IT6006 Data Analytics Syllabus Semester VII Elective IT BTECH Anna University-Regulation-2013

NotesKhan




IT6006                                                  DATA ANALYTICS                                                        L T P C
3 0 0 3
OBJECTIVES:
The Student should be made to:

• Be exposed to big data
• Learn the different ways of Data Analysis
• Be familiar  with data streams
• Learn the mining and clustering
• Be familiar with the visualization

UNIT I             INTRODUCTION TO BIG DATA                                                                                    8
Introduction to Big Data Platform – Challenges of conventional systems -  Web data – Evolution of Analytic scalability, analytic processes and tools, Analysis vs reporting - Modern data analytic tools, Stastical concepts: Sampling distributions, resampling, statistical inference, prediction error.

UNIT II            DATA ANALYSIS                                                                                                        12
Regression modeling, Multivariate analysis, Bayesian modeling, inference and Bayesian networks, Support  vector  and kernel  methods,  Analysis  of  time  series:  linear  systems  analysis,  nonlinear dynamics - Rule induction - Neural networks: learning and generalization, competitive learning, principal component analysis and neural networks; Fuzzy logic: extracting fuzzy models from data, fuzzy decision trees, Stochastic search methods.

UNIT III           MINING DATA STREAMS                                                                                             8
Introduction to Streams Concepts – Stream data model and architecture - Stream Computing, Sampling data in a stream – Filtering streams – Counting distinct elements in a stream – Estimating moments – Counting oneness in a window – Decaying window - Realtime Analytics Platform(RTAP) applications -  case studies - real time sentiment analysis, stock market predictions.

UNIT IV          FREQUENT ITEMSETS AND CLUSTERING                                                                9
Mining Frequent itemsets - Market based model – Apriori Algorithm – Handling large data sets in Main memory – Limited Pass algorithm – Counting frequent itemsets in a stream – Clustering Techniques – Hierarchical – K- Means – Clustering high dimensional data – CLIQUE and PROCLUS – Frequent pattern based clustering methods – Clustering in non-euclidean space – Clustering for streams and Parallelism.

UNIT V          FRAMEWORKS AND VISUALIZATION                                                                         8
MapReduce – Hadoop, Hive, MapR – Sharding – NoSQL Databases - S3 - Hadoop Distributed file systems – Visualizations - Visual data analysis techniques, interaction techniques; Systems and applications:


OUTCOMES:
The student should be made to:

• Apply the  statistical analysis methods.
• Compare and contrast various soft computing frameworks.
• Design distributed file systems.
• Apply Stream data model.
• Use Visualisation techniques

TOTAL: 45 PERIODS


TEXT BOOKS:

1. Michael Berthold, David J. Hand, Intelligent Data Analysis, Springer, 2007.
2. Anand Rajaraman and Jeffrey David Ullman, Mining of Massive Datasets,Cambridge University

Press, 2012.

REFERENCES:

1. Bill Franks, Taming the Big Data Tidal Wave: Finding Opportunities in Huge Data Streams with advanced analystics, John Wiley & sons, 2012.
2. Glenn J. Myatt, Making Sense of Data, John Wiley & Sons, 2007  Pete Warden, Big Data

Glossary, O?Reilly, 2011.

3. Jiawei Han, Micheline Kamber “Data Mining Concepts and Techniques”, Second Edition, Elsevier,

Reprinted 2008.

CS6003 Ad hoc and Sensor Networks Syllabus Semester VII Elective IT BTECH Anna University-Regulation-2013

NotesKhan

CS6003	AD HOC AND SENSOR NETWORKS	L	T  P	C
		3	0  0	3

OBJECTIVES:
The student should be made to:

• Understand the desig n issues in ad hoc and sensor networks.
• Learn the different types of MAC protocols.
• Be familiar with different types of adhoc routing protocols.
• Be expose t o t he T CP issues in adhoc networks.
• L e a r n the architecture and protocols of wireless sensor networks..

UNIT I             INTRODUCTION                                                                                                             9
Fundamentals of Wireless Communication Technology – The Electromagnetic Spectrum – Radio propagation Mechanisms – Characteristics of the Wireless Channel -mobile ad hoc networks (MANETs) and wireless sensor networks (WSNs) :concepts and architectures. Applications of Ad Hoc and Sensor networks. Design Challenges in Ad hoc and Sensor Networks.

UNIT II          MAC PROTOCOLS FOR AD HOC WIRELESS NETWORKS                                        9
Issues in designing a MAC Protocol- Classification of MAC Protocols- Contention based protocols- Contention based protocols with Reservation Mechanisms- Contention based protocols with Scheduling Mechanisms – Multi channel MAC-IEEE 802.11

UNIT III        ROUTING PROTOCOLS AND TRANSPORT LAYER IN
AD HOC WIRELESS NETWORKS                                                                                   9
Issues in designing a routing and Transport Layer protocol for Ad hoc networks- proactive routing, reactive routing (on-demand), hybrid routing- Classification of Transport Layer solutions-TCP over
Ad hoc wireless Networks.

UNIT IV       WIRELESS SENSOR NETWORKS (WSNS) AND MAC PROTOCOLS                          9 single node architecture: hardware and software components of a sensor node - WSN Network architecture: typical network architectures-data relaying and aggregation strategies -MAC layer protocols: self-organizing, Hybrid TDMA/FDMA and CSMA based MAC- IEEE 802.15.4.

UNIT V        WSN ROUTING, LOCALIZATION & QOS                                                                        9
Issues in WSN routing – OLSR- Localization – Indoor and Sensor Network Localization-absolute and relative localization, triangulation-QOS in WSN-Energy Efficient Design-Synchronization-Transport Layer issues.


OUTCOMES:
Upon completion of the course, the student should be able to:

TOTAL: 45 PERIODS

• Explain the concepts, network architectures and applications of ad hoc and wireless sensor networks.
• Analyze the protocol design issues of ad hoc and sensor networks.
• Design routing protocols for ad hoc and wireless sensor networks with respect to some protocol design issues.
• Evaluate the QoS related performance measurements of ad hoc and sensor networks.

TEXT BOOK:

1. C. Siva Ram Murthy, and B. S. Manoj, "Ad hoc Wireless Networks: Architectures and Protocols ", Prentice Hall Professional Technical Reference, 2008.

REFERENCES:

1. Carlos  De   Morais   Cordeiro,   Dharma   Prakash   Agrawal   “Ad   Hoc   &   Sensor   Networks:

Theory and  Applications”, World Scientific Publishing Company, 2006.

2. Feng Zhao and Leonides Guibas, "Wireless Sensor Networks", Elsevier Publication –

2002.

3. Holger   Karl   and   Andreas   Willig   “Protocols   and   Architectures   for   Wireless   Sensor

Networks”, Wiley, 2005

3. Kazem Sohraby, Daniel Minoli, & Taieb Znati, “Wireless Sensor Networks-Technology, Protocols,

and Applications”, John Wiley, 2007.

4. Anna Hac, “Wireless Sensor Network Designs”, John Wiley, 2003.

IT6005 Digital Image Processing Syllabus Semester VII Elective IT BTECH Anna University-Regulation-2013

NotesKhan






IT6005                                         DIGITAL IMAGE PROCESSING                                            L T P C
3 0 0  3
OBJECTIVES:
The student should be made to:

• Learn digital image fundamentals
• Be exposed to simple image processing techniques
• Be familiar with image compression and segmentation techniques
• Learn to represent image in form of features

UNIT I             DIGITAL IMAGE FUNDAMENTALS                                                                              8
Introduction  – Origin – Steps in Digital Image Processing  – Components  – Elements of  Visual Perception – Image Sensing and Acquisition – Image Sampling and Quantization – Relationships between pixels - color models

UNIT II            IMAGE ENHANCEMENT                                                                                             10
Spatial Domain: Gray level transformations – Histogram processing – Basics of Spatial Filtering–
Smoothing and Sharpening Spatial Filtering – Frequency Domain: Introduction to Fourier Transform
– Smoothing and Sharpening frequency domain filters – Ideal, Butterworth and Gaussian filters

UNIT III         IMAGE RESTORATION AND SEGMENTATION                                                           9
Noise models – Mean Filters – Order Statistics – Adaptive filters – Band reject Filters – Band pass Filters – Notch Filters – Optimum Notch Filtering – Inverse Filtering – Wiener filtering Segmentation: Detection of Discontinuities–Edge Linking and Boundary detection – Region based segmentation- Morphological processing- erosion and dilation

UNIT IV        WAVELETS AND IMAGE COMPRESSION                                                                    9
Wavelets – Subband coding - Multiresolution expansions - Compression: Fundamentals – Image
Compression models – Error Free Compression – Variable Length Coding – Bit-Plane Coding – Lossless  Predictive  Coding  –  Lossy  Compression  –  Lossy  Predictive  Coding  –  Compression Standards

UNIT V        IMAGE REPRESENTATION AND RECOGNITION                                                          9
Boundary representation – Chain Code – Polygonal approximation, signature, boundary segments – Boundary description – Shape number – Fourier Descriptor, moments- Regional Descriptors – Topological feature, Texture - Patterns and Pattern classes - Recognition based on matching.

TOTAL: 45 PERIODS

OUTCOMES:
Upon successful completion of this course, students will be able to:

• Discuss digital image fundamentals
• Apply image enhancement and restoration techniques
• Use  image compression and segmentation Techniques
• Represent features of  images

TEXT BOOK:

1. Rafael C. Gonzales, Richard E. Woods, “Digital Image Processing”, Third Edition,  Pearson

Education, 2010.

REFERENCES:

1. Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, “Digital Image Processing Using

MATLAB”, Third Edition Tata McGraw Hill Pvt. Ltd., 2011.

2. Anil Jain K. “Fundamentals of Digital Image Processing”, PHI Learning Pvt. Ltd., 2011.
3. Willliam K Pratt, “Digital Image Processing”, John Willey, 2002.
4. Malay K.  Pakhira,  “Digital  Image  Processing  and  Pattern  Recognition”,  First  Edition,  PHI

Learning Pvt. Ltd., 2011.

5. http://eeweb.poly.edu/~onur/lectures/lectures.html
6. http://www.caen.uiowa.edu/~dip/LECTURE/lecture.html

IT6004 Software Testing Syllabus Semester VII Elective IT BTECH Anna University-Regulation-2013

NotesKhan

IT6004                                                 SOFTWARE TESTING                                                 L  T P C
3 0  0 3
OBJECTIVES:
The student should be made to:

• Expose the criteria for test cases.
• Learn the design of test cases.
• Be familiar with test management and test automation techniques.
• Be exposed to test metrics and measurements.

UNIT I             INTRODUCTION                                                                                                             9
Testing as an Engineering Activity – Testing as a Process – Testing axioms – Basic  definitions – Software Testing Principles – The Tester?s Role in a Software Development Organization – Origins of Defects – Cost of defects – Defect Classes – The Defect Repository and Test Design – Defect Examples – Developer/Tester Support of Developing a Defect Repository – Defect Prevention strategies.

UNIT II           TEST CASE DESIGN                                                                                                      9
Test case Design Strategies – Using Black Bod Approach to Test Case Design – Random Testing – Requirements based testing – Boundary Value Analysis – Equivalence Class Partitioning – State- based testing – Cause-effect graphing – Compatibility testing – user documentation testing – domain testing – Using White Box Approach to Test design – Test Adequacy Criteria – static testing vs. structural testing – code functional testing – Coverage and Control Flow Graphs – Covering Code Logic – Paths – code complexity testing – Evaluating Test Adequacy Criteria.

UNIT III           LEVELS OF TESTING                                                                                                    9
The need for Levers of Testing – Unit Test – Unit Test Planning – Designing the Unit Tests – The Test Harness – Running the Unit tests and Recording results – Integration tests – Designing Integration Tests – Integration Test Planning – Scenario testing – Defect bash elimination   System Testing – Acceptance testing – Performance testing – Regression Testing – Internationalization testing – Ad- hoc testing – Alpha, Beta Tests – Testing OO systems – Usability and Accessibility testing – Configuration testing – Compatibility testing – Testing the documentation – Website testing.

UNIT IV         TEST AMANAGEMENT                                                                                                   9
People and organizational issues in testing – Organization structures for testing teams – testing services – Test Planning – Test Plan Components – Test Plan Attachments – Locating Test Items – test management – test process – Reporting Test Results – The role of three groups in Test Planning and Policy Development – Introducing the test specialist – Skills needed by a test specialist – Building a Testing Group.

UNIT V        TEST AUTOMATION                                                                                                         9
Software test automation – skill needed for automation – scope of automation – design and architecture for automation – requirements for a test tool – challenges in automation – Test metrics and measurements – project, progress and productivity metrics.


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

TOTAL: 45 PERIODS

• Design test cases suitable for a software development for different domains.
• Identify suitable tests to be carried out.
• Prepare test planning based on the document.
• Document test plans and test cases designed.
• Use of automatic testing  tools.
• Develop and validate a test plan.

TEXT BOOKS:

1. Srinivasan Desikan  and  Gopalaswamy  Ramesh,  “Software  Testing   –  Principles  and

Practices”, Pearson Education, 2006.

2. Ron Patton, “ Software Testing”, Second Edition, Sams Publishing, Pearson Education, 2007.

REFERENCES:

1. Ilene Burnstein, “ Practical Software Testing”, Springer International Edition, 2003.
2. Edward Kit,” Software Testing in the Real World – Improving the Process”, Pearson Education,

1995.

3. Boris Beizer,” Software Testing Techniques” – 2nd Edition, Van Nostrand Reinhold, New York,

1990.

4. Aditya  P.   Mathur,   “Foundations   of   Software   Testing   _   Fundamental   Algorithms   and

Techniques”, Dorling Kindersley (India) Pvt. Ltd., Pearson Education, 2008.

IT6003 Multimedia Compression Techniques Syllabus Semester VII Elective IT BTECH Anna University-Regulation-2013

NotesKhan

IT6003                                MULTIMEDIA COMPRESSION TECHNIQUES                             L T  P  C
3   0 0  3
OBJECTIVES:
The student should be made to:

• Understand error–control coding.
• Understand encoding and decoding of digital data streams.
• Be familiar with the methods for the generation of these codes and their decoding techniques.
• Be aware of  compression and decompression techniques.
• Learn the concepts of multimedia communication.

UNIT I             MULTIMEDIA COMPONENTS                                                                                       9
Introduction - Multimedia skills - Multimedia components and their characteristics - Text, sound, images, graphics, animation, video, hardware.

UNIT II            AUDIO AND VIDEO COMPRESSION                                                                            9
Audio compression–DPCM-Adaptive PCM –adaptive predictive coding-linear Predictive coding-code excited LPC-perpetual coding Video compression –principles-H.261-H.263-MPEG 1, 2, and 4.

UNIT III          TEXT AND IMAGE COMPRESSION                                                                             9
Compression principles-source encoders and destination encoders-lossless and lossy compression- entropy encoding –source encoding -text compression – static Huffman coding dynamic coding – arithmetic coding –Lempel Ziv-Welsh Compression-image compression.

UNIT IV          VOIP TECHNOLOGY                                                                                                     9
Basics of IP transport, VoIP challenges, H.323/ SIP –Network Architecture, Protocols, Call establishment and release, VoIP and SS7, Quality of Service- CODEC Methods- VOIP applicability.

UNIT V          MULTIMEDIA NETWORKING                                                                                         9
Multimedia networking -Applications-streamed stored and audio-making the best Effort service- protocols for  real time interactive  Applications-distributing  multimedia-beyond  best  effort  service- secluding and policing Mechanisms-integrated services-differentiated Services-RSVP.


OUTCOMES:
Upon Completion of the course, the students will be able to

• Design an application with error–control.
• Use compression and decompression techniques.
• Apply the concepts of multimedia communication.

TOTAL: 45 PERIODS


TEXT BOOKS:

1. Fred Halshall “Multimedia Communication - Applications, Networks, Protocols and Standards”,

Pearson Education, 2007.

2. Tay Vaughan, “Multideai: Making it Work”, 7th Edition, TMH 2008 98.
3. Kurose and W.Ross” Computer Networking “a Top down Approach, Pearson Education 2005.

REFERENCES:

1. Marcus Goncalves “Voice over IP Networks”, Mc Graw Hill 1999.
2. KR. Rao,Z  S  Bojkovic,  D  A  Milovanovic,  “Multimedia  Communication  Systems:  Techniques, Standards, and Networks”, Pearson Education 2007.
3. R. Steimnetz, K. Nahrstedt, “Multimedia Computing, Communications and Applications”, Pearson

Education Ranjan Parekh, “Principles of Multimedia”, TMH 2007.

IT6713 GRID AND CLOUD COMPUTING LABORATORY Syllabus Semester VII IT BTECH Anna University-Regulation-2013

NotesKhan




IT6713                                GRID AND CLOUD COMPUTING LABORATORY                        L T P C
0 0 3  2
OBJECTIVES:
The student should be made to:

• Be exposed to tool kits for grid and cloud environment.
• Be familiar with developing web services/Applications in grid framework
• Learn to run virtual machines of different configuration.
• Learn to use Hadoop

LIST OF EXPERIMENTS: GRID COMPUTING LAB:
Use Globus Toolkit or equivalent and do the following:

1. Develop a new Web Service for Calculator.
2. Develop new OGSA-compliant Web Service.
3. Using Apache Axis develop a Grid Service.

4. Develop applications using Java or C/C++ Grid APIs
5. Develop secured applications using basic security mechanisms available in Globus Toolkit.
6. Develop a Grid portal, where user can submit a job and get the result. Implement it with and without GRAM concept.

CLOUD COMPUTING LAB:
Use Eucalyptus or Open Nebula or equivalent to set up the cloud and demonstrate.

1. Find procedure to run the virtual machine of different configuration. Check how many virtual machines can be utilized at particular time.
2. Find procedure to attach virtual block to the virtual machine and check whether it holds the

data even after the release of the virtual machine.

3. Install a C compiler in the virtual machine and execute a sample program.
4. Show the virtual machine migration based on the certain condition from one node to the other.
5. Find procedure to install storage controller and interact with it.
6. Find procedure to set up the one node Hadoop cluster.
7. Mount the one node Hadoop cluster using FUSE.
8. Write a program to use the API's of Hadoop to interact with it.
9. Write a word count program to demonstrate the use of Map and Reduce tasks.

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

• Use the grid and cloud tool kits.
• Design and implement applications on the Grid.
• Design and Implement applications on the Cloud.

LAB EQUIPMENT  FOR A BATCH OF 30 STUDENTS: SOFTWARE:
Globus Toolkit or equivalent
Eucalyptus or Open Nebula or equivalent to

HARDWARE
Standalone desktops                         30 Nos

TOTAL: 45 PERIODS

IT6712 SECURITY LABORATORY Syllabus Semester VII IT BTECH Anna University-Regulation-2013

NotesKhan



IT6712                                                  SECURITY LABORATORY                                         L T P C
0 0 3  2
OBJECTIVES:
The student should be made to:

• Be exposed to the different cipher techniques
• Learn to implement the algorithms DES, RSA,MD5,SHA-1
• Learn to use tools like GnuPG, KF sensor, Net Strumbler

LIST OF EXPERIMENTS

1. Implement the following SUBSTITUTION & TRANSPOSITION TECHNIQUES concepts:
2. a) Caesar Cipher b) Playfair Cipher c)  Hill Cipher
3. d) Vigenere Cipher
4. e) Rail fence – row & Column Transformation

2. Implement the following algorithms a) DES
3. b) RSA Algorithm c) Diffiee-Hellman
4. d) MD5
5. e) SHA-1

3  Implement the SIGNATURE SCHEME - Digital Signature Standard

4. Demonstrate how to provide secure data storage, secure data transmission and for creating digital signatures (GnuPG).
5. Setup a honey pot and monitor the honeypot on network (KF Sensor)
6. Installation of rootkits and study about the variety of options

7. Perform wireless audit on an access point or a router and decrypt WEP and WPA.( Net Stumbler)
8. Demonstrate intrusion detection system (ids) using any tool (snort or any other s/w)

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

• Implement the cipher techniques
• Develop the various security algorithms
• Use different open source tools for network security and analysis

LAB EQUIPMENTS FOR A BATCH OF 30 STUDENTS: SOFTWARE:
C / C++ / Java or equivalent compiler

GnuPG, KF Sensor or Equivalent, Snort, Net Stumbler or Equivalent

HARDWARE:
Standalone desktops                                     -30 Nos. (or)
Server supporting 30 terminals or more.

TOTAL: 45 PERIODS

IT6711 DATA MINING LABORATORY Syllabus Semester VII IT BTECH Anna University-Regulation-2013

NotesKhan

IT6711                                             DATA MINING LABORATORY                                         L  T P C
0   0 3 2
OBJECTIVES:
The student should be made to:

• Be familiar with the algorithms of data mining,
• Be acquainted with the tools and techniques used for Knowledge Discovery in Databases.
• Be exposed to web mining and text mining

LIST OF EXPERIMENTS:

1. Creation of a Data Warehouse.
2. Apriori Algorithm.
3. FP-Growth Algorithm.
4. K-means clustering.
5. One Hierarchical clustering algorithm.
6. Bayesian Classification.
7. Decision Tree.
8. Support Vector Machines.
9. Applications of classification for web mining.
10. 10. Case Study on Text Mining or any commercial application.

OUTCOMES:
After completing this course, the student will be able to:

• Apply data mining techniques and methods to large data sets.
• Use data mining  tools.
• Compare and contrast the various classifiers.

LAB EQUIPMENT  FOR A BATCH OF 30 STUDENTS: SOFTWARE:
WEKA, RapidMiner, DB Miner or Equivalent

HARDWARE
Standalone desktops                         30 Nos















TOTAL : 45 PERIODS

CS6703 GRID AND CLOUD COMPUTING Syllabus Semester VII IT BTECH Anna University-Regulation-2013

NotesKhan

CS6703	GRID AND CLOUD COMPUTING	L  T	P C
		3 0	0 3

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                                                                                                                9
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                                                                                                               9
Introduction to Open Grid Services Architecture (OGSA) – Motivation – Functionality Requirements –
Practical & Detailed view of OGSA/OGSI – Data intensive grid service models – OGSA services.
UNIT III        VIRTUALIZATION                                                                                                             9
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                                                                                                  9
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                                                                                                                           9
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.


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

TOTAL: 45 PERIODS

• Apply grid computing techniques to solve  large scale scientific problems
• Apply the concept of virtualization
• Use the grid and cloud tool kits
• Apply the security models in the grid and the cloud environment

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.

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.

IT6702 DATA WAREHOUSING AND DATA MINING Syllabus Semester VII IT BTECH Anna University-Regulation-2013

NotesKhan



IT6702                                  DATA WAREHOUSING AND DATA MINING                             L  T  P C
3 0  0 3
OBJECTIVES:
The student should be made to:

• Be familiar with the concepts of data warehouse and data mining,
• Be acquainted with the tools and techniques used for Knowledge Discovery in Databases.

UNIT I           DATA WAREHOUSING                                                                                                    9
Data warehousing Components –Building a Data warehouse –- Mapping the Data Warehouse to a Multiprocessor Architecture – DBMS Schemas for Decision Support – Data Extraction, Cleanup, and Transformation Tools –Metadata.

UNIT II          BUSINESS ANALYSIS                                                                                                    9
Reporting and Query tools and Applications – Tool Categories – The Need for Applications – Cognos Impromptu – Online Analytical Processing (OLAP) – Need – Multidimensional Data Model – OLAP Guidelines – Multidimensional versus Multirelational OLAP – Categories of Tools – OLAP Tools and the Internet.

UNIT III          DATA MINING                                                                                                                 9
Introduction – Data – Types of Data – Data Mining Functionalities – Interestingness of Patterns – Classification of Data Mining Systems – Data Mining Task Primitives – Integration of a Data Mining System with a Data Warehouse – Issues –Data Preprocessing.

UNIT IV         ASSOCIATION RULE MINING AND CLASSIFICATION                                                9
Mining Frequent Patterns, Associations and Correlations – Mining Methods – Mining various Kinds of Association Rules – Correlation Analysis – Constraint Based Association Mining – 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 V          CLUSTERING AND TRENDS IN DATA MINING                                                            9
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.



OUTCOMES:
After completing this course, the student will be able to:

• Apply data mining techniques and methods to large data sets.
• Use data mining tools.
• Compare and contrast the various classifiers.

TOTAL: 45 PERIODS


TEXT BOOKS:

1. Alex Berson and Stephen J.Smith, “Data Warehousing, Data Mining and OLAP”, Tata McGraw –

Hill Edition, Thirteenth Reprint 2008.

2. Jiawei Han  and  Micheline  Kamber,  “Data  Mining  Concepts  and  Techniques”,  Third  Edition,

Elsevier, 2012.

REFERENCES:

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

Person Education, 2007.

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

Economy Edition, Prentice Hall of India, 2006.

3. G. K. Gupta, “Introduction to Data Mining with Case Studies”, Eastern Economy Edition, Prentice

Hall of India, 2006.

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

CS6701 CRYPTOGRAPHY AND NETWORK SECURITY Syllabus Semester VII IT BTECH Anna University-Regulation-2013

NotesKhan


CS6701                             CRYPTOGRAPHY AND NETWORK SECURITY                            L T P C
3 0 0  3
OBJECTIVES:
The student should be made to:

• Understand OSI security architecture and classical encryption techniques.
• Acquire fundamental knowledge on the concepts of finite fields and number theory.
• Understand various block cipher and stream cipher models.
• Describe the principles of public key cryptosystems, hash functions and digital signature.

UNIT I           INTRODUCTION & NUMBER THEORY                                                                        10
Services, Mechanisms and attacks-the OSI security architecture-Network security model-Classical Encryption techniques (Symmetric cipher model, substitution techniques, transposition techniques, steganography).FINITE FIELDS AND NUMBER THEORY: Groups, Rings, Fields-Modular arithmetic- Euclid?s algorithm-Finite fields- Polynomial Arithmetic –Prime numbers-Fermat?s and Euler?s theorem- Testing for primality -The Chinese remainder theorem- Discrete logarithms.

UNIT II          BLOCK CIPHERS & PUBLIC KEY CRYPTOGRAPHY                                                10
Data Encryption Standard-Block cipher principles-block cipher modes of operation-Advanced Encryption Standard (AES)-Triple DES-Blowfish-RC5 algorithm. Public key cryptography: Principles of public key cryptosystems-The RSA algorithm-Key management - Diffie Hellman Key exchange- Elliptic curve arithmetic-Elliptic curve cryptography.

UNIT III       HASH FUNCTIONS AND DIGITAL SIGNATURES                                                           8
Authentication requirement – Authentication function – MAC – Hash function – Security of hash function and MAC –MD5 - SHA - HMAC – CMAC - Digital signature and authentication protocols – DSS – EI Gamal – Schnorr.

UNIT IV       SECURITY PRACTICE & SYSTEM SECURITY                                                               8
Authentication applications – Kerberos – X.509 Authentication services - Internet Firewalls for Trusted System: Roles of Firewalls – Firewall related terminology- Types of Firewalls - Firewall designs - SET for E-Commerce Transactions. Intruder – Intrusion detection system – Virus and related threats – Countermeasures – Firewalls design principles – Trusted systems – Practical implementation of cryptography and security.

UNIT V       E-MAIL, IP & WEB SECURITY                                                                                           9
E-mail Security: Security Services for E-mail-attacks possible through E-mail - establishing keys privacy-authentication of the source-Message Integrity-Non-repudiation-Pretty Good Privacy-S/MIME.
IPSecurity: Overview of IPSec - IP and IPv6-Authentication Header-Encapsulation Security Payload
(ESP)-Internet Key Exchange (Phases of IKE, ISAKMP/IKE Encoding). Web Security: SSL/TLS Basic Protocol-computing the keys- client authentication-PKI as deployed by SSLAttacks fixed in v3-
Exportability-Encoding-Secure Electronic Transaction (SET).
TOTAL: 45 PERIODS

OUTCOMES:
Upon Completion of the course, the students should be able to:

• Compare various Cryptographic Techniques
• Design Secure applications
• Inject secure coding in the developed applications

TEXT BOOKS:

1. William Stallings, Cryptography and Network Security, 6th Edition, Pearson Education, March
2. 2013. (UNIT I,II,III,IV).
3. Charlie Kaufman, Radia Perlman and Mike Speciner, “Network Security”, Prentice Hall of India,
4. 2002. (UNIT V).

REFERENCES:

1. Behrouz A. Ferouzan, “Cryptography & Network Security”, Tata Mc Graw Hill, 2007.
2. Man Young Rhee, “Internet Security: Cryptographic Principles”, “Algorithms and Protocols”, Wiley

Publications, 2003.

3. Charles Pfleeger, “Security in Computing”, 4th Edition, Prentice Hall of India, 2006.
4. Ulysess Black, “Internet Security Protocols”, Pearson Education Asia, 2000.
5. Charlie Kaufman and Radia Perlman, Mike Speciner, “Network Security, Second Edition, Private

Communication in Public World”, PHI 2002.

6. Bruce Schneier and Neils Ferguson, “Practical Cryptography”, First Edition, Wiley Dreamtech

India   Pvt Ltd, 2003.

7. Douglas R Simson “Cryptography – Theory and practice”, First Edition, CRC Press, 1995.
8. http://nptel.ac.in/.

IT6701 INFORMATION MANAGEMENT Syllabus Semester VII IT BTECH Anna University-Regulation-2013

IT6701                                        INFORMATION MANAGEMENT                                            L T P C
3 0  0 3
OBJECTIVES:

• To expose students with the basics of managing the information
• To explore the various aspects of database design and modelling,
• To examine the basic issues in information governance and information integration
• To understand the overview of information architecture.

UNIT I           DATABASE MODELLING, MANAGEMENT AND DEVELOPMENT                              9
Database design and modelling - Business Rules and Relationship; Java database Connectivity (JDBC), Database connection Manager, Stored Procedures. Trends in Big Data systems including NoSQL - Hadoop HDFS, MapReduce, Hive, and enhancements.

UNIT II          DATA SECURITY AND PRIVACY                                                                                     9
Program Security, Malicious code and controls against threats; OS level protection; Security – Firewalls, Network Security Intrusion detection systems. Data Privacy principles. Data Privacy Laws and compliance.

UNIT III        INFORMATION GOVERNANCE                                                                                         9
Master Data Management (MDM) – Overview, Need for MDM, Privacy, regulatory requirements and compliance.  Data Governance – Synchronization and data quality management.

UNIT IV       INFORMATION ARCHITECTURE                                                                                     9
Principles of Information architecture and framework, Organizing information, Navigation systems and
Labelling systems, Conceptual design, Granularity of Content.

UNIT V        INFORMATION LIFECYCLE MANAGEMENT                                                                  9
Data retention policies; Confidential and Sensitive data handling, lifecycle management costs. Archive data using Hadoop; Testing and delivering big data applications for performance and functionality; Challenges with data administration;


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

TOTAL: 45 PERIODS

• Cover core relational database topics including logical and physical design and modeling
• Design and implement a complex information system that meets regulatory requirements;

define and manage an organization's key master data entities

• Design, Create and maintain data warehouses.
• Learn recent advances in NOSQL , Big Data and related tools.

TEXT BOOKS:

1. Alex Berson, Larry Dubov MASTER DATA MANAGEMENT AND DATA GOVERNANCE, 2/E, Tata McGraw Hill, 2011
2. Security in Computing, 4/E, Charles P. Pfleeger, Shari Lawrence Pfleeger, Prentice Hall; 2006
3. Information Architecture for the World Wide Web; Peter Morville, Louis Rosenfeld ; O'Reilly

Media; 1998

REFERENCES:

1. Jeffrey A. Hoffer, Heikki Topi, V Ramesh - MODERN DATABASE MANAGEMENT, 10 Edition, PEARSON, 2012
2. http://nosql-database.org/ Next Gen databases that are distributed, open source and scalable.
3. http://ibm.com/big-data - Four dimensions of big data and other ebooks on Big Data Analytics
4. Inside Cyber Warfare: Mapping the Cyber Underworld- Jeffrey Carr, O'Reilly Media; Second

Edition 2011