MCA -Master of Computer Applications Third Semester University Syllabus and Classes of MCA Third Semester

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MCA Semester- 3 CBCS 2020-21 & Onwards

Second Year M.C.A. Semester III(CBCS)
Core
Paper 1 - 3T1

Big Data Analytics

Credits: 4

Getting an Overview of Big Data: What is Big Data,History of Data management,Structuring Big data,Elements of Big data,Big data Analytics,Advantages of Big data Analytics Exploring The Use of Big data. Introducing Technologies for Handling Big data: Distributed and Parallel Computing in Big Data,Introducing Hadoop,HDFS and Map reduce,Cloud computing and big data, Features of Cloud Computing.
Understanding Hadoop Ecosystem:Hadoop Ecosystem,Hadoop Distributed file system,HDFS Architecture,HDFS Commands,Mapreduce,Hadoop YARM,Introducing HBase,HBase Architecture,Combining HBase and HDFS,Hive,Pig and Pig latin,Sqoop,Zookeeper,Flume,Oozie. Understanding MapReduce Fundamentals and HBase: The MapReduce Framework,Exploring the Features of MapReduce,Working of MapReduce,Techniques to Optimize MapReduce Jobs, Uses of MapReduce.

Unit2:
Understanding Big Data Technology Foundation:Exploring The Big data Stack,Data Source Layer,Ingestion Layer,Storage Layer,Physical Infrastructure Layer,Platform Management Layer,Security Layer,Monitoring Layer, Visualization Layer,Big Data Applications, Virtualization and Big Data, VirtualizationApproachesStoring Data In Data Bases and DataWarehouses: RDBMS and BigData,CAP Theorem,Issues with Relational Model,Non- Relational Database, Issues with Non-Relational Model, Integrating Big Data with Traditional Data Warehouses.

Unit3:

Exploring R:Exploring Basic Features of R,Statistical Features,Packages,Graphical User Interfaces,R Console,Developing a Programme,Exploring R Studio,Basic Arithmetic in R,Variables and Functions in R,Handling Data in R Workspace Reading DataSets and Exporting Data from R: Using c() Command,Using scan() Command,Reading Mutiple Data values from Large Files,Reading Data from RStudio,Exporting Data from R. Manipulating and Processing Data In R: Creating Data Subsets, Merging Data Sets in R,Sorting Data, Managing Data in R using Matrices,Managing Data in R using Data Frames.Working withFuctions and Packages in R:Using Functions instead of Scripts, Using Arguments in Functions,Built in Functions in R,Introducing Packages, Working with Packages.Performing Graphical Analysis in R:Using Plots, Saving Graphs to External Files,Advance Features of R.

Unit4:
Data Visualization Ways of Representing Visual Data,Techniques,Types,Applications,Visualizing Big Data,Tools used in Data Visualization Social Media Analytics and Text Mining:Introducing Social Media, Introducing Text Mining, Understanding Text Mining Processes,Sentiment Analysis Mobile Analytics: Introducing Mobile Analytics, Define Mobile Analytics,Introducing Mobile Analytics Tools,Performing Mobile Analytics,Challenges of Mobile Analytics.

Books:

Big Data (Covers Hadoop 2, MapReduce,Hive,YARN,Pig,R and Data Visualization) Black Book, DT Editorial Services, Dreamtech Press.
Data Science & Big Data Analytics Discovering, Analyzing, Visualizing and Presenting Data EMC Education Services, WILEY Publication
Beginners Guide for Data Analysis using R Programming, Jeeva Jose, KhannaPubli.
Data Analytics, Maheshwari, McGraw
Hands-On Programming with R by Grolemund and Garrett
Beginning R: The Statistical Programming Language by Mark Gardener

Second Year M.C.A. Semester III(CBCS) Core
Paper 2 - 3T2 Credits: 4

Data Mining

Unit 1:Introduction to Data Mining: What is Data Mining? Motivating Challenges, Definitions, Origins of Data Mining, Data Mining Tasks, Data: Types of Data- Attributes and Measurement and Types of data sets, Data Quality-Measurement and Data Collection Issues, Issues Related to Applications, Data Preprocessing- Aggregation, Sampling, Dimensionality Reduction, Feature subset selection, Feature creation, Discretization and Binarization, Variable Transformation.

Unit 2:
Exploring Data: The Iris Data Set, Summary Statistics- Frequencies and Mode, Percentiles, Measures of Location: Mean and Median, Measures of Spread: Range and Variance, Multivariate Summary Statistics,Visualization: Representation, Arrangement, Selection, Visualization Techniques: Histograms, Box Plots, Scatter Plots, Contour Plots, Matrix Plots, Parallel Coordinates, Visualizing Higher-Dimensional data, OLAP and Multidimensional data Analysis, Classification: Basic Concepts, Decision Trees, and Model Evaluation: Preliminaries, General Approach to Solving Classification Problem, Decision Tree Induction, Evaluating the Performance of a Classifier, Methods for Comparing Classifiers.

Unit 3:
Classification: Alternative Techniques: Rule-Based Classifier, Rule Ordering Schemes, Building Rules-Based Classifier, Nearest Neighbor Classifiers, Bayesian Classifiers, Naive Bayes Classifier, Artificial Neural Networks (ANN), Support Vector Machines.
Association Analysis: Basic Concepts and Algorithms: Problem Definition, Frequent Itemset Generation- Apriori Principle, Candidate Generation and Pruning, Support Counting, Computational Complexity, Rule Generation, Compact Representation of Frequent Itemsets, Alternative Methods for Generating Frequent Itemsets, FP-Growth Algorithm, FP-Tree Representation.

Unit 4:
Cluster Analysis: Basic Concepts and Algorithms: What is Cluster Analysis? Different Types of Clustering, Types of Clusters, Clustering Algorithms: K-means and its variants, Hierarchical clustering, Density based clustering. Graph-Based Clustering, Shared Nearest Neighbor Approach, Jarvis Patrick Clustering, SNN Density-Based Clustering, Anomaly Detection:Causes of Anomaly Detection, Approaches to Anomaly Detection, Statistical Approaches, Proximity-Based Outlier Detection, Density-based Outlier Detection, Clustering- Based Techniques.

Books:

Introduction to Data Mining , Tan, Steinbach, Kumar.
Data Mining: Concepts and Techniques , Jiawei Han, MichelineKamber, Morgan Kaufmann
Data Mining: Practical Machine Learning Tools and Techniques by Ian H. Witten and Eibe Frank, Morgan Kaufmann
Principles of Data Mining: David Hand, HeikkiMannila and Padhraic Smyth, PHP

Second Year M.C.A. Semester III (CBCS) Core
Paper 3 - 3T3 Credits: 4

Python Programming

Unit 1 :

Introducing Python:What is Python? Python History,Similar Languages Python Fundamentals:Extending Python programms: Interactively,From a File,Other Methods,Script, program or module? Components of a python programming:Built – In- Object types: Python objects and other Languages,Operators basics, Numbers,Strings,Lists,Tuples,Working with Sequences,Dictionaries,Files, object storage, type conversion, type comparisons Statements: statement format, comments, assignments,print, control statements, common traps.
Functions: Function definition and execution, scoping: making objects global, the LGB Rule, scope traps, Arguments: Arguments are Objects, argument calling by Keywords, default arguments, argument tuples, argument dictionaries, function Rules, Return values, Advanced Function calling: The apply statement, the Map Statement, indirect function calls, anonymous functions, Modules: Importing a modules,Packages.Object orientation:Creating a Class Exceptions and error trapping: Exception handling, Built in exceptions.

Unit 2 :
Python’s Built-In Functions: _import_(name[,globals [,locals [,fromlist]]]), apply(function,args,[,keywords ]),getattr(object,name[ ,default ]), hash(object), id(object), isinstance(object,class), list(sequence),setattr(object , name , value) , str(object) , type(object).
Interfacing to the OS : Working with the system(sy module), Working with the Operating system(os module), Multithreading.Processing Information : Manipulating numbers,Text Manipulation,Time,Data types and Operator,Unicode strings.

Unit 3:
Working with Files: File processing:Reading,writing to file,changing position,Controlling File I/O:File Control,IO Control,File Locking,Getting File List,Basic File/Directory Management,Access and Ownership:Checking Access,Getting File information,Setting File Permissions,Manipulating File Paths. Communicating over a network: Creating a network server, client modulles,Handling internet data.Using Python for multimedia: Audio modules, Graphic Modules Using Python as RAD Tool: What RAD realy is, Why Python Application development withPython:Integrated Development Enviornment, Python standard Library. Distributing Python Modules: Using Distutils,future features.

Unit 4 :
Web Development Basics:Writing HTML,Uniform Resource Locators,Dynamic Websites using CGI, Cookies, Security Standard Markup Language Processing: Processing SGML,Processing HTML,Processing XML. Other Python Web Tools: Zope,the Z-Objects

Publishing Enviorment,Jython,Python.Net,Python Server Pages,Python And Active Script,MailMan,Grail,Apache and Python,Socket Server and Base HTTP Server,Medusa. Paths to Cross Platform Development: Basic Platform Support, Execution Enviorntment,Line Termination,Character sets, Files and Pathnames. The Python Architecture:Namespaces,Code blocks and Frames:Code Blocks,Frames,Namespaces,Tracebacks,putting it together,Built in types:Callable object types,Modules,Classes,Class Instances,Internal Types,Byte Code:Python bytecode,bytecode disassembly,byte code instructions(opcodes)

Books:

The Complete Reference Python, Martin C.Brown , Tata McGraw Hill Publication
Programming in Python3, Mark Summerfield
Beginning Python From Novice to Professional, Magnus Lie Hetland(Apress)
Taming Python by Programming, Jeeva Jose, KhannaPubli.
Introduction to Computing and Problem Solving with Python, Jeeva Jose, Khanna Publi.
Python Programming, Seema Thareja, Pearson.

Second Year M.C.A. Semester III (CBCS) Core Elective 2 (CE2-1)
Paper 4 - 3T4 Credits: 4

Artificial Intelligence

Unit 1 :
AI problems, AI Techniques, Tic-tac-toe, Question Answering, Problem as a state space search, A water jug problem, production system, Control strategies, Heuristic Search, Problem Characteristics, Production system characteristics, Design of search programs,
AI Search techniques:- Depth-first, Breadth-first search, Generate-and-test, Hill climbing, Best- first search, Constraint satisfaction, Mean-ends-analysis, A* Algorithm, AO* algorithm.
Unit 2 :
Knowledge Representation:- Representations and mappings, Knowledge Representations, Issues in Knowledge Representation, Predicate Logic:- Representing Instance and Isa Relationships, Computable Functions and predicates, Resolution, Natural Deduction, Logic programming, Forward versus Backward Reasoning, Matching, Control knowledge.
Unit 3 :
Games playing: Minimax search procedure , adding alpha-beta cutoffs, additional refinements,

Planning:- Component of a planning system, Goal task planning, Nonlinear planning, Hierarchical Planning.
Unit 4 :
Understanding, Understanding as Constraint satisfaction, Natural Language Processing, Syntactic Processing, Unification grammars, Semantic Analysis, Parallel and Distributed AI, Psychological Modeling, Distributed Reasoning Systems
Books:

Artificial Intelligence , Elaine Rich, Mcgrawhill Inc.
Lisp Programming,RajeoSangal,TMH
Artificial intelligence, Russell, Pearson.
Artificial Intelligence and Expert Systems , Jankiraman, Sarukes
A first course in Artificial intelligence, Deepak Khemani, McGraw hill.

Second Year M.C.A. Semester III (CBCS) Core Elective 2 (CE2-2)
Paper 4 - 3T4 Credits: 4

Mobile Computing

Unit 1

Mobile Communications: An Overview: Mobile Communication, Mobile Computing, Mobile Computing Architecture, Mobile Devices, Mobile System Networks, Data Dissemination, Mobility Management, Security Mobile Devices and Systems: Mobile Phones, Digital Music Players, Handheld Pocket Computers, Handheld Devices: Operating Systems, Smart Systems, Limitations of Mobile Devices, Automotive Systems GSM and Similar Architectures: GSM‐Services and System, Architecture, Radio Interfaces, Protocols, Localization, Calling Handover, Security, New Data Services, General Packet Radio Service, High‐speed Circuit Switched Data, DECT

Unit 2 :
Wireless Medium Access Control and CDMA based Communication: Medium Access Control, Introduction to CDMA‐based Systems, Spread Spectrum in CDMA Systems, Coding Methods in CDMA, IS‐95 cdma One System, IMT‐ 20 0 0, i‐ m o d e , O F D M , Mobile IP Network Layer: IP and Mobile IP Network Layers, Packet Delivery and Handover Management, Location Management, Registration, Tunnelling and Encapsulation Route Optimization, Dynamic Host Configuration Protocol, Mobile Transport Layer, Conventional TCP/IP Transport, Layer Protocols, Indirect TCP, Snooping TCP, Mobile TCP, Other Methods of TCP‐layer Transmission for Mobile Networks, TCP Over 2.5G/3G Mobile Networks

Unit 3 :
Databases: Database Hoarding Techniques, Data Caching, Client‐Server Computing and Adaptation, Transactional Models, Query Processing, Data Recovery Process, Issues relating to Quality of Service, Data Dissemination and Broadcasting Systems: Communication Asymmetry, Classification of Data‐Delivery Mechanisms, Data Dissemination Broadcast Models, Selective Tuning and Indexing Techniques, Digital Audio Broadcasting, Digital Video Broadcasting, Data Synchronization in Mobile Computing Systems: Synchronization, Synchronization Software for Mobile Devices, Synchronization Protocols, SyncML Synchronization Language for Mobile Computing, Sync4J (Funambol), Synchronized Multimedia ,Markup Language (SMIL)

Unit 4 :
Mobile Devices Server and Management: Mobile Agent, Application Server, Gateways, Portals, Service Discovery, Device Management, Mobile File Systems, Security, Mobile Adhoc and Sensor Networks: Introduction to Mobile Ad‐hoc Network, MANET, Wireless Sensor Networks, Applications Wireless LAN, Mobile Internet Connectivity, and Personal Area Network: Wireless LAN (WiFi) Architecture and Protocol Layers, WAP 1.1 and WAP 2.0, Architectures, XHTML‐MP (Extensible Hypertext Markup Language Mobile Profile), Bluetooth‐enabled Devices Network, Layers in Bluetooth Protocol, Security in Bluetooth Protocol, IrDA, ZigBee Mobile Application Languages XML, Java, J2ME, and Java Card: Introduction, XML, JAVA, Java 2 Micro Edition (J2ME), JavaCard, Mobile Operating Systems : Operating System PalmOS, Windows CE, Symbian OS, Linux for Mobile Devices 530 20

Books :

Mobile Computing, Raj Kamal, Oxford University Press
Mobile Communications Jochen Schiller, Addison‐Wesley.
Handbook of Wireless Networks and Mobile Computing, Stojmenovic and Cacute, Wiley,
Mobile Computing , Talukdar, TMH
Applications with UML and XML, Reza Behravanfar, Cambridge University Press
Mobile Computing ,Brijesh K Gupta, Khanna Publi.

Second Year M.C.A. Semester III (CBCS) Core Elective 2 (CE2-3)
Paper 4 - 3T4 Credits: 4

Machine Learning

Unit1 :Learning – Types of Machine Learning – Supervised Learning – The Brain and the Neuron – Design a Learning System – Perspectives and Issues in Machine Learning – Concept Learning Task – Concept Learning as Search – Finding a Maximally Specific Hypothesis – Version Spaces and the Candidate Elimination Algorithm – Linear Discriminants – Perceptron – Linear Separability – Linear Regression.
Unit 2 :
Multi-layer Perceptron – Going Forwards – Going Backwards: Back Propagation Error – Multilayer Perceptron in Practice – Examples of using the MLP – Overview – Deriving BackPropagation – Radial Basis Functions and Splines – Concepts – RBF Network – Curse of Dimensionality – Interpolations and Basis Functions – Support Vector Machines.
Unit 3 :
Learning with Trees – Decision Trees – Constructing Decision Trees – Classification and Regression Trees – Ensemble Learning – Boosting – Bagging – Different ways to Combine Classifiers – Probability and Learning – Data into Probabilities – Basic Statistics – Gaussian Mixture Models – Nearest Neighbor Methods – Unsupervised Learning – K means Algorithms – Vector Quantization – Self Organizing Feature Map
Unit 4:
Dimensionality Reduction :Linear Discriminant Analysis – Principal Component Analysis – Factor Analysis – Independent Component Analysis – Locally Linear Embedding – Isomap – Least Squares Optimization – Evolutionary Learning – Genetic algorithms – Genetic Offspring: - Genetic Operators – Using Genetic Algorithms – Reinforcement Learning – Overview – Getting Lost Example – Markov Decision Process. Graphical Models:Markov Chain Monte Carlo Methods – Sampling – Proposal Distribution – Markov Chain Monte Carlo – Graphical Models – Bayesian Networks – Markov Random Fields – Hidden Markov Models – Tracking Method

Books:

Introduction to Machine Learning (Adaptive Computation and Machine Learning Series), EthemAlpaydin,Third Edition, MIT Press
Machine learning – Hands on for Developers and Technical Professionals, Jason Bell, Wiley
Machine Learning: The Art and Science of Algorithms that Make Sense of Data‖, Peter Flach,Cambridge University Press.
Deep Learning , Rajiv Chopra, Khanna Publi.
Machine Learning, V. K. Jain, Khanna Publi.

Second Year M.C.A. Semester III (CBCS) Core
Paper 5 - 3T5 Credits: 4

Soft Computing

Unit 1:Introduction of soft computing, soft computing vs hard computing. Soft computing techniques. Computational Intelligence and applications, problem space and searching: Graph searching, different searching algorithms like breadth first search, depth first search techniques, heuristic searching Techniques like Best first Search, A* algorithm, AO* Algorithms. Game Playing: Minimax search procedure, adding alpha-beta cutoffs, additional refinements, Iterative deepening, Statistical Reasoning: Probability and Bayes theorem, Certainty factors and Rules based systems, Bayesian Networks, Dempster Shafer theorem

Unit 2 :Neural Network: Introduction, Biological neural network: Structure of a brain, Learning methodologies. Artificial Neural Network(ANN): Evolution of, Basic neuron modeling , Difference between ANN and human brain, characteristics, McCulloch-Pitts neuron models, Learning (Supervised & Unsupervised) and activation function, Architecture, Models, Hebbian learning , Single layer Perceptron, Perceptron learning, Windrow-Hoff/ Delta learning rule, winner take all , linear Separability, Multilayer Perceptron, Adaline, Madaline, different activation functions Back propagation network, derivation of EBPA, momentum, limitation, Applications of Neural network.

Unit 3 :Unsupervised learning in Neural Network: Counter propagation network, architecture, functioning & characteristics of counter Propagation network, Associative memory, hope field network and Bidirectional associative memory. Adaptive Resonance Theory: Architecture, classifications, Implementation and training. Introduction to Support Vector machine, architecture and algorithms, Introduction to Kohanan’s Self organization map, architecture and algorithms

Unit 4 : Fuzzy systems: Introduction, Need, classical sets (crisp sets) and operations on classical sets Interval Arithmetics,Fuzzy set theory and operations, Fuzzy set versus crisp set, Crisp relation & fuzzy relations, Membership functions.
Fuzzy rule base system: fuzzy propositions, formation, decomposition & aggregation of fuzzy rules, fuzzy reasoning, fuzzy inference systems, fuzzy decision making & Applications of fuzzy logic, fuzzification and defuzzification, Fuzzy associative memory. Fuzzy Logic Theory, Modeling & Control Systems

Books :

S.N. Shivnandam, “Principle of soft computing”, Wiley India.
David Poole, Alan Mackworth “Computational Intelligence: A logical Approach” Oxford.
Eiben and Smith “Introduction to Evolutionary Computing” Springer
E. Sanchez, T. Shibata, and L. A. Zadeh, Eds., "Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives, Advances in Fuzzy Systems - Applications and Theory", River Edge, World Scientific

MCA Semester- 3 Old Syllabus 2016-17 onwards

1.E-Commerce

2. Data Communication and Network

3.Design and Analysis of Algorithm

4. Operation Research

5.Database Administration

6.Practical I (Based on Oracle)

7.Practical II (Based on Operation Research using C++ )

3CSA-1: E-Commerce

UNIT-1
Overview of e-commerce: - Introduction, e-business, benefits of e-commerce, impact of ecommerce on business models, impact of e-commerce on value chain, three pillars of e-commerce e-commerce security. E-commerce and the role of independent third party: - Consulting practices and accountants independence, CPA vision project, New Assurance services identified by AICPA, The Elliott Committee and the Cohen Committee, three waves of e-commerce assurances on ecommerce, third-party assurance of web-based e-commerce, web site seal options,implocation for the accounting profession.

UNIT- 2
The Regulatory environment: - Introduction, Cryptography Issues, Privacy issues, Web linking, Domain name disputes, Internet sales tax, Electronic agreements and Digital signatures,Internet servies providers and International libel Laws, Implications for the accounting profession. EDI, ECommerce and the Internet: - Introduction, Traditional EDI system ,Data transfer and standard, financial EDI, EDI system and the Internet, Impact of EDI-Internet application on the accounting profession.

UNIT - 3
Risk of Insecure Systems: - Introduction, Overview of Risk associated with Internet transaction, Internet associated risks, Intranet associated Risks, Social Engineering Risk associated with business transaction, Risk associated with confidentially maintained Archival, Master file and reference data, Risk associated with Viruses and malicious code overflows, Implications for the Accounting profession. Risk Management: - Control Weakness vs. Control Risk, Risk management, Risk management paradigm, Disaster Recovery Plans, Implications for the accounting profession - evolution of Internet control, Framework, The, role of internal controls in risk management.

UNIT - 4
Cryptography and Authentication:- Messaging Security issues, Encryption Techniques, Key management, additional authentication methods, additional non-repudiation techniques, implications for the accounting profession - Confidentiality, Message integrity, Authentication, non-repudiation, access controls, inter control and risk analysis. Firewalls: - Firewall defined, TCP/IP,,open systems interconnect (OSI), components of Firewall, typical functionality of Firewalls, Network topology, securing the Firewall, factors to consider in Firewall design, In-house solutions vs. commercial firewall software, limitations of the security prevention provided by firewalls, implications for the accounting profession.

UNIT - 5
Electronic commerce payment mechanisms: - The SET protocol, Magnetic strip cards, Smart cards, Electronic checks, electronic cash, implications for the accounting profession.
Intelligent agents: - Definition of intelligent agents, capabilities of intelligent agents, level of
agent sophistication, agent societies, Intelligent agents & e-commerce, The online information chain, limitation of agents, implications for the accounting. Web-Based marketing: - The scope of marketing, business, marketing and Information technology strategy congruence, the four P's applied to internet marketing, the fifth "P" personalization, Internet marketing techniques, On-line advertising mechanisms, Web Site design issues, Intelligent agents and their impact on marketing techniques.

Books

Electronic commerce By Greenstein and Feinman - Tata McGraw-Hill
E-commerce By Bhushan Dewan - S. Chand
Introduction to Computers - Peter Norton's - TMH(4th Ed.)
E-Business: A beginners Guide By Elsenpeter - Tata McGraw-Hill
E-Commerce: The cutting Edge of Business by Bajaj & Nag - Tata McGraw-Hill
E-Commerce by Deepak Goel - S. Chand
E-Commerce, Business on the Net by Kamlesh Agarwal, McMillan.

3CSA-2: Data Communication & Network

Unit - 1: Data Communication
Data Transmission- Concept & Terminology, Analog & Digital data transmission, Transmission Impairment, Transmission Media. Data Encoding- Digital data, Analog Data, Digital signal, Analog signal. Digital Data Communication- Asynchronous and Synchronous transmission, Error detection technique, interfacing. Data Link Control -Line configurations, Flow control, Error control, Data link control protocols. Multiplexing-Frequency division multiplexing, Synchronous time division multiplexing.

Unit - 2: Data Communication Networking
Circuit Switching- Communication Networks, Circuit switching, Single Node network, Digital switching concept, Control Signaling. Packet Switching- Packet switching principles, Virtual circuits and Datagram’s, Routing, Traffic control, X.25. LAN & MAN-LAN, MAN Technology, Bus/Tress and Star topologies, Optical Fiber Bus, Ring Topology, Medium Access Control Protocols, LAN/MAN standards.

Unit - 3: Communication Architecture
Protocols & Architecture- Protocols, The Layered Approach, OSI Model, TCP/IP protocol suite, System Network Architecture. Internetworking- Principles of Internetworking, The Bridge, Routing with Bridges, Connectionless Internetworking, Connectionless Internetwork protocol, Router-level protocol, Connection Oriented Internetworking.

Unit - 4: Protocols
Transport Protocols- Transport services, Protocol Mechanism, Network services, ISO Transport
Standards, TCP & UDP, Light Weight Transport Protocol. Session Service & Protocols- Session Characteristics, OSI Session Service definition, OSI Session Protocol definition.

Unit - 5: Digital Network
ISDN & Broadband ISDN- The integrated digital network; Overview of ISDN, Transmission structure, User Access, ISDN protocols, Broadband ISDN.

Books

Data and Computer Communication by William Stalling, PHI Publication.
Data Communication and Network by Forouzan, Tata McGraw Hill.
Computer Networks, 3rd Edition by Tanenbaum, PHI Publication.
Internetworking with TCP/IP Vol-1 by Comer, PHI Publication.

3CSA-3: Design and Analysis of Algorithm

UNIT: 1
Elementary Algorithmics: Introduction - Problems and Instances - The Efficiency algorithms - Average and worst case Analyses - some examples.
Asymptotic Notation: A notation for "the order of" - Other asymptotic notation - Conditional asymptotic notation - Asymptotic notation with several parameters - Operations on asymptotic notation.

UNIT : 2
Analysis of Algorithms: Introduction - Analyzing control structures - Average case analysis - Amortized Analysis - Solving recurrences.
Greedy Algorithms: Making change - General Characteristics of greedy algorithms - Minimum spanning trees and shortest paths - Knapsack Problem - scheduling.

UNIT-3:
Divide-and Conqure:Introduction-Multiplying large number-The general template-Binary search-sorting , Finding the median-matrix multiplication-Introduction to cryptography.

UNIT:4
Dynamic Programming: The Principle of optimality-making change the knapsack problem- shortest paths - Chained matrix multiplication-approaches using recursion – Memory function.

UNIT5
Back Tracking & Branch Bound: Traversing trees - Depth first search of directed and ndirected graphs - Breadth first search - Back tracking - Branch and bound - the minimax
Principle, Introduction to NP - Completeness: Classes P and NP - Polynomial reductions Complete Problems NP-Hard Problems - Non-deter- ministic algorithms

TextBook:
Fundamentals of Algorithmic.
Gillies Brassard & Paul Brately.
Prentice-Hall (India) Ltd,
References: Fundamentals of Computer Algorithms.
Ellis Horowitz & Sartaj Sahani.
GalgotaPublications.
Computer Algorithms: Introduction to Design & Analysis.
Sara Baase & Alien Van Gelder. ;
Addison Wesley Publishing Company.

3CSA-4: Operation Research

Unit - 1:Introduction to Operation Research (OR) - Origin and Development of OR, Nature of CR Characteristics of OR, Classification of Problems in OR, Models in OR, Phases of OR, uses and Limitations of OR, Methodologies in OR, Applications in OR. Linear Programming - Concept of
Linear Programming Model, Mathematical Formulation of the problem, Graphical solution Methods. Linear Programming Methods - Simplex Methods, 5 5 M methods, Dual Simplex Method, Two Phase Methods. Duality in Linear
Programming - Formulation of Dual Problem, application of Duality.

Unit -2:
Transportation Problem - Mathematical Model for Transportation Problem, Types of spoliation Problem. Assignment Problem - Zero-One programming model for assignment Problem, Types of assignment Problem, Hungerian Method, Branch and Bound technique for Assignment Problem. Game Theory - Terminologies of Game Theory, Two re-sen Zero-Sum Games, The Maximin-Minimax Principle, Games without Saddle Points Mixed Strategies, Graphical Solution of 2xn and mx2 Games, Dominance Property.
Unit -3;
Decision Theory - Introduction, Decision under Certainty, Decision under Risk, Decision Under
Uncertainty, Decision Tree. Network Scheduling By CPM/PERT - Introduction, Basic Concept,
Constraints in Network, Critical Path Methods (CPM), PERT Network, PERT Calculation, Time-Cost-TradeOff Aspects in Network Technique, Advantage of Network

Unit -4:
Inventory Control -Introduction, Inventory Control, Selective Control Techniques, Types of Inventory, Economic Lot Size Problem, Problem of EOQ with shortage, Inventory Control Techniques-Uncertainty Demand, Inventory Control Techniques-Stochastic Problem, Inventory Control with Price Breaks.

Unit -5:
Queuing Theory - Introduction, Terminologies of Queuing System, Characteristics of Queuing System, Poisson Process and Exponential Distribution, Classification of Queues,
Definition of Transient and steady States, Poisson Queues, Non-Poisson Queuing Systems,
Cost-Profit Models in Queuing, Queuing Control. ,
Books

Operation Research By Kanti Swarup, P.K.Gupta, Man Mohan [Sultan].
Operation Research By R. Panneerselvam [PHI].
Introduction To Operation Research By Billy E. Gillett [Tata McGraw-Hill]
Operation Research By Hira Gupta.
Operation Research Problems & Solutions by Sharma J.K., Macmillan
Operation Research Theory & Application by Sharma J,K, MacMillan.

3CSA-5: Database Administration

Unit - 1: Introduction to Oracle Database Administration
Introduction to relational database management system, Database modeling and relational database design, Creating databases, Background processes, Internal database structure, Database file layout, Verification of I/O estimate, Database space usage overview, Resizing data file.

Unit - 2: Oracle SQL and PL/SQL
Basic SQL and PL/SQL concepts terminology and programming, Enhancements SQL, Enhancement to Globalization, Writing queries, Using procedure builders, Data Manipulation language (DML), Data definition language (DDL).

Unit - 3:
Oracle Database Architecture and Administration Oracle database architecture, Design, Creation,
Migration and Management of Oracle Databases and related database schemes, Data Dictionary views and standard packages, Maintaining the control, Redo Log files, Managing Table spaces and Data Files, Storage structure and relationships, Managing rollback segment, Managing tables, Indexes, Managing data Integrity, Managing password security and resources, Managing users, Privileges, roles.

Unit - 4: Oracle Backup and Recovery Strategies
Backup and recovery considerations, Oracle recovery structure and processes, Oracle backup and recovery configuration, Physical backup, Complete recovery of an Oracle database, Incomplete recovery of an Oracle database with Archiving, Oracle Export / Import utilities, Oracle standby database.

Unit - 5: Oracle Tuning and Troubleshooting
Oracle performance tuning methodology, Oracle alert and trace files, Tuning the shared pool, Buffer Cache, Redo Log buffer, Database configuration and I/O issues, Using Oracle Blocks efficiently, Optimizing sort operations, Rollback segment tuning, Monitoring and detecting lock contention, SQL issues and tuning considerations for different application.

Note: Oracle 8i version to be used.

BOOKS