

Routing in Ad Hoc Wireless Networks.

Introduction- Issues and Challenges in Providing QOS in Ad Hoc Wireless Networks –

Classifications of QOS Solutions – MAC Layer Solutions – DBASE.

Unit V: WIRELESS SENSOR NETWORKS

Introduction – Sensor Network Architecture – Data Dissemination –Data Gathering-

MAC Protocols for Sensor Networks – Location Discovery – Quality of a Sensor Networks-

Evolving Standards – Other Issues.


22

CASE STUDY:

Implementation of Ad hoc Routing Protocols using NS2

Text Books:

1. C.Sivarama Muruthy and B.Manoj,” Ad hoc Wireless Networks, Architectures and

Protocols”, Prentice Hall Pearson Education Inc. 2004 Editing.

Reference Books:

1. Prasant Mohapatra,Srikanth and Krishnamoorthy “Ad Hoc Networks:Technologies

and Protocols”, Springer, 2005

2. William Stallings “Cryptography and Network Security Principles and Practices”,

Pearson Education , 2008

Prepared By : Dr. A. RANICHITRA

Signature :


23



SATTUR-626 203.



SYLLABUS



Subject : Optional Subject-Paper III-D No. of Credits : 5

Title of the Paper: BIG DATA ANALYTICS

Objectives:

• Understand the Big Data Platform and its Use cases.

• Provide an overview of Apache Hadoop.

• Provide HDFS Concepts and Interfacing with HDFS.

• Understand Map Reduce Jobs.

• Provide hands on Hodoop Eco System.

• Apply analytics on Structured, Unstructured Data.

• Exposure to Data Analytics with R.

• Apply Machine Learning Techniques using R.

UNIT I : INTRODUCTION TO BIG DATA AND HADOOP

Types of Digital Data, Introduction to Big Data, Big Data Analytics, History of

Hadoop, Apache Hadoop, Analysing Data with Unix tools, Analysing Data with Hadoop,

Hadoop Streaming, Hadoop Echo System, IBM Big Data Strategy, Introduction to

Infosphere BigInsights and Big Sheets.

UNIT II : HDFS(Hadoop Distributed File System)

The Design of HDFS, HDFS Concepts, Command Line Interface, Hadoop file system

interfaces, Data flow, Data Ingest with Flume and Scoop and Hadoop archives, Hadoop I/O:

Compression, Serialization, Avro and File-Based Data structures.


24

UNIT III : MAP REDUCE

Anatomy of a Map Reduce Job Run, Failures, Job Scheduling, Shuffle and Sort, Task

Execution, Map Reduce Types and Formats, Map Reduce Features.

Unit IV : HADOOP ECO SYSTEM

Pig : Introduction to BIG, Execution Modes of Pig, Comparison of Pig with Databases,

Grunt, Pig Latin, User Defined Functions, Data Processing operators.

Hive : Hive Shell, Hive Services, Hive Metastore, Comparison with Traditional

Databases, HiveQL, Tables, Querying Data and User Defined Functions.

Hbase : HBasics, Concepts, Clients, Example, Hbase Versus RDBMS. Big SQL :

Introduction

UNIT V : DATA ANALYTICS WITH R

Machine Learning : Introduction, Supervised Learning, Unsupervised Learning,

Collaborative Filtering. Big Data Analytics with BigR.

CASE STUDY:

Implementation of Big Data using R - tool.

Text Books

1. Tom White “ Hadoop: The Definitive Guide” Third Edition, O‟reily Media, 2012.

2. Seema Acharya, Subhasini Chellappan, "Big Data Analytics " Wiley 2015.

References

1. Michael Berthold, David J. Hand, "Intelligent Data Analysis”, Springer, 2007.

2. Jay Liebowitz, “Big Data and Business Analytics” Auerbach Publications, CRC

press, 2013

3. Tom Plunkett, Mark Hornick, “Using R to Unlock the Value of Big Data: Big Data

Analytics with Oracle R Enterprise and Oracle R Connector for Hadoop”, McGraw-

Hill/Osborne Media 2013, Oracle press.


25

4. Anand Rajaraman and Jef rey David Ulman, “Mining of Massive Datasets”,

Cambridge University Press, 2012.

5. Bill Franks, “Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data

Streams with Advanced Analytics”, John Wiley & sons, 2012.

6. Glen J. Myat, “Making Sense of Data”, John Wiley & Sons, 2007

7. Pete Warden, “Big Data Glossary”, O‟Reily, 2011.

8. Michael Mineli, Michele Chambers, Ambiga Dhiraj, "Big Data, Big Analytics:

Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley

Publications, 2013.

9. Arvind Sathi, “Big Data Analytics: Disruptive Technologies for Changing the

Game”, MC Press, 2012

10. Paul Zikopoulos ,Dirk DeRoos , Krishnan Parasuraman , Thomas Deutsch , James

Giles , David Corigan , "Harness the Power of Big Data The IBM Big Data

Platform", Tata McGraw Hill Publications, 2012.

Prepared By : Dr. K. ARUNESH

Signature :


26



SATTUR-626 203.



SYLLABUS



Subject : Optional Subject-Paper III-E No. of Credits : 5

Title of the Paper: Cloud Computing

OBJECTIVES:

The student should be made to:

Understand how Grid computing helps in solving large scale scientific problems.

Gain knowledge on the concept of virtualization that is fundamental to cloud

computing.

Learn how to program the grid and the cloud.

Understand the security issues in the grid and the cloud environment.

UNIT I INTRODUCTION

Evolution of Distributed computing: Scalable computing over the Internet –

Technologies for network based systems – clusters of cooperative computers - Grid

computing Infrastructures – cloud computing - service oriented architecture – Introduction to

Grid Architecture and standards – Elements of Grid – Overview of Grid Architecture.

UNIT II GRID SERVICES

Introduction to Open Grid Services Architecture (OGSA) – Motivation – Functionality

Requirements – Practical & Detailed view of OGSA/OGSI – Data intensive grid service

models – OGSA services.


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UNIT III VIRTUALIZATION

Cloud deployment models: public, private, hybrid, community – Categories of cloud

computing: Everything as a service: Infrastructure, platform, software - Pros and Cons of

cloud computing – Implementation levels of virtualization – virtualization structure –

virtualization of CPU, Memory and I/O devices – virtual clusters and Resource Management

– Virtualization for data center automation.

UNIT IV PROGRAMMING MODEL

Open source grid middleware packages – Globus Toolkit (GT4) Architecture ,

Configuration – Usage of Globus – Main components and Programming model -

Introduction to Hadoop Framework - Mapreduce, Input splitting, map and reduce functions,

specifying input and output parameters, configuring and running a job – Design of Hadoop

file system, HDFS concepts, command line and java interface, dataflow of File read & File

write.

UNIT V SECURITY

Trust models for Grid security environment – Authentication and Authorization

methods – Grid security infrastructure – Cloud Infrastructure security: network, host and

application level – aspects of data security, provider data and its security, Identity and access

management architecture, IAM practices in the cloud, SaaS, PaaS, IaaS availability in the

cloud, Key privacy issues in the cloud.

Case study: Implementation of cloud computing services and methods using the tools Hadoop,

aneka, etc.

TEXT BOOK:

1. Kai Hwang, Geoffery C. Fox and Jack J. Dongarra, “Distributed and Cloud Computing:

Clusters, Grids, Clouds and the Future of Internet”, First Edition, Morgan Kaufman

Publisher, an Imprint of Elsevier, 2012.


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REFERENCES:

1. Jason Venner, “Pro Hadoop- Build Scalable, Distributed Applications in the Cloud”, A

Press, 2009

2. Tom White, “Hadoop The Definitive Guide”, First Edition. O‟Reilly, 2009.

3. Bart Jacob (Editor), “Introduction to Grid Computing”, IBM Red Books, Vervante,

2005

4. Ian Foster, Carl Kesselman, “The Grid: Blueprint for a New Computing

Infrastructure”, 2nd

Edition, Morgan Kaufmann.

5. Frederic Magoules and Jie Pan, “Introduction to Grid Computing” CRC Press, 2009.

6. Daniel Minoli, “A Networking Approach to Grid Computing”, John Wiley Publication,

2005.

7. Barry Wilkinson, “Grid Computing: Techniques and Applications”, Chapman and

Hall, CRC, Taylor and Francis Group, 2010.

Prepared by : Dr. K. KRISHNAVENI

Signature :


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SATTUR - 626 203.



Programme : M.Phil (Computer Science) Subject Code : MP18CSDN

Semester : II

Part II : DISSERTATION No. of Credits : 10

Objectives:

To get clear idea about the emerging research trends in Computer Science / Information

Technology.

To produce innovative ideas and to develop those ideas into full-fledged research

results.

To provide an opportunity to carry out the research projects with strong analytical and

synthesizing capability with innovative and creative thinking to build a strong scientific

community.

Able to make original scientific contributions that have both practical significance and

a rigorous, elegant theoretical background that underpins various areas in Computer

Science and Information Technology.

To develop the ability to apply theoretical and practical tools / techniques to solve real

life problems related to industry, academic institutions and research laboratories.

To develop the ability of students to prepare the documentation of the Dissertation.

Regulations for the Dissertation

The topic of the dissertation must be on recent trends in Computer Science /IT/

Applications selected from recent reputed National/International Journal or

Conference.

The methods and techniques applied in the execution of the work should be

appropriate to the subject matter and should be original and aesthetically effective.


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Every scholar has to attend three reviews:

a) Problem Definition & Literature Survey

b) Implementation Techniques

c) Data Analysis & Result.

All candidates are required to make a presentation of their research findings

objectives, methods, findings and significance of his/her research work prior to the

Viva-voce examination.

Every candidate has to publish their research work in a reputed journal on or before

the viva- voce.

A candidate should not include the reprints or journal articles in their published form

as part of the body of the dissertation.

The documentation of the work (including catalogue/ program material where

appropriate) should be sufficiently thorough with a standard that will provide a

reference for subsequent researchers.

The students should submit three copies of dissertation with hard binding for

evaluation.

The number of pages in the Dissertation may be from 100 to 150.

The Format of the Dissertation may be as per the structure given by the Department.


Signature :

Chairman Dean – Academic

Documents

M.Phil. Programme in Computer Sciencesrnmcollege.ac.in/event_img/355d11968eb16df506a328b274f7ec35799fd739…Computer Science with not less than 55% of marks shall be eligible to register