DEPARTMENT OF INFORMATION TECHNOLOGY FACULTY OF ENGINEERING · PDF fileDEPARTMENT OF...

M.Tech. (Full Time) – DATABASE SYSTEMS

CURRICULUM & SYLLABUS

2013 – 2014

DEPARTMENT OF INFORMATION TECHNOLOGY

FACULTY OF ENGINEERING AND TECHNOLOGY

SRM UNIVERSITY

SRM NAGAR, KATTANKULATHUR – 603 203

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DEPARTMENT OF INFORMATION TECHNOLOGY

M.Tech. (Full Time) – DATABASE SYSTEMS

CURRICULUM & SYLLABUS

2013 – 2014

COURSE

CODE

COURSE NAME L T P C

SEMESTER I & II

IT2001 Data Structures and Algorithms 3 0 2 4

IT2003 Operating System and Linux Administration 3 0 2 4

DB2001 Database Management Systems 3 0 2 4

DB2002 Database Administration 3 0 2 4

DB2003 Business Analytics and Intelligence 3 0 2 4

DB2004 Data Warehousing and Data Mining 3 0 2 4

SEMESTER III

DB2047 Seminar 0 0 1 1

DB2049 Project work Phase I 0 0 12 6

SEMESTER IV

DB2050 Project work Phase II 0 0 32 16

SUPPORTIVE COURSE

CC2011 Data Analysis using Multivariate Techniques and Forecasting Methods

3 0 0 3

INTER DISCIPLINARY ELECTIVE

One course to be taken in Semester I or II or III

3 0 0 3

PROGRAM ELECTIVES

6 courses of 3 credits each to be taken in Semesters I -III

- - - 18

TOTAL CREDITS 71

Total number of credits to be earned for the award of M.Tech degree = 71

CONTACT HOUR/CREDIT:

L: Lecture Hours per week T: Tutorial Hours per week

P: Practical Hours per week C: Credit

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PROGRAM ELECTIVES

Course

Code

Name of the course L T P C

DB2101 Big Data Analytics 2 0 2 3 DB2102 Distributed Database Systems 3 0 0 3 DB2103 Semantic Web Intelligence 2 0 2 3 DB2104 Advanced Database Management Systems 3 0 0 3

DB2105 Decision Support Systems 3 0 0 3 DB2106 Database Security 3 0 0 3 DB2107 Knowledge Management 2 0 2 3 DB2108 Backup Recovery Systems and

Architecture 3 0 0 3

DB2109 Text Mining 3 0 0 3 DB2110 Object Oriented Software Engineering 3 0 0 3 IT2103 Mobile Application Development 2 0 2 3 IT2105 Artificial Intelligence Planning 3 0 0 3 IT2110 Information Storage Management 3 0 0 3 IT2111 Cloud Computing 2 0 2 3 CC2110 Cloud Application Development 2 0 2 3

NOTE: Students have to register for the courses as per the following

guidelines:

Sl.

No.

Category Credits

I

Semester

II Semester III

Semester

IV

Semester

Category

total

1 Core courses 12 ( 3 courses)

12( 3 courses)

--- --- 24

2 Program Elective courses

18 (in I to III semesters) --- 18

Interdisciplinary elective courses(any one program elective from other programs)

3 (One course to be taken in Semester I or II or III)

3

3 Supportive courses - mandatory

3 (One course to be taken in Semester I or II or III)

--- 3

4 Seminar --- --- 1 --- 1

6 Project work --- --- 06 16 22

Total 71

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SEMESTER I & II

IT2001

DATA STRUCTURES AND ALGORITHMS L T P C

Total contact hours - 75 3 0 2 4

Prerequisite

Nil

PURPOSE

Data structures play a central role in modern computer science. Interaction with data structures much more often than with algorithms (think of Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course will cover major results and current directions of research in data structures.

INSTRUCTIONAL OBJECTIVES

1. To make the student learn an object oriented way of solving problems.

2. To make the student write ADTS for all data structures.

3. To make the student learn different algorithm design techniques.

UNIT I - OVERVIEW OF C++ (5 hours)

C++ class overview – class definition, objects, class members, access control, constructors and destructors, parameter passing methods, dynamic memory allocation and deallocation. Function overloading. UNIT II - LINEAR DATA STRUCTURES AND ALGORITHM ANALYSIS (7 hours)

Review of Arrays, Stacks, Queues, linked lists , Linked stacks and Linked queues, Applications. Efficiency of algorithms, Asymptotic Notations, Time complexity of an algorithm using Big O notation, Average, Best, and Worst Case Complexities, Analyzing Recursive Programs. UNIT III - NON LINEAR DATA STRUCTURES AND HASH TABLES (14 hours)

Introduction, Definition and Basic terminologies of trees and binary trees, Representation of trees and Binary trees, Binary tree Traversals, Threaded binary trees, Graphs- basic concepts – representation and traversals. Introduction, Binary Search Trees: Definition, Operations and applications. AVL Trees: Definition, Operations and applications. B Trees: Definition, Operations and applications. Red – Black Trees, Splay Trees and its applications. Hash Tables: Introduction, Hash Tables, Hash Functions and its applications.

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UNIT IV- DIVIDE AND CONQUER & GREEDY METHOD (9 hours)

General Method, Binary Search, Finding Maximum and Minimum, Quick Sort, Merge sort, Strassen’s Matrix Multiplication, Greedy Method- General Method, Minimum Cost Spanning Trees, Single Source Shortest Path. UNIT V- DYNAMIC PROGRAMMING AND BACKTRACKING (10 hours)

General Method, 0 / 1 Knapsack problem, Reliability Design, Traveling Sales Person’s Problem. General Method, 8 – Queen’s Problem, Graph Coloring. Branch – and – Bound.

PRACTICAL (30 hours)

REFERENCES

1. Mark Allen Weiss, “Data Structures and Problem Solving using C++”, The Benjamin Cummings / Addison Wesley Publishing Company, 2002.

2. Pai G.A.V., “Data Structures and Algorithms”, TMH, 2009. 3. Ellis Horowitz, Sartaj Sahni and Sanguthevar Rajasekaran “Fundamentals of

Computer Algorithms”, 2nd edition, University Press, 1996. 4. Samanta D., “Classic Data Structures”, PHI., 2005. 5. Aho, Hopcraft, Ullman, “Design and Analysis of Computer Algorithms” PEA,

1998. 6. Goodman and Hedetniemi, “Introduction to the Design and Analysis of

Algorithms”, TMH 2002. 7. Horowitz E., Sahani S., “Design and Analysis of Algorithms”, 3rd Edition,

University Press, 2002. 8. Drozdek, “Data Structures and Algorithms in C++”, 2nd Edition, Thomson

Learning Academic Resource Center, 2001.

IT2003

OPERATING SYSTEMS AND LINUX

ADMINISTRATION

L T P C

Total contact hours – 75 3 0 2 4

Prerequisite

Knowledge of Computer Architecture is preferred

PURPOSE

To have a thorough knowledge of processes, scheduling concepts, memory management, I/O and file systems in an operating system.

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1. Get the overview of different types of operating systems

2. Gain thorough knowledge of process management

3. Thorough knowledge of storage management and memory

4. Know the how operating system concepts are implemented in Linux.

5. Know the fundamentals of Linux Administration

UNIT I - OVERVIEW OF OPERATING SYSTEM (9 hours)

Introduction - Mainframe systems – Desktop Systems – Multiprocessor Systems – Distributed Systems. Operating System Services – System Calls – System Programs .Process Concept – Process Scheduling – Operations on Processes – Cooperating Processes – Interposess Communication. Threads- Multithreading Models- Threading Issues. UNIT II - PROCESS SCHEDULING AND MANAGEMENT (9 hours)

CPU Scheduling – Basic Concepts – Scheduling Criteria – Scheduling Algorithms – Multiple-Processor Scheduling – Real Time Scheduling. The Critical-Section Problem – Synchronization Hardware – Semaphores – Classic problems of Synchronization – Critical regions-Monitors- Deadlock characterization-Methods for handling Deadlock-Recovery from Deadlock. UNIT III - MEMORY AND STORAGE MANAGEMENT (9 hours)

Storage Management – Swapping – Paging – Segmentation – Segmentation with Paging- Demand Paging-Page Replacement -Virtual Memory – Demand Paging – Process creation – Page Replacement – Allocation of frames – Thrashing. UNIT IV - LINUX SYSTEMS FUNDAMENTALS (5 hours)

The Linux System – Design Principles – Kernel Modules – Process Management – Scheduling – Memory management – File systems – Network Structure – Security. UNIT V - LINUX ADMINISTRATION (13 hours)

Setting up a LINUX Multifunction Server - Domain Name Service - Installing, Setingup and Safeguarding Linux Web Server – Mail Server – Local Network Services – Backingup Data.


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REFERENCES

1. Abraham Silberschatz, Peter Baer Galvin and Greg Gagne, “Operating System Concepts”, 7th Edition, John Wiley & Sons (ASIA) Pvt. Ltd, 2005.

2. Tom Adelstein and Bill Lubanovic “Linux System Administration”, Published by O’Reilly Media, Inc., March 2007.

3. Harvey M. Deitel, “Operating Systems”, Third Edition, Pearson/Prentice Hall, 2004.

4. Andrew S. Tanenbaum, “Modern Operating System”,Third Edition, Pearson Prentice Hall, 2008.

5. William Stallings, “Operating Systems”, Prentice Hall, 2008.

DB2001

DATABASE MANAGEMENT SYSTEMS L T P C


Prerequisite

Nil

PURPOSE

Most of the organizations depend on databases for storing the data and to share the data among different kinds of users for their business operations. Persistent storage required and several users must be able to safely access the same data concurrently. Hence this course discusses about the problems with the file processing system and how it can be handled effectively in Database Systems through various design tools, design techniques and algorithms.


1. Learn the fundamentals of Database management and to design the database for any given problem.

2. Understand the SQL and Provide the proof of good database design.

3. Know the fundamentals of transaction processing, practical problems of Concurrency control and Recovery mechanisms.

UNIT I - INTRODUCTION TO DATABASE DESIGN (7 hours)

Data- Database – DBMS-File Processing System Vs DBMS- Approaches to build a Database - Data Independence-Data Catalog-Three schema Architecture of a database-Functional components of a DBMS - DBMS Languages - Database design and ER diagrams - Beyond ER Design Entities, Attributes and Entity sets - Relationships and Relationship sets - Additional features of ER Model - Concept Design with the ER Model - Conceptual Design for Large enterprises.

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UNIT II - RELATIONAL MODEL AND SQL (6 hours)

Relational Algebra - Selection and projection set operations - renaming - Joins - Division - Examples of Algebra overviews - Relational calculus – SQL - Basic SQL Query -Nested queries - correlated and uncorrelated queries - Comparison Operators - Aggregative Operators - NULL values - Comparison using Null values - Logical connectivity's - AND, OR and NOT - Impact on SQL Constructs - Outer Joins – PLSQL programming – cursors, procedures, functions, triggers. UNIT III - DEPENDENCIES AND NORMAL FORMS (11 hours)

The importance of a good schema design, - Problems encountered with bad schema designs - Motivation for normal forms- functional dependencies -Armstrong's axioms for FD's- Closure of a set of FD's- Minimal covers-Definitions of 1NF- 2NF- 3NF and BCNF- Decompositions and desirable properties - Algorithms for 3NF and BCNF normalization-Multivalued dependencies-4NF-5NF. UNIT I – TRANSACTION MANAGEMENT AND CONCURRENCY CONTROL

(12 hours)

Overview of Transaction Management: ACID Properties – Transactions and Schedules – Concurrent Execution of the transaction – Lock Based Concurrency Control – Performance Locking – Introduction to Crash recovery. Concurrency Control: Serializability, and recoverability – Introduction to Lock Management – Lock Conversions – Dealing with Dead Locks – Specialized Locking Techniques – Concurrency without Locking. Crash recovery: Introduction to ARIES – the Log – Other Recovery related Structures – the Write- Ahead Log Protocol – Check pointing – recovering from a System Crash – Media recovery. UNIT V - RECOVERY (9 hours)

Overview of Storage and Indexing: Data on External Storage – File Organization and Indexing – Cluster Indexes, Primary and Secondary Indexes – Index data Structures – Hash Based Indexing – Tree base Indexing – Comparison of File Organizations – Indexes and Performance Tuning.


REFERENCES

1. Abraham Silberschatz, Henry F. Korth, S. Sudarshan, “Database System Concepts”, McGraw-Hill, 6th Edition, 2010.

2. Raghu Ramakrishnan, Johannes Gehrke,“Database Management System”, McGraw Hill., 3rd Edition, 2007.

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3. Elmasri & Navathe, “Fundamentals of Database System”, Addison-Wesley Publishing, 5th Edition, 2008.

4. Date C.J, “An Introduction to Database”, Addison-Wesley Pub Co, 8th Edition, 2006.

5. Peter rob, Carlos Coronel, “Database Systems – Design, Implementation, and Management”, 9th Edition, Thomson Learning, 2009.

DB2002

DATABASE ADMINISTRATION L T P C


Prerequisite

Knowledge of Database Management Systems is preferred

PURPOSE

Database administration is the function of managing and maintaining database management systems software. This course includes the concepts those are used to improve the skills in managing the database and to make strong career as Database Administrator for challenging and critical environment.


1. Understand the architecture of database

2. Install, create and maintain databases.

3. Understand the backup and recovery concepts.

4. Configure the database in real time environment

UNIT I - OVERVIEW OF ORACLE AND PHYSICAL STRUCTURE (5 hours)

Introduction - Oracle DB Architecture – Logical and Physical database structure - Instance– Control files – Redo logs Files – Datafiles - Oracle database configuration.

UNIT II-PROFILES AND SECURITY (10 hours)

User creation – Authenticating users – Privileges – System privileges – Role creation – Secure Roles – Assigning roles to users -Security in oracle – Database Auditing – Uniform Audit Trails - Memory Management.

UNIT III – DATA MANAGEMENT (10 hours)

Data pump export – export monitoring – parallel operation – database pump import – using Flashback table feature to save session – Automatic Storage Management – Creating and maintaining ASM.

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UNIT IV-BACKUP AND RECOVERY (10 hours)

Types of failures – Statement failure, User Process failure, Network failure, User error, Instance failure – Background Processes and Recovery - Checkpoint, redo log files and log writer, archiver - Recovery Manager RMAN – Incremental back up - Flash recovery area – Incremental Merge – Resetlogs and Recovery. UNIT V- DATABSE AUDITING AND TUNING (10 hours)

Auditing the Database – Extended Timestamp – GLOBAL_UID and PROXY_SESSIONID, INSTANCE_NUMBER, OS_PROCESS, TRANSACTIONID, Extended DB Auditing, Uniform Audit Trail. Automatic Database Diagnostic Monitor ADDM – SQL Tuning advisor – Reactive, Proactive, Development tuning.


REFERENCES

1. Tom Best, Maria Billings, “Oracle Database 10g: Administration Workshop I”, Oracle Press, Edition 3.1, 2008.

2. Sam R Alapati, “Expert Oracle 10g/11g Administration”, Dreamtech Press, First Edition, 2009.

3. Matthew Hart and Robert G.Freeman, “Oracle Db 10G Rman Backup & Recovery”, Tata McGraw-Hill, 2006.

4. http://www.oracle.com/technetwork/tutorials/index.html 5. http://docs.oracle.com/javase/tutorial/ 6. http://www.oracle.com/technetwork/database/features/availability/rman-

overview-096633.html 7. http://www.youtube.com/watch?v=PIjcMMnSpq4 8. http://www.dba-oracle.com/concepts/rman.htm

DB2003

BUSINESS ANALYTICS AND INTELLIGENCE L T P C


Prerequisite

Nil

PURPOSE

To provide knowledge in business analytics and business intelligence and the way it is implied in data warehousing and data mining by collecting, managing and interpreting data to solve issues and in improving decision making using the knowledge retrieved from database.

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1. To learn the need of business analytics and business intelligence.

2. Use of business analytics in data warehousing and data mining architects.

3. To learn the need for business intelligence and to implement Business intelligence in data mining.

4 Use of business intelligence in data warehousing and data mining architects.

5. Business intelligence in knowledge storage and retrieval.

UNIT I - BUSINESS ANALYTICS (7 hours)

Overview of business analytics - Examples of BA Applications - Business analytics at the strategic level - link between strategy and deployment of BA - four scenarios on strategy and BA - Common database marketing application-obstacles to implementing database marketing application-two definition on data mining-classes of data mining methods.

UNIT II - BUSINESS ANALYTICS IN DATA WAREHOUSNIG AND MINING

(10 hours)

Business analytics at the data warehousing level - why a data warehouse - architects and processes in the data warehouse - business analytics in future - data visualization - Business analytics and data mining - two definition on data mining - classes of data mining methods -grouping method - predictive modeling method – Crisp - dm phase - process model within a phase-business understanding-data understanding. UNIT III - BUSINESS INTELLIGENCE (8 hours)

Defining business intelligence - need for business intelligence - building a road map - designing and planning business intelligence process - From raw data to marketing information - Customer and transactional file - Internal and external data sources (data enhancements and overlays). UNIT IV - BUSINESS INTELLIGENCE IN DATA WAREHOUSNIG AND MINING

(10 hours)

Data warehousing, legacy system, data marts and marketing databases -Relational databases and models- Structured query language (SQL) – end-user perspective -Data mining for business intelligence- Online transaction processing (OLTP)- Online analytical processing (OLAP- Data warehouses and data marts.

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UNIT V - DATA STORAGE AND RETRIEVAL (10 hours)

Querying data from data servers (SQL)- Restructuring transactional files - Recoding alphanumeric and date variables- Date transformation into time periods- Data Import and Transformation- Linear Regression- Regression Output-Regression Transformation- Logistic Regression- Logistic Regression Output. PRACTICAL (30 hours)

REFERENCES

1. Shmueli, Patel and Bruce, “Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner”, Wiley publication,edition 2010.

2. Daniel S. Putler, Robert E. Krider, “Customer and Business Analytics: Applied Data mining for business decision making using R”, CRC press, edition 2012.

3. Gert H. N. Laursen, Jesper Thorlund, “Business Analytics for Managers: Taking Business Intelligence Beyond Reporting”, edition 2010.

4. Turban, Sharda, Delen, King, “Business Intelligence: A Managerial Approach”, Publisher: Prentice Hall, Edition: 2nd, ISBN: 13-978-0-136-10066-9, 2011.

5. Galit Shmueli, Nitin R. Patel and Peter C. Bruce,“Data Mining for Business Intelligence: Concepts, Techniques, and Applications in Microsoft Office Excel with XLMiner”, Wiley, 2007.

6. Paulraj Ponniah, “Data Warehousing Fundamentals - A comprehensive guide for IT professionals”, John Wiley publications, 2nd edition, 2010.

7. http: // www.statsoft.com/textbook/ 8. http: //www.fsb.muohio.edu / departments / isa / undergraduate / minor-

requirements / business-analytics

DB2004

DATA WAREHOUSING AND DATA MINING L T P C


Prerequisite

Nil

PURPOSE

Dramatic advances in data capture, processing power, data transmission, and storage capabilities are enabling organizations to integrate their various databases into data warehouses. Data mining is primarily used by the companies with a strong consumer focus. It enables these companies to determine the factors such as price, product positioning, or staff skills, and economic indicators, competition,

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and customer demographics.


1 Provide efficient distribution of information and easy access to data.

2 Create user friendly reporting environment

3 Find the unseen pattern in large volume of historical data that helps to mange an organization efficiently.

4 Understand the concepts of various data mining Techniques

UNIT I - DATA WAREHOUSING (9 hours)

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 hours)

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 hours)

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 hours)

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 APPLICATIONS AND TRENDS IN DATA MINING

(9hours)

Cluster Analysis - Types of Data – Categorization of Major Clustering Methods - Kmeans– Partitioning Methods – Hierarchical Methods - Density-Based Methods –Grid Based Methods – Model-Based Clustering Methods – Clustering High

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Dimensional Data - Constraint – Based Cluster Analysis – Outlier Analysis – Data Mining Applications.


REFERENCES

1. Alex Berson and Stephen J. Smith, “Data Warehousing, Data Mining & OLAP”, Tata McGraw – Hill Edition, Thirteenth Reprint, 2008.

2. Jiawei Han and Micheline Kamber, Jian Pei, “Data Mining Concepts and Techniques”, Third Edition, Elsevier, 2012.

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

4. Soman K.P., Shyam Diwakar and V. Ajay, “Insight into Data mining Theory and Practice”, Easter Economy Edition, Prentice Hall of India, 2006.

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

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

DB2047

SEMINAR L T P C


Prerequisite

Nil

PURPOSE

Seminar is one of the important components for the engineering graduates to exhibit and expose their knowledge in their field of interest. It also gives a platform for the students to innovate and express their ideas in front of future engineering graduates and professionals.


1 To make a student study and present a seminar on a topic of current relevance in Information Technology or related fields.

2 Enhancing the debating capability of the student while presenting a seminar on a technical topic.

3 Training a student to face the audience and freely express and present his ideas without any fear and nervousness, thus creating self-confidence and courage which are essentially needed for an Engineer.

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GUIDELINES

1 Each student is expected to give a seminar on a topic of current relevance in IT/Related field with in a semester.

2 Students have to refer published papers from standard journals.

3 The seminar report must not be the reproduction of the original papers but it can be used as reference.

ASSESMENT

1 Assessment will be done according to university regulation.

L T P C

DB2049 PROJECT WORK PHASE I

(III SEMESTER)

0 0 12 6

DB2050 PROJECT WORK PHASE II

(IV SEMESTER)

0 0 32 16

PURPOSE

To undertake research in an area related to the program of study INSTRUCTIONAL OBJECTIVE The student shall be capable of identifying a problem related to the program of study and carry out wholesome research on it leading to findings which will facilitate development of a new/improved product, process for the benefit of the society. M.Tech projects should be socially relevant and research oriented ones. Each student is expected to do an individual project. The project work is carried out in two phases – Phase I in III semester and Phase II in IV semester. Phase II of the project work shall be in continuation of Phase I only. At the completion of a project the student will submit a project report, which will be evaluated (end semester assessment) by duly appointed examiner(s). This evaluation will be based on the project report and a viva voce examination on the project. The method of assessment for both Phase I and Phase II is shown in the following table:

Assessment Tool Weightage

In- semester I review 10% II review 15% III review 35%

End semester Final viva voce examination

40%

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Student will be allowed to appear in the final viva voce examination only if he / she has submitted his / her project work in the form of paper for presentation / publication in a conference / journal and produced the proof of acknowledgement of receipt of paper from the organizers / publishers.

SUPPORTING COURSE

CC2011

DATA ANALYSIS USING MULTIVARIATE TECHNIQUES AND FORECASTING

METHODS

L T P C

Total Contact Hours - 45 3 0 0 3

Prerequisite

Nil

PURPOSE

The purpose of this course is to introduce the students into the field of Multivariate Techniques and Forecasting Methods for analyzing large volumes of data and to take decisions based on inference drawn.


1. Data characteristics and form of Distribution of the Data Structures

2. Understanding the usage of multivariate techniques and forecasting methods for the problem under the consideration

3. For drawing valid inferences and to plan for future investigations

UNIT I - MULTIVARIATE ANALYSIS (5 hours)

Meaning of Multivariate Analysis, Measurements Scales: Metric measurement scales and Non-metric measurement scales, Classification of multivariate techniques (Dependence Techniques and Inter-dependence Techniques), Applications of Multivariate Techniques in different disciplines.

UNIT II - FACTOR ANALYSIS (10 hours)

Meanings, Objectives and Assumptions, Designing a factor analysis, Deriving factors and assessing overall factors, Interpreting the factors and validation of factor analysis.

UNIT III - CLUSTER ANALYSIS (10 hours)

Objectives and Assumptions, Research design in cluster analysis, Deriving clusters and assessing overall fit (Hierarchical methods, Non Hierarchical

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Methods and Combinations), Interpretation of clusters and validation of profiling of the clusters.

UNIT IV- FORECASTING TECHNIQUES (10 hours)

Basics of forecasting: Basic steps in forecasting task. The forecasting scenario: Averaging methods, Exponential smoothing methods, Holt’s linear method, Holt-Winters trend and Seasonality method.

UNIT V- TIME SERIES ANALYSIS ( 10 hours)

Box-Jenkins Methodology for ARIMA models: Examining correlation and stationarity of time series data, ARIMA models for time series data (An Auto-regressive model of order one and a Moving Average Model of order one). REFERENCES

1. Joseph F.Hair, William C.Black, Barry J.Babin, Rolph E.Anderson and Ronald L.Tatham (2006). “Multivariate Data Analysis, 6th Edition”, Pearson Education, Inc., (Chapters 1, 3 and 8 ), 2009.

2. Spyros Makridakis, Steven C.Wheelwright and Rob J. Hyndman. “Forecasting methods and Applications, Third Edition”, John Wiley & Sons Inc., New York (Chapters 1, 4 and 7 ), 2005.

INTERDISCIPLINARY ELECTIVE L T P C


Students to choose one Elective course from the list of Post Graduate courses specified under the Faculty of Engineering and Technology other than courses under M.Tech (DBS) curriculum either in I, II or III semester

PROGRAM ELECTIVES

DB2101

BIG DATA ANALYTICS L T P C


Prerequisite

Knowledge of Databse Management Systems, Data mining are preferred

PURPOSE

The purpose is to understand data Science and perform some analytics over big data. Today’s world is data-driven world. Increasingly, the efficient operation of organizations across sectors relies on the effective use of vast amounts of data.This course provides grounding in basic and advanced analytic methods and

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an introduction to big data analytics technology and tools, including MapReduce and Hadoop. INSTRUCTIONAL OBJECTIVES 1. Learn about the basics of data Science. 2. Understand the various supervised and Unsupervised learning Techniques 3. Bring together several key technologies used in manipulating, storing, and

analyzing big data 4. Gain the ability to design highly scalable systems that can accept, process,

store, and analyze large volumes of unstructured data in real time

UNIT I - INTRODUCTION TO DATA SCIENCE (6 hours)

Introduction: Introduction of Data Science-Getting started with R- Exploratory Data Analysis- Review of probability and probability distributions- Bayes Rule Supervised Learning- Regression- polynomial regression- local regression- k-nearest neighbors.

UNIT II - UNSUPERVISED LEARNING (6 hours)

Unsupervised Learning- Kernel density estimation- k-means- Naive Bayes- Data and Data Scraping Classification-ranking- logistic regression .Ethics- time series- advanced regression- Decision trees- Best practices- feature selection. UNIT III - BIG DATA FROM DIFFERENT PERSPECTIVES (6 hours)

Big data from business Perspective: Introduction of big data-Characteristics of big data-Data in the warehouse and data in Hadoop- Importance of Big data- Big data Use cases: Patterns for Big data deployment. Big data from Technology Perspective: History of Hadoop-Components of Hadoop-Application Development in Hadoop-Getting your data in Hadoop-other Hadoop Component. UNIT IV - INFOSPHERE BIGINSIGHTS (6 hours)

Infosphere Big Insights: Analytics for Big data at rest-A Hadoop -Ready Enterprise-Quality file system-Compression –Administrative tooling-Security-Enterprise Integration –Improved workload scheduling-Adaptive map reduce-Data discovery and visualization-Machine Analytics UNIT V- INFOSPHERE STREAMS (6 hours)

Infosphere Streams: Analytics for Big data in motion- Infosphere Streams Basics-working of Infosphere Streams-Stream processing language-Operators-Stream toolkits-Enterprise class

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PRACTICAL : ( 30 hours)

REFERENCES

1. Noreen Burlingame and Lars Nielsen, “A Simple Introduction To Data Science”, 2012.

2. “Understanding Big Data: Analytics for Enterprise Class Hadoop and streaming Data”, The McGraw-Hill Companies, 2012.

DB2102

DISTRIBUTED DATABASE SYSTEMS L T P C

Total contact hours -45 3 0 0 3

Prerequisite

Knowledge of Database management systems is preferred

PURPOSE

The purpose of this course is to learn the breadth and depth of the emerging field in Database, also to learn some advanced transaction models suitable for different types of distributed database systems. To give a good knowledge on query fragmentation and distribution for improving performance.


1. To learn the key concepts and techniques for distributed database implementation, such as data storage, indexing, query evaluation, query optimization, transaction management, concurrency control and cash recovery.

2. To analyze and design distributed database systems based on the principles of distributed indexing, distributed query evaluation, data replication, distributed transaction and distributed concurrency and recovery.

3. To discuss the principles and techniques for database replication and reliability.

UNIT I - OVERVIEW OF DISTRIBUTED DATABASE (7 hours)

Distributed Databases: What and Why? - Distributed Database Management Systems - Promises of distributed database - design issues of distributed databases - distributed database architecture - data fragmentation - Distributed Database Access Primitives - Integrity Constraints in Distributed Databases. UNIT II - DISTRIBUTED DATABASE DESIGN (10 hours)

Framework for Distributed Database Design - Database Fragmentation Design - horizontal fragmentation - vertical fragmentation - Allocation of Fragments -

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allocation problem - allocation model - Translation of Global Queries to Fragment Queries - The Equivalence Transformation for Queries, Transforming Global Queries into Fragment Queries, Distributed Grouping - Aggregate Function Evaluation, Parametric Queries - Database Integration - Schema Matching- Schema Integration- Schema Mapping. UNIT III - QUERY DECOMPOSITION AND DATA LOCALIZATION

(9 hours)

Overview of Query Processing-objectives- Characterization of Query Processors- Layers of Query Processing- Query Decomposition and Data Localization- Localization of Distributed Data- Optimization of Distributed Queries- Centralized Query Optimization- Join Ordering in Distributed Queries- Distributed Query Optimization. UNIT IV - DISTRIBUTED TRANSACTION MANAGEMENT AND CONCURRENCY

CONTROL (10 hours)

Introduction to Transaction Management - Properties of Transactions - Types of Transactions - Distributed Concurrency Control - Taxonomy of Concurrency Control Mechanisms – Locking -Based Concurrency Control Algorithms – Timestamp Based Concurrency Control Algorithms - Optimistic Concurrency Control Algorithms - Deadlock Management - The System R * The Architecture of System R*- Compilation - Execution and Recompilation of Queries - Protocols for Data Definition and Authorization in R* - Transaction and Terminal Management. UNIT V - RELIABILITY AND REPLICATION (9 hours)

Distributed DBMS Reliability - Reliability Concepts and Measures - Failures in Distributed DBMS - Local Reliability Protocols - Distributed Reliability Protocols - Data Replication - Consistency of Replicated Databases - Update Management Strategies - Replication Protocols. REFERENCES

1. Stefano Ceri, Guiseppe Pelagatti, “Distributed Databases - Principles and Systems”, Tata McGraw Hill, 2008.

2. Ozsu M.T./ Sridhar S., “Principles of Distributed database systems”, Pearson education, 2011.

3. Raghu RamaKrishnan, Johnaas Gehrke, “Database Management Systems”, Tata McGrawHill, 2000.

4. Elmasri, Navathe, “Fundamentals of Database Systems”, Addison-Wesley, Fifth Edition 2008.

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5. Peter Rob, Carlos Coronnel, “Database Systems- Design, Implementation and Management”, Course Technology, 2000.

DB2103

SEMANTIC WEB INTELLIGENCE L T P C


Prerequisite

Knowledge in any programming language is preferred.

PURPOSE

This course provides the students with the concepts to create the Semantic Web include a systematic treatment of the different languages like XML, RDF, OWL, and rules and technologies (explicit metadata, ontologies, and logic and inference) that are central to Semantic Web development.


1. Understand the XML technologies, RDF and OWL

2. Develop semantic web application using protégé

3. Develop semantic web services

UNIT I - THE SEMANTIC WEB VISION (4 hours)

Thinking and Intelligent Web applications – The information age – The World Wide Web- Limitations of Today’s Web, syntactic web, data-unstructured, semi structured and structured, Levels of semantics, Semantic Web Technologies – Layered Architecture.

UNIT II - ONTOLOGY DEVELOPMENT (7 hours)

The role of XML – XML and the web – SOAP – Web services – XML technologies – XML revolution - Structuring with schemas – presentation technologies. RDF basic ideas – RDF: Introduction to RDF, Syntax for RDF ,Simple Ontologies in RDF Schema, An Example. Querying in RDF. OWL language – OWL Syntax and Intuitive Semantics, OWL Species, examples.

UNIT III - ONTOLOGY RULES AND QUERYING (7 hours)

Ontology tools- Ontology development using protégé, Description Logics, Automated Reasoning with OWL, Exercises – First-Order Rule Language, Combining Rules with OWL DL. SPARQL: Query Language for RDF, Conjunctive Queries for OWL DL, Exercises, Ontology Engineering.

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UNIT IV – SEMANTIC WEB SERVICE (6 hours)

Semantic web service concepts – Representation mechanisms for semantic web services- WSMO – WSDL-S – Related work in the area of semantic web service frameworks. UNIT V - SEMANTIC WEB SERVICE DISCOVERY (6 hours)

Shortcomings and limitation of conventional web service discovery – Centralized discovery architecture – P2P discovery architecture – Algorithm approaches. Web service modeling ontology – Conceptual model for service discovery –Discovery based on semantic descriptions.

PRACTICAL : (30 hours)

REFERENCES

1. Grigoris Antoniou and Frank Van Harmelen, “A Semantic Web Primer”, The MIT Press, Cambridge, Massachusetts London, England, 2004.

2. www.semanticweb.org 3. Frank. P. Coyle, “XML, Web Services and the data revolution”, Pearson

Education, 2002. 4. Jorge Cardoso, “Semantic web services: Theory, tools and applications”,

Information science, 2007. 5. Michael C, Daconta, Leo J. Obrst and Kevin T. Smith, “The semantic Web: A

guide to the future of XML, web services, and knowledge management”, John wiley & sons, 2003.

6. http://www.dcs.bbk.ac.uk/~michael/sw/sw.html

DB2104

ADVANCED DATABASE MANAGEMENT

SYSTEMS L T P C


Prerequisite

Nil

PURPOSE

Advanced database course aims at developing computer applications with different kinds of data models. A range of features and benefits of Advanced Database Management Systems discusses about parallel databases, object oriented databases, web databases and emerging trends in database systems.

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1 Study the needs of different databases.

2 Get familiarized with transaction management of the database

3 Gain knowledge about web and intelligent database.

4 Provide an introductory concept about the way in which data can be stored in geographical information systems.

UNIT I – PARALLEL DATABASES (9 hours)

Database System Architectures: Centralized and Client-Server Architectures – Server System Architectures – Parallel Systems- Distributed Systems – Parallel Databases: I/O Parallelism – Inter and Intra Query Parallelism – Inter and Intra operation Parallelism – - Case Studies. UNIT II - OBJECT ORIENTED DATABASES (9 hours)

Object Oriented Databases – Introduction – Weakness of RDBMS – Object Oriented Concepts Storing Objects in Relational Databases – Next Generation Database Systems – Object Oriented Data models – OODBMS Perspectives – Persistence – Issues in OODBMS – Object Oriented Database Management System Manifesto – Advantages and Disadvantages of OODBMS – Object Oriented Database Design – OODBMS Standards and Systems – Object Management Group – Object Database Standard ODMG – Object Relational DBMS –Postgres - Comparison of ORDBMS and OODBMS. UNIT III - WEB DATABASES (9 hours)

Web Technology And DBMS – Introduction – The Web – The Web as a Database Application Platform – Scripting languages – Common Gateway Interface – HTTP Cookies – Extending the Web Server – Java – Microsoft’s Web Solution Platform – Oracle Internet Platform – Semi structured Data and XML – XML Related Technologies – XML Query Languages.

UNIT IV - INTELLIGENT DATABASES (9 hours)

Enhanced Data Models For Advanced Applications – Active Database Concepts And Triggers – Temporal Database Concepts – Deductive databases – Knowledge Databases. UNIT V - CURRENT TRENDS (9 hours)

Mobile Database – Geographic Information Systems – Genome Data Management – Multimedia Database – Parallel Database – Spatial Databases - Database administration – Data Warehousing and Data Mining.

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REFERENCES

1. Thomas M. Connolly, Carolyn E. Begg, “Database Systems - A Practical Approach to Design, Implementation, and Management”, Third Edition , Pearson Education, 2003.

2. Ramez Elmasri & Shamkant B.Navathe, “Fundamentals of Database Systems”, Fourth Edition , Pearson Education , 2004.

3. Tamer Ozsu M., Patrick Ualduriel, “Principles of Distributed Database Systems”, Second Edition, Pearson Education, 2003.

4. Prabhu C.S.R., “Object Oriented Database Systems”, PHI, 2003. 5. Peter Rob and Corlos Coronel, “Database Systems – Design,

Implementation and Management”, Thompson Learning, Course Technology, 5th Edition, 2003.

6. Subramanian V.S., “Principles of Multimedia Database Systems”, Harcourt India Pvt Ltd., 2001.

7. Vijay Kumar, “Mobile Database Systems”, John Wiley & Sons, 2006

DB2105

DECISION SUPPORT SYSTEMS L T P C


Prerequisite

Knowledge in Datawarehousihng and Data Mining are preferred.

PURPOSE

Decision-support systems support management decision-making in a business environment. Its focus is to provide viable alternatives for managers rather than replacing judgment with an optimized solution.


1. Introduce the development of decision support or business intelligence, and expert systems as both academic fields and as commercially viable software systems for use to support, and to automate business decision making

2. Enable students to acquire an understanding of the basic concepts and skills associated with decision theory and the modeling of business decisions.

3. Enable students to recognize the different classes of decision support systems or business intelligence, expert systems, and to appreciate the different settings in which these may be used to best effect.

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4 Enable the student to appreciate the role and nature of Group Decision Support Systems and related approaches such as Cognitive Mapping as a means of structuring and supporting complex unstructured decision problems with high levels of uncertainty

UNIT I - MANAGEMENT SUPPORT SYSTEMS-AN OVERVIEW (9 hours) Overview of different types of Decision making system – mapping of databases, MIS, EIS, KBS, expert systems ans OR - Decision Making and computerized support: Management support systems. Decision making systems modeling- support. UNIT II - DECISION MAKING SYSTEMS (9 hours)

Normative, descriptive and prescriptive analysis - Decision Making Systems – Modeling and Analysis – Business Intelligence – Data Warehousing, Data Acquisition - Data Mining. Business Analysis – Visualization - Decision Support System Development – Intelligent decision support systems tools and applications.

UNIT III - KNOWLEDGE MANAGEMENT (9 hours)

Collaboration, Communicate Enterprise Decision Support System & Knowledge management – relationship among knowledge, information and data – organizational knowledge - Collaboration Com Technologies Enterprise information system – knowledge management – Organizational Learning – knowledge management processes and strategy – KM tools.

UNIT IV- INTELLIGENT SUPPORT SYSTEMS (9 hours)

Intelligent Support Systems – AI & Expert Systems – Knowledge based Systems – Knowledge Acquisition, Representation & Reasoning, and advanced intelligence system – Intelligence System over internet. UNIT V- IMPLEMENTATION OF MSS (9 hours)

Managerial requirement of MSS - Implementing MSS in the E-Business ERA – Electronic Commerce – Integration of management support systems - Management Support Systems Emerging Trends and Impacts. REFERENCES

1. Efraim Turban, Jay E. Aronson, Ting-Peng Liang, “Decision Support Systems & Intelligent Systems”, 9th edition, Prentice Hall, 2010.

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2. George M Marakas, “Decision support Systems”, 2nd Edition, Pearson / Prentice Hall,2002.

3. Janakiraman V.S., Sarukesi K., “Decision Support Systems”, PHI, ISBN8120314441, 9788120314443, 2004.

4. Efrem G Mallach, “Decision Support systems and Data warehouse Systems”, 1st Edition, Tata McGraw Hill 2000.

DB2106

DATABASE SECURITY L T P C


Prerequisite

Knowledge of Database Management Systems, Database Administration are preferred.

PURPOSE

This course is about database security, with many methods and techniques that will be helpful in securing, monitoring and auditing database environments. It covers diverse topics that include all aspects of database security and auditing - including network security for databases, authentication and authorization issues, links and replication, database Trojans, etc. It also includes vulnerabilities and attacks that exist within various database environments or that have been used to attack databases.


1. Describe and apply security policies on Databases

2. Understand authentication and password security

3. Know about application vulnerabilities

4 Understand about auditing techniques

UNIT I - DATABASE SECURITY (6 hours)

Introduction to database security – Security in Information Technology - importance of data – database review - identity theft – Levels of security - Human level: Corrupt/careless User, Network/User Interface, Database application program, Database system, Operating System, Physical level. UNIT II - AUTHENTICATION AND AUTHORIZATION (11 hours)

Passwords, Profiles, Privileges and Roles - Authentication – operating system authentication, database authentication, Network or third-party authentication, Database vector password policies - Authorization – User Account authorization,

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- Database/Application Security - Limitations of SQL Authorization - Access Control in Application Layer - Oracle Virtual Private Database – Privacy. UNIT III - APPLICATION VULNERABILITIES (10 hours)

Application Vulnerabilities - Application Security - OWASP Top 10 Web Security Vulnerabilities - Unvalidated input, Broken access control, Broken account/session management, Cross-site scripting (XSS) flaws, Buffer overflows - SQL Injection flaws, Improper error handling, Insecure storage, Denial-of-service, Insecure configuration management. UNIT IV - SECURING DATABASE TO DATABASE COMMUNICATIONS (9 hours)

Monitor and limit outbound communications – secure database links – protect link usernames and passwords – monitor usage of database links – secure replication mechanisms - map and secure all data sources and sinks. Trojans – four types of database Trojans. UNIT V - ENCRYPTING AND AUDITING THE DATA (9 hours)

Encrypting data in transit – encrypting data at rest – auditing architectures – audit trail – architectures of external audit systems - archive auditing information – secure auditing information – audit the audit system. REFERENCES

1. Ron Ben-Natan, “Implementing Database Security and Auditing: A Guide for DBAs, Information Security Administrators and Auditors”, Published by Elsevier, 2005.

2. Silvana Castano, “Database Security” , Published by Addison-Wesley, 1994. 3. Alfred Basta, Melissa Zgola, Dana Bullaboy, Thomas L. Witlock SR,

“Database Security”, google books, 2011. 4. Silberschatz, Korth and Sudarshan, “Database System Concepts”, 6th

Edition, 2010. 5. The Open Web Application Security Project, http://www.owasp.org 6. Web application security scanners, http: // www. Window security . com /

software/Web-Application-Security/ 7. SQL Injection, http://www.cgisecurity.com/development/sql.shtml 8. 9 ways to hack a web app, http : / / developers. sun. com / learning / javaone

online/2005/webtier/TS-5935.pdf 9. Database security, http : / / docs . oracle . com / cd / B19306_01 /

server.102 / b14220/security.htm

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DB2107

KNOWLEDGE MANAGEMENT L T P C

Total contact hours -60 2 0 2 3

Prerequisite

Nil

PURPOSE

Knowledge management is a topic of key interest among businesses which compete with each other to survive in the market. In order to make the students manage knowledge in the data driven world, this course is designed to provide an overview of knowledge representation, management, and tools available for the same.


1. Design and develop knowledge-based information systems for knowledge representation, management, and discovery

2. Understand various knowledge management tools

3. Discuss about relevant case studies to understand how knowledge management is applied in real time scenario

UNIT I - INTRODUCTION (6 hours)

Introduction: An Introduction to Knowledge Management - The foundations of knowledge management- including cultural issues- technology applications- organizational concepts and processes- management aspects- and decision support systems. The Evolution of Knowledge management: From Information Management to Knowledge Management - Key Challenges Facing the Evolution of Knowledge Management - Ethics for Knowledge Management.

UNIT II-CREATING THE CULTURE OF LEARNING AND KNOWLEDGE SHARING

(5 hours)

Organization and Knowledge Management - Building the Learning Organization. Knowledge Markets: Cooperation between Distributed Technical Specialists - Tacit Knowledge and Quality Assurance. UNIT III-KNOWLEDGE MANAGEMENT-THE TOOLS (7 hours)

Telecommunications and Networks in Knowledge Management - Internet Search Engines and Knowledge Management - Information Technology in Support of Knowledge Management - Knowledge Management and Vocabulary Control - Information Mapping in Information Retrieval - Information Coding in the Internet Environment - Repackaging Information.

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UNIT IV-KNOWLEDGEMANAGEMENT-APPLICATION (6 hours)

Components of a Knowledge Strategy - Case Studies (From Library to Knowledge Center, Knowledge Management in the Health Sciences, Knowledge Management in Developing Countries).

UNIT V-FUTURE TRENDS AND CASE STUDIES (6 hours)

Advanced topics and case studies in knowledge management - Development of a knowledge management map/plan that is integrated with an organization's strategic and business plan - A case study on Corporate Memories for supporting various aspects in the process life -cycles of an organization. PRACTICAL (30 hours)

REFERENCES

1. Srikantaiah, T.K., Koenig, M., “Knowledge Management for the Information Professional”, Information Today, Inc., 2000.

2. Nonaka, I., Takeuchi, H., “The Knowledge-Creating Company: How Japanese Companies Create the Dynamics of Innovation”, Oxford University Press, 1995.

DB2108

BACKUP RECOVERY SYSTEMS AND

ARCHITECTURE L T P C

Total contact hours –45 3 0 0 3

Prerequisite

Knowledge on Information Storage and its management is preferred

PURPOSE

The function of backup and recovery is very important in today’s world where systems are frequently subjected to attacks and incidents. In order to understand the principles involved in backup and recovery, this course focuses on the concepts and technologies involved backup and recovery, planning of related activities, backup methods and its related terminology.


1. Describe backup and recovery terminology and operations

2. Understand various types of storage systems and backup storage media

3. Examine the steps involved in planning for backup and recovery

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UNIT I -INTRODUCTION (6 hours)

Need for backup and recovery – common backup and recovery terminology – components of client/server backup server architecture – flow of data in client/server backup and restore operations. UNIT II–INFORMATION STORAGE CONCEPTS (9 hours)

Components of storage system and disk drive – intelligent storage systems – RAID levels and operations – direct attached storage – benefits of SCSI architecture.

UNIT III–CLIENT BASED BACKUP DATA (12 hours)

Backup data – file system and database backup – Microsoft VSS for backup- NDMP – Different forms of virtualization- VMware backup for clients – challenges impacting client backup environments – factors impacting client backup performance. UNIT IV–STORAGE NODE (9 hours)

Components of storage node – Protocols during backup process – types of backup storage media – technologies involved in backup and recovery. UNIT V-BACKUP AND RECOVERY PLANNING (9 hours)

Backup and recovery planning considerations- backup and recovery testing – disaster recovery considerations – key software and hardware products in the backup and recovery – Proposing a backup and recovery solution.

REFERENCES

1. “Backup Recovery Systems and Architecture Student Guide”, EMC Education Services, 2013.

2. Wei-Dong Zhu; Gary Allenbach; Ross Battaglia; Julie Boudreaux; David Harnick-Shapiro; Heajin Kim; Bob Kreuch; Tim Morgan; Sandip Patel; Martin Willingham, “Disaster Recovery and Backup Solutions for IBM FileNet P8 Version 4.5.1 Systems”, IBM Redbooks, 2010.

3. Techbook: “Backup and Recovery in a SAN” EMC Education Services, 2011-2013.

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DB2109

TEXT MINING L T P C


Prerequisite

Knowledge in C++ / Perl / Python, Data structures and Algorithms are preferred

PURPOSE

Text mining is the analysis of data contained in natural language text. The application of text mining techniques is used to solve business problems .Text mining can help an organization derive potentially valuable business insights from text-based content such as word documents, email and postings on social media streams like Facebook, Twitter and LinkedIn.This course covers the techniques for interpreting and retrieving required information from large volumes of unstructured texts.


1. Learn the concepts of Machine Learning

2. Know the concepts of Information Extraction

3. Understand the concepts of Information Retrieval

4. Practice and understand the concepts of Classification and Clustering

UNIT I -NATURAL LANGUAGE PROCESSING (9 hours)

Natural Language Processing – Introduction, Indian Languages, Language and Grammar, Morphology, Syntax, Semantics, Discourse, Synthesis, Machine Translation. Implementation - Regular Expressions, Stemmer, POS Taggers, Spell Checkers, Text Summarization, Question, Answer Systems.

UNIT II - INFORMATION EXTRACTION (9 hours)

Information Extraction - Statistical Modeling, Training Set Preparation, Hidden Markov Models, Conditional Random Fields, Model Evaluation, Model Optimization and Hacks. Implementation - HMM POS Taggers, CRF Address Parsers, Rules based Extraction.

UNIT III – INFORMATION RETRIEVAL (9 hours)

Information Retrieval - Precision-Recall – Vector Space Models – Probabilistic Retrieval – Feature Identification – Feature Selection – Term-Document Matrix – Principal Component Analysis – Latent Semantic Indexing – Similarity Measurements – Cross Language Retrieval - Implementation - Plagiarism detection, Dimension Reduction , Query Expansion.

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UNIT IV-ALGORITHMIC TECHNIQUES (9 hours)

Probabilistic models - Aspect Models, Polysemy, Topic Proportion , Probabilistic Latent, Semantic Analysis, Expectation Maximization Algorithm, Latent Dirichlet Allocation, Gibbs Sampling, Model Evaluation. Implementation - Clustering Terms, Document Classification, Polysemy Keyword Retrieval. UNIT V- CLASSIFICATION (9 hours)

Classification - Naïve Bayes Classifier, Neural Net based Classification, Support Vector Machines. Clustering - Agglomerative Clustering, Divisive Clustering, Distance Measures , K-Means,, K-Nearest Neighbors, Co-clustering, Fuzzy C-Means. Implementation - Keywords Clustering, Document Classification, Taxonomy.

REFERENCES 1. Charles.T.Meadow, Bert R Boyce, Donald H Karft, “Text information Retrievel

System”, 3rd Edition, 2007. 2. David Grossman, OphirFrieder, “Information Retrieval – Algorithms and

Heuristics”, Springer, 2004. 3. Stefan Buttcher,Charles LA Clarke,Dordon. V.Cormack, “Information

Retrieval, Implementing and evaluating Search Engine”, 2010. 4. TanveerSiddiqui, Tiwari, “Natural Language Processing and Information

Retrieval”, Oxford University Press, 2008 . 5. Gerald Kowalski, Mary Maybury, “Information Storage and Retrieval

Systems”, Springer, 2006.

DB2110

OBJECT ORIENTED SOFTWARE ENGINEERING L T P C


Prerequisite

Knowledge of Object Oriented Analysis and Design, Programming in Java are preferred.

PURPOSE:

As Software development is the expensive process, proper measures are required so that the resources can be used efficiently and effectively. Thus this course is to provide the students with the concepts of organized methodology for implementing medium-large software systems like Team programming, Common design and coding methodologies, including Object-Oriented Design (OOD), Design Patterns, Refactoring, and the Unified Modeling Language (UML) and Standard software engineering tools.

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1 Understand the phases in a software project and activities in project management

2 Comprehend the purpose of different UML diagrams

3 Understand the major considerations in collecting, documenting and analyzing project requirements.

4 Cognize the activities in the crucial phase of system design.

5 Identify the key phases in the recent trends of RUP and agile development

UNIT I-INTRODUCTION TO SOFTWARE ENGINEERING (3 hours)

Software engineering development activities-Managing software development.

UNIT II–MODELING WITH UML (9 hours)

UML Diagrams- Use Case Diagrams - Class Diagrams - Interaction Diagrams - State Machine Diagrams - Activity Diagrams. Modeling Concepts - Diagram Organization - Diagram Extension. UNIT III–REQUIREMENTS AND ANALYSIS (9 hours)

Requirements Elicitation - Concepts - Activities & Managing Requirements Elicitation Analysis- Concepts - Analysis Activities - Analysis Model UNIT IV–SYSTEM DESIGN (15 hours)

Decomposing the System - Addressing Design Goals - Reusing Patterns - Specifying Interfaces - Mapping Models to Code.

UNIT V-AGILE DEVELOPMENT AND RATIONAL UNIFIED PROCESS (9 hours)

Rational Unified Process Key Features - Software Best Practices - Static Structure - Dynamic Structure. Agile Development - Adapting to Scrum - Patterns for Adopting to Scrum - New Roles - Changed Roles - Sprints - Product Backlogs – Teamwork. REFERENCES

1. Bernd Bruegge, Alan H Dutoit, “Object-Oriented Software Engineering Using UML, Patterns, and Java”, 3rd Edition, ISBN-10: 0136061257 | ISBN-13: 978-0136061250, 2010.

2. Philippe Kruchten, “The Rational Unified Process: An Introduction”, 3rd Edition, ISBN-10: 0321197704 | ISBN-13: 978-0321197702,2003.

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3. Mike Cohn, “Succeeding with Agile: Software Development Using Scrum”, 1st Edition, ISBN-10: 0321579364 | ISBN-13: 9780321579362, 2010.

4. Grady Booch, James Rumbaugh and Ivar Jacobson, “The Unified Modeling Language User Guide”, Addison-Wesley Longman, USA, 2nd Edition, ISBN-10: 0321267974 | ISBN-13: 978-0321267979, 2005.

5. Timothy Lethbridge, Robert Laganiere, “Object-oriented software engineering: practical software development using uml and java”, | ISBN-10: 0077109082 | ISBN-13: 978-0077109080 | 2nd Edition, 2004.

IT2103

MOBILE APPLICATION DEVELOPMENT L T P C

Total Contact Hours – 60 2 0 2 3

Prerequisite

Knowledge of Core java Programming is required.

PURPOSE:

The course harnesses the skills of student in developing mobile application development using the Android platform.


1. Understanding Mobile Application development features and trends

2. Understand the basics of Android devices and Platform.

3. Impart knowledge on basic building blocks of Android programming Activities, Services, Broadcast Receivers and Content providers

4. Understanding persistence Data storage in Android

5. Understanding Advanced application concepts like networking, cloud interface and Google Maps services etc

6. Enable Students to develop and publish Android applications in to Android Market

UNIT 1- INTRODUCTION (6 hours) Introduction to mobile application development, trends, introduction to various platforms, introduction to smart phones, introduction to development environment/IDE, Android platform features and architecture, versions, android market ANDROID DEVELOPMENT SETUP

Eclipse, ADT, android sdk, tools. Android application anatomy, emulator setup, application framework basics-,resources-layout, values, asset XML

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representation and generated R.Java file ,Android manifest file. Creating a simple application.

UNIT II– ACTIVITIES (8 hours)

Introduction to activities, activities life-cycle, User Interface INTENT – intent object, intent filters – adding categories, linking activities, user interface design components-Fragments, basic views, list views, picker views ,adapter views, Menu ,Action Bar etc, layouts, basics of screen design, registering listeners and different event Listeners. Creating application using multiple activities.,UI views with different layouts UNIT III– DATA PERSISTENCE (4 hours) Shared preferences, File Handling, Managing data using SQLite database CONTENT PROVIDERS – user content provider, android provided content providers. Creating a simple examples using content provider and persisting data into database UNIT IV – BACK GROUND RUNNING PROCESS, NETWORKING AND

TELEPHONY SERVICES (6 hours) Services-Introduction to services–local service-remote service and binding the service- communication between service and activity-Multi-Threading-Handlers and AsyncTask-Android network programming- Telephony services- SMS and telephony applications BROADCAST RECEIVERS–Introduction to receivers, pending intent, Notification. UNIT V- ADVANCED APPLICATIONS (6 hours) Location based services-Google maps services using Google API, Overview on Tweened animations, Property animations- android media-Google App engine and connecting Android apps-Cloud Storage-Android application development guidelines-publishing android applications


REFERENCES

1. Wei-Meng Lee, “Beginning Android 4 Application Development” Wrox Publications, 2012.

2. Paul Deital and Harvey Deital,”Android How to Program”, Detial associates publishers, 2013.

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3. Zigurd Mednieks, Laird Dornin, G. Blake Meike, Masumi Nakamura, “Programming Android Java Programming for the New Generation of Mobile Devices”, O'Reilly Media, July 2011.

4. http://developer.android.com

IT2105

ARTIFICIAL INTELLIGENCE PLANNING L T P C


Prerequisite

Nil

PURPOSE

Planning is a fundamental part of intelligent systems. This course aims to provide a foundation in artificial intelligence techniques for planning, with an overview of the wide spectrum of different problems and approaches, including their underlying theory and their applications.


1. Understand different planning problems

2. Have the basic know how to design and implement AI planning systems using state-space Planning

3. Know how to use AI planning technology for projects in different application domains using HTN (Hierarchical Task Network) Planning

4. Have the ability to make use of graph plan for the problems and developing its heuristics.

5. Know how to plan the time and resources of the problem

UNIT I - INTRODUCTION AND PLANNING IN CONTEXT (9 hours)

Introduction to planning-Conceptual model for planning-Representations for classical planning-Complexity of classical planning UNIT II - STATE-SPACE SEARCH (9 hours)

Heuristic Search and STRIPS-State-Space Planning-The STRIPS algorithm-Domain-Specific State Space Planning

UNIT III - PLAN-SPACE SEARCH AND HTN PLANNING (9 hours)

The Search-Space of Partial Plans-Solution Plans-Algorithms for Plan-Space Planning-Plan-Space versus State-Space Planning-HTN (Hierarchical Task Network) Planning

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UNIT IV - GRAPH PLAN AND ADVANCED HEURISTICS (9 hours)

Planning Graphs-The GraphPlan Planner-Constraint Satisfaction Techniques-Heuristics in Planning UNIT V - PLAN EXECUTION AND APPLICATIONS (9 hours)

Planning with Time and Resources-Time for Planning-Temporal Planning - Planning and Resource Scheduling - Case Studies and Applications. REFERENCES

1. Ghallab M., Nau D., and Traverso P., “Automated Planning: Theory & Practice (The Morgan Kaufmann Series in Artificial Intelligence”, Elsevier, ISBN 1-55860-856-72004, 2004.

2. Stuart Russell, Peter Norvig, “Artificial Intelligence: A Modern Approach”, 3rd Edition, December 11, ISBN-10: 0136042597, ISBN-13: 978-0136042594, 2009.

IT2110

INFORMATION STORAGE MANAGEMENT L T P C


Prerequisite

Knowledge in Database Management Systems, Computer Networks is preferred

PURPOSE

Information Storage and Management have highly developed into a sophisticated pillar of information technology, provides a variety of solutions for storing, managing, accessing, protecting, securing, sharing and optimizing information.


1. Identify the components of managing the data center and Understand logical and physical components of a storage infrastructure.

2. Evaluate storage architectures, including storage subsystems SAN, NAS, IPSAN,CAS

3. Understand thebusiness continuity, backup and recovery methods.

UNIT I - INTRODUCTION TO STORAGE AND MANAGEMENT (9 hours)

Introduction to Information Storage Management - Data Center Environment–Database Management System (DBMS) - Host - Connectivity –Storage-Disk Drive Components- Intelligent Storage System -Components of an Intelligent Storage System- Storage Provisioning- Types of Intelligent Storage Systems

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UNIT II - STORAGE NETWORKING (9 hours)

Fibre Channel: Overview - SAN and Its Evolution -Components of FC SAN -FC Connectivity-FC Architecture- IPSAN-FCOE-FCIP-Network-Attached Storage- General-Purpose Servers versus NAS Devices - Benefits of NAS- File Systems and Network File Sharing-Components of NAS - NAS I/O Operation -NAS Implementations -NAS File-Sharing Protocols-Object-Based Storage Devices-Content-Addressed Storage -CAS Use Cases. UNIT III - BACKUP AND RECOVERY (9 hours)

Business Continuity -Information Availability -BC Terminology-BC Planning Life Cycle - Failure Analysis -Business Impact Analysis-Backup and Archive - Backup Purpose -Backup Considerations -Backup Granularity - Recovery Considerations -Backup Methods -Backup Architecture - Backup and Restore Operations.

UNIT IV - CLOUD COMPUTING (9 hours)

Cloud Enabling Technologies -Characteristics of Cloud Computing -Benefits of Cloud Computing -Cloud Service Models-Cloud Deployment models-Cloud computing Infrastructure-Cloud Challenges.

UNIT V - SECURING AND MANAGING STORAGE INFRASTRUCTURE (9 hours)

Information Security Framework -Storage Security Domains-Security Implementations in Storage Networking - Monitoring the Storage Infrastructure -Storage Infrastructure Management Activities -Storage Infrastructure Management Challenges.

REFERENCES

1. EMC Corporation, “Information Storage and Management”, WileyIndia, 2nd Edition, 2011.

2. Robert Spalding, “Storage Networks: The Complete Reference”, Tata McGraw Hill, Osborne, 2003.

3. Marc Farley, “Building Storage Networks”, Tata McGraw Hill, Osborne, 2nd

edition, 2001. 4. Meeta Gupta, “Storage Area Network Fundamentals”, Pearson Education

Limited, 2002.

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IT2111

CLOUD COMPUTING L T P C


Prerequisite

Knowledge of Computer Networks is preferred

PURPOSE

Cloud Computing has drawn the attention of industries and researchers worldwide. Many applications that are being built nowadays were developed to suit the needs of cloud environment. Hence it becomes necessary to have course in cloud computing which deals with the basics of cloud, different services offered by cloud, and security issues in cloud. In a nutshell, this course on cloud computing provides information on fundamental aspects of the cloud environment.


1. Learn about different deployment models of cloud and different services offered by cloud

2. Understand the technique of virtualization through theoretical concepts and practical training

3. Become knowledgeable in the rudimentary aspects of cloud application development

UNIT I - CLOUD COMPUTING BASICS (4 hours)

Cloud computing components- Infrastructure-services- storage applications-database services – Deployment models of Cloud- Services offered by Cloud- Benefits and Limitations of Cloud Computing – Issues in Cloud security- Cloud security services and design principles. UNIT II - VIRTUALIZATION FUNDAMENTALS (4 hours)

Virtualization – Enabling technology for cloud computing- Types of Virtualization- Server Virtualization- Desktop Virtualization – Memory Virtualization – Application and Storage Virtualization- Tools and Products available for Virtualization. UNIT III - SAAS AND PAAS (6 hours)

Getting started with SaaS- Understanding the multitenant nature of SaaS solutions- Understanding OpenSaaS Solutions- Understanding Service Oriented Architecture- PaaS- Benefits and Limitations of PaaS.

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UNIT IV - IAAS AND CLOUD DATA STORAGE (6 hours) Understanding IaaS- Improving performance through Load balancing- Server Types within IaaS solutions- Utilizing cloud based NAS devices – Understanding Cloud based data storage- Cloud based backup devices- Cloud based database solutions- Cloud based block storage. UNIT V-CLOUD APPLICATION DEVELOPMENT (10 hours)

Client Server Distributed Architecture for cloud – Traditional apps vs. Cloud apps – Client side programming model: Web clients. Mobile clients- Server Side Programming Technologies : AJAX, JSON, Web Services (RPC, REST)- MVC Design Patterns for Cloud Application Development PRACTICAL (30 hours)

REFERENCES

1. Anthony T .Velte, Toby J.Velte, Robert Elsenpeter, “Cloud Computing: A Practical Approach”, Tata McGraw Hill Edition, Fourth Reprint, 2010.

2. Kris Jamsa, “Cloud Computing: SaaS, PaaS, IaaS, Virtualization, Business Models, Mobile, Security and more”, Jones & Bartlett Learning Company LLC, 2013.

3. Ronald L.Krutz, Russell vines, “Cloud Security: A Comprehensive Guide to Secure Cloud Computing”, Wiley Publishing Inc., 2010.

IT2110

CLOUD APPLICATION DEVELOPMENT L T P C


Prerequisite

Knowledge of Java programming, Computer Networks are preferred

PURPOSE

This module introduces students to developing web and cloud applications. By the end of the module the student will be able to build and deploy web and cloud-based application.


1. Use best practices in the design and development of elegant and flexible cloud software solutions.

2. Create, implement and deploy a cloud/LAMP based application.

3. Analyze a real world problem and develop a cloud/LAMP based software solution.

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4. Contrast software development in the web, cloud and others.

UNIT I - CLOUD BASED APPLICATIONS (4 hours)

Introduction, Contrast traditional software development and development for the cloud. Public vs private cloud apps. Understanding Cloud ecosystems – what is SaaS/PaaS, popular APIs, mobile.

UNIT II - DESIGNING CODE FOR THE CLOUD (8 hours) Designing code for the Cloud - Class and Method design to make best use of the Cloud infrastructure; Web Browsers and the Presentation Layer - Understanding Web browsers attributes and differences. Building blocks of the presentation layer: HTML, HTML5, CSS, Silverlight, and Flash.

UNIT III - WEB DEVELOPMENT TECHNIQUES AND FRAMEWORKS (8 hours)

Web Development Techniques and Frameworks-Building Ajax controls, introduction to Javascript using JQuery, working with JSON, XML, REST. Application developement Frameworks e.g. Ruby on Rails , .Net, Java API's or JSF; Deployment Environments – Platform As A Service (PAAS) ,Amazon, vmForce, Google App Engine, Azure, Heroku, AppForce.

UNIT IV – BUILDING AN APPLICATION USING THE LAMP STACK (4 hours)

Use Case 1: Building an Application using the LAMP stack: Setting up a LAMP development environment. Building a simple Web app demonstrating an understanding of the presentation layer and connectivity with persistance. UNIT V - DEVELOPING AND DEPLOYING AN APPLICATION IN THE CLOUD

(6 hours)

Use Case 2: Developing and Deploying an Application in the Cloud - Building on the experience of the first project students will study the design, development, testing and deployment of an application in the cloud using a development framework and deployment platform. PRACTICAL (30 hours)

REFERENCES

1. Chris Hay, Brian Prince, “Azure in Action” [ISBN: 978-1935182481], Microsoft, 2010.

2. Henry Li, “Introducing Windows Azure”, [ISBN: 978-1-4302-2469-3] Apress, 2009

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3. Eugenio Pace, Dominic Betts, Scott Densmore, Ryan Dunn, Masashi Narumoto, MatiasWoloski, “Developing Applications for the Cloud on the Microsoft Windows Azure Platform”, [ISBN: 9780735656062], Microsoft Press; 1 edition, 2010.

4. Eugene Ciurana, “Developing with Google App Engine” [ISBN: 978-1430218319], Apress, 2011.

5. Charles Severance, “Using Google App Engine” [ISBN: 978-0596800697], O'Reilly Media; 1 edition, 2009).

6. George Reese, “Cloud application architectures”, O'Reilly Sebastopol, CA [ISBN: 978-0596156367], 2009.

7. Dan Sanderson, “Programming Google App Engine”, [ISBN: 978-0596522728], O'Reilly Media; 1 edition, 2009.

8. Paul J. Deitel, Harvey M. Deitel, “Ajax, rich Internet applications, and web development for programmers”, Prentice Hall Upper Saddle River, NJ [ISBN: 978-0-13-158738-0], 2008.

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