industrial management& entrepreneurship development ... · introduction to hadoop –open...

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INDUSTRIAL MANAGEMENT& ENTREPRENEURSHIP DEVELOPMENT

14ME801

Lectures : 4 Periods/Week Continuous Evaluation : 40

Final Exam : 3 hours Semester End Exam : 60 UNIT – I

General management: Management definition, Functions of Management and Principles ofManagement. Forms of Business Organization: Salient features of Sole Proprietorship, Partnership, Joint Stock Company: Private Limited and Public Limited companies; Merits and Demerits of above types Marketing Management: Functions of Marketing, Concepts of Selling and Marketing, Marketing mix (4 Ps); Advertising and sales promotion; Product life cycle.

UNIT – II

Production Management: Types of production systems, Productivity Vs Production, Production planning and control Materials Management: Inventory Control, Basic EOQ model, ABC analysis Quality Control: Control Charts: chart, R chart, P chart, C chart, Acceptance sampling.

UNIT – III

Financial Management: Functions of finance, Types of Capital-Fixed and Working Capital,

Break Even Analysis.

Depreciation- Straight line method of depreciation, declining balance method and the Sum

of Years digits method of Depreciation.

Personnel Management: Functions of personnel management, human resource planning,recruitment, selection, placement, training and development and performance appraisal. Motivation theories, leadership styles.

UNIT – IV

Entrepreneurship Development: Introduction, Entrepreneurial characteristics, Functions

of anEntrepreneur; Factors affecting entrepreneurship; Role of communication in

entrepreneurship; Entrepreneurial development-Objectives, Need of Training for

enterprises; Finance for the enterprises; Product, Process and Plant Design- Product

analysis and Product Design process. Steps in process design and Plant Design.

TEXT BOOKS:

1. Industrial Engineering and Operations Management, S.K.Sharma, Savita Sharma and

Tushar Sharma. 2. Industrial Engineering and Production Management, Mahajan. 3. Management Science, A.R.Aryasri REFERENCE BOOKS: 1. Operations Management, Joseph G Monks.

2. Marketing Management, Philip Kotler.

3. The Essence of Small Business, Barrow colin. 4. Small Industry Ram K Vepa

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BIG DATA ANALATICS 14CS802

Lectures : 4 Periods/Week, Tutorial: 1 Continuous Evaluation : 40

Final Exam : 3 hours Semester End Exam : 60

UNIT I

UNDERSTANDING BIG DATA: (14 Periods) What is big data – why big data – convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and bigdata – credit risk management – big data and algorithmic trading – big data and healthcare – bigdata in medicine – advertising and big data – big data technologies – introduction to Hadoop –open source technologies – cloud and big data – mobile business intelligence – Crowd sourcinganalytics – inter and trans firewall analytics

UNIT II NOSQL DATA MANAGEMENT: (14 Periods) Introduction to NoSQL – aggregate data models – aggregates – key-value and document datamodels – relationships – graph databases – schemaless databases – materialized views –distribution models – sharding – master-slave replication – peer-peer replication – sharding andreplication – consistency – relaxing consistency – version stamps – map-reduce – partitioning andcombining – composing map-reduce calculations

UNIT III BASICS OF HADOOP: (16 Periods) Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes –design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow –Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures MAPREDUCE APPLICATIONS: MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy ofMapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN –job scheduling – shuffle and sort – task execution – MapReduce types – input formats – outputformats

UNIT IV HADOOP RELATED TOOLS : (16 Periods) Hbase – data model and implementations – Hbase clients – Hbase examples – praxis.Cassandra– cassandra data model – cassandra examples – cassandra clients – Hadoop integration.Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts.Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation – HiveQLqueries. REFERENCES: 1. Michael Minelli, Michelle Chambers, and Ambiga Dhiraj, "Big Data, Big Analytics: EmergingBusiness Intelligence and Analytic Trends for Today's Businesses", Wiley, 2013. 2. P. J. Sadalage and M. Fowler, "NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence", Addison-Wesley Professional, 2012. 3. Tom White, "Hadoop: The Definitive Guide", Third Edition, O'Reilley, 2012. 4. Eric Sammer, "Hadoop Operations", O'Reilley, 2012.

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5. E. Capriolo, D. Wampler, and J. Rutherglen, "Programming Hive", O'Reilley, 2012. 6. Lars George, "HBase: The Definitive Guide", O'Reilley, 2011. 7. Eben Hewitt, "Cassandra: The Definitive Guide", O'Reilley, 2010. 8. Alan Gates, "Programming Pig", O'Reilley, 2011.

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Web Mining 14CS803/A

Lectures : 4 Periods/Week, Self Study:1 Continuous Assessment : 40

Final Exam : 3 hours Final Exam Marks : 60

UNIT I

INTRODUCTION: (16 Periods) Introduction – Web Mining – Theoretical background –Algorithms and techniques – Association rule mining – Sequential Pattern Mining -Information retrieval and Web search – Information retrieval Models-Relevance Feedback- Text and Web page Pre-processing – Inverted Index – Latent Semantic Indexing – Web Search – Meta-Search – Web Spamming

UNIT II WEB CONTENT MINING: (15 Periods) Web Content Mining – Supervised Learning – Decision tree - Naïve Bayesian Text Classification - Support Vector Machines - Ensemble of Classifiers. Unsupervised Learning - K-means Clustering -Hierarchical Clustering –Partially Supervised Learning – Markov Models - Probability-Based Clustering - Evaluating Classification and Clustering – Vector Space Model – Latent semantic Indexing – Automatic Topic Extraction - Opinion Mining and Sentiment Analysis - Document Sentiment Classification

UNIT III WEB LINK MINING: (14 Periods) Web Link Mining – Hyperlink based Ranking – Introduction -Social Networks Analysis- Co-Citation and Bibliographic Coupling - Page Rank -Authorities and Hubs -Link-Based Similarity Search -Enhanced Techniques for Page Ranking - Community Discovery – Web Crawling -A Basic Crawler Algorithm- Implementation Issues- Universal Crawlers- Focused Crawlers- Topical Crawlers-Evaluation - Crawler Ethics and Conflicts - New Developments

UNIT IV STRUCTURED DATA EXTRACTION: (15 Periods) Structured Data Extraction: Wrapper Generation – Preliminaries- Wrapper Induction- Instance-Based Wrapper Learning ·- Automatic Wrapper Generation: Problems - String Matching and Tree Matching -.Multiple Alignment - Building DOM Trees - Extraction Based on a Single List Page and Multiple pages- Introduction to Schema Matching - Schema-Level Match -Domain and Instance-Level Matching – Extracting and Analyzing Web Social Networks. REFERENCES: 1. Bing Liu, “Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data (Data-Centric Systems and Applications)”, Springer; 2nd Edition 2009 2. GuandongXu, Yanchun Zhang, Lin Li, “Web Mining and Social Networking: Techniques and Applications”, Springer; 1st Edition.2010 3. Zdravko Markov, Daniel T. Larose, “Data Mining the Web: Uncovering Patterns in Web Content, Structure, and Usage”, John Wiley & Sons, Inc., 2007

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4. SoumenChakrabarti, “Mining the Web: Discovering Knowledge from Hypertext Data”, Morgan Kaufmann; edition 2002 5. Adam Schenker, “Graph-Theoretic Techniques for Web Content Mining”, World Scientific Pub Co Inc , 2005 6. Min Song, Yi Fang and Brook Wu, Handbook of research on Text and Web mining technologies, IGI global, information Science Reference – imprint of :IGI publishing, 2008.

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ADVANCED DATABASE MANAGEMENT 14CS803/B

Lectures : 4 Periods/Week, Self Study:1 Continuous Assessment : 40


UNIT I

RELATIONAL MODEL ISSUES (16 Periods)

ER Model - Normalization – Query Processing – Query Optimization - Transaction Processing

- Concurrency Control – Recovery - Database Tuning.

UNIT II

DISTRIBUTED DATABASES (15 Periods)

Parallel Databases – Inter and Intra Query Parallelism – Distributed Database Features –

Distributed Database Architecture – Fragmentation – Distributed Query Processing –

Distributed Transactions Processing – Concurrency Control – Recovery – Commit Protocols.

UNIT III

OBJECT ORIENTED DATABASES (14 Periods)

Introduction to Object Oriented Data Bases - Approaches - Modeling and Design Persistence

– Query Languages - Transaction - Concurrency – Multi Version Locks – Recovery –

POSTGRES – JASMINE –GEMSTONE - ODMG Model.

UNIT IV

EMERGING SYSTEMS (15 Periods)

Enhanced Data Models - Client/Server Model - Data Warehousing and Data Mining -

Web Databases – Mobile Databases- XML and Web Databases.

CURRENT ISSUES Rules - Knowledge Bases - Active and Deductive Databases -

Multimedia Databases– Multimedia Data Structures – Multimedia Query languages -

Spatial Databases.

TEXT BOOKS 1. Thomas Connolly and CarlolynBegg, “Database Systems, A Practical Approach to Design, Implementation and Management”, Third Edition, Pearson Education REFERENCES 1. R. Elmasri, S.B. Navathe, “Fundamentals of Database Systems”, Fifth Edition, PearsonEducation,2006. 2. Abraham Silberschatz, Henry F. Korth, S. Sudharshan, “Database System Concepts”, FifthEdition,TataMcGrawHill,2006. 3. C.J.Date, A.Kannan, S.Swamynathan, “An Introduction to Database Systems”, Eighth Edition, Pearson Education, 2006.

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BIOINFORMATICS 14CS803/C

Lectures : 4 Periods/Week, Self Study: 1 Continuous Assessment : 40


UNIT-1 uni (16 Periods)

Introduction: Definitions, Sequencing, Molecular Biology and Bioinformatics, Biological sequence/structure, Genomoe Projects, Pattern Recognition and prediction, Folding problem, Sequence Analysis, Homology and Analogy, Bioinformatics Applications, Central Dogma of Molecular Biology Information Resources: Biological databases, Primary Sequence databases, Protein sequence databases, Secondary databases, Protein pattern databases, and Structure classification databases DNA sequence databases, specialized genomic resources

UNIT – II (14 Periods) DNA Sequence Analysis: Importance of DNA analysis, Gene Structure and DNA sequences,Features of DNA sequence analysis, EST (Expressed Sequence Tag) searches, Gene Hunting, Profile of a cell, EST analysis, Effects of EST data on DNA databases, The Human Genome Project Pair Wise Alignment Techniques: Database Searching, Alphabets and complexity, algorithmand programs, comparing two sequences, sub-sequences, Identity and similarity, The Dot plot, Local and Global similarity, Different alignment techniques, Scoring Matrices, Dynamic Programming, Pair wise database searching

UNIT – III (15 Periods) Multiple sequence alignment & Phylogenetic Analysis: Definition and goal, The consensus,Computational complexity, Manual methods, Simultaneous methods, Progressive methods, Databases of Multiple alignments, and searching, Applications of Multiple Sequence alignment, Phylogenetic Analysis, Methods of Phylogenetic Analysis, Tree Evaluation, Problems in Phylogenetic analysis, Tools for Phylogenetic Analysis Secondary database Searching: Importance and need of secondary database searches,secondary database structure and building a sequence search protocol.

UNIT – IV (15 Periods)

Gene Expression and Microarrays: Introduction, DNA Microarrays, Clustering Gene Expression Profiles, Data Sources and tools, Applications. Analysis Packages: Analysis Package structure, commercial databases, commercial software,comprehensive packages, packages specializing in DNA analysis, Intranet Packages, Internet Packages.

TEXT BOOK: 1. “Introduction to Bioinformatics”, T K Attwood and D.J. Parry-Smith, Pearson. 2. “Bioinformatics methods and applications”, S.C. Rastogi, N. Mendiratta and P.

Rastogi., PHI.

REFERENCE BOOKS: 1. “Introduction to Bioinformatics”, Arthur M. Lesk, OXFORD Publishers (Indian Edition). 2. “Elementary Bioinformatics”, ImtiyazAlam Khan, Pharma Book Syndicate.

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BUSINESS INTELLIGENCE

14CS803/D

Lectures : 4 Periods/Week, Self Study: 1 Continuous Assessment : 40


UNIT I LINEAR REGRESSION (16 Periods) Introduction to data analysis – Statistical processes – statistical models – statistical inference – review of random variables and probability distributions – linear regression – one predictor – multiple predictors – prediction and validation – linear transformations – centering and standardizing – correlation – logarithmic transformations – other transformations – building regression models – fitting a series of regressions UNIT II LOGISTIC AND GENERALIZED LINEAR MODELS (14 Periods) Logistic regression – logistic regression coefficients – latent-data formulation – building a logisticregression model – logistic regression with interactions – evaluating, checking, and comparingfitted logistic regressions – identifiability and separation – Poisson regression – logistic-binomialmodel – Probit regression – multinomial regression – robust regression using t model – buildingcomplex generalized linear models – constructive choice models UNIT III SIMULATION AND CAUSAL INFERENCE (15Periods) Simulation of probability models – summarizing linear regressions – simulation of non-linear predictions – predictive simulation for generalized linear models – fake-data simulation – simulating and comparing to actual data – predictive simulation to check the fit of a time-series model – causal inference – randomized experiments – observational studies – causal inference using advanced models – matching – instrumental variables UNIT IV MULTILEVEL REGRESSION (15 Periods) Multilevel structures – clustered data – multilevel linear models – partial pooling – group-level predictors – model building and statistical significance – varying intercepts and slopes – scaled inverse-Wishart distribution – non-nested models – multi-level logistic regression – multi-level generalized linear models REFERENCES: 1. Andrew Gelman and Jennifer Hill, "Data Analysis using Regression and multilevel/Hierarchical Models", Cambridge University Press, 2006. 2. Philipp K. Janert, "Data Analysis with Open Source Tools", O'Reilley, 2010.

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3. Wes McKinney, "Python for Data Analysis", O'Reilley, 2012. 4. DavinderjitSivia and John Skilling, "Data Analysis: A Bayesian Tutorial", Second Edition, Oxford University Press, 2006. 5. Robert Nisbelt, John Elder, and Gary Miner, "Handbook of statistical analysis and data mining applications", Academic Press, 2009. 6. Michael Minelli, Michelle Chambers, and AmbigaDhiraj, "Big Data, Big Analytics: Emerging Business Intelligence and Analytic Trends for Today's Businesses", Wiley, 2013. 7. John Maindonald and W. John Braun, "Data Analysis and Graphics Using R: An Examplebased Approach", Third Edition, Cambridge University Press, 2010. 8. David Ruppert, "Statistics and Data Analysis for Financial Engineering", Springer, 2011.

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WIRELESS NETWORKS

14CS 804/A

Lectures : 4 Periods/Week Continuous Assessment : 40


UNIT – I (20 Periods)

Introduction: Applications, A short history of Wireless Communications, A market forMobile Communications, A simplified reference model. Wireless Transmission: Frequencies, Signals, Antennas, Signal Propagation, Multiplexing,Modulation, Spread Spectrum. Medium Access Control: Motivation for a specialized MAC, SDMA, FDMA, TDMA, CDMA,Comparison.

UNIT – II (22 Periods)

Telecommunication Systems: GSM, DECT, TETRA, UMTS and IMT-2000. Satellite Systems – History, Applications, Basics (GEO, LEO, MEO), Routing, Localization,Handover. Broadcast Systems: Over view, Cyclic repetition of data, Digital Audio Broadcasting, DigitalVideo Broadcasting.

UNIT – III (21 Periods)

Wireless LAN: Infrared Vs. Radio transmission, Infrastructure and ad hoc networks, IEEE802.11: System Architecture, Protocol Architecture, Physical Layer, MAC Layer, MAC Management, Bluetooth: User Scenarios, Architecture, Protocol Stack.

Mobile Network Layer: Mobile IP, Dynamic host configuration, Ad hoc networks. UNIT – IV

(18 Periods) Mobile Transport Layer: Traditional TCP, Indirect TCP, Snooping TCP, Mobile TCP, Fastretransmit / fast recovery, Transmission / time-out freezing, Selective retransmission, Transaction oriented TCP. Wireless Application Protocol: Architecture, Wireless datagram protocol, Wireless transportlayer security, Wireless transaction protocol, Wireless session protocol, Wireless application environment, Wireless markup language, WML Script, Wireless telephony application, Example stacks with WAP.

TEXT BOOK: 1. J.Schiller, “Mobile communications”, Addison-Wesley, 2003

REFERENCE BOOKS: 1. William Stallings, “Wireless Communication Networks”, Pearson Education. 2. UWE Hansmann, LotherMerk, Martin S.Nicklous, Thomas Stober, “Principles of Mobile

Computing”, 2nd

Edition.

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HIGH SPEED NETWORKS

14CS 804/B



UNIT – I

HIGH SPEED NETWORKS: Frame Relay Networks – Asynchronous transfer mode – ATM Protocol Architecture,ATM logical Connection, ATM Cell – ATM Service Categories – AAL. High SpeedLAN’s: Fast Ethernet, Gigabit Ethernet, Fibre Channel – Wireless LAN’s.

UNIT – II

CONGESTION AND TRAFFIC MANAGEMENT: Queuing Analysis‐ Queuing Models – Single Server Queues – Effects of Congestion –Congestion Control – Traffic Management – Congestion Control in Packet SwitchingNetworks – Frame Relay Congestion Control.

UNIT – III

TCP AND ATM CONGESTION CONTROL: TCP Flow control – TCP Congestion Control – Retransmission – Timer Management –Exponential RTO backoff – KARN’s Algorithm – Window management – Performance ofTCP over ATM. Traffic and Congestion control in ATM – Requirements – Attributes –Traffic Management Frame work, Traffic Control – ABR traffic Management – ABR ratecontrol, RM cell formats, ABR Capacity allocations – GFR traffic management.

UNIT – IV

INTEGRATED AND DIFFERENTIATED SERVICES: Integrated Services Architecture – Approach, Components, Services‐ QueuingDiscipline, FQ, PS, BRFQ, GPS, WFQ – Random Early Detection, DifferentiatedServices. PROTOCOLS FOR QoS SUPPORT: RSVP – Goals & Characteristics, Data Flow, RSVP operations, Protocol Mechanisms –Multiprotocol Label Switching – Operations, Label Stacking, Protocol details – RTP –Protocol Architecture, Data Transfer Protocol, RTCP. Text Books:

1 .William Stallings, “HIGH SPEED NETWORKS AND INTERNET”, Pearson

Education,Second Edition, 2002.

References: 1 . Warland&PravinVaraiya, “HIGH PERFORMANCE

COMMUNICATIONNETWORKS”, Jean Harcourt Asia Pvt. Ltd., II Edition, 2001.

2. IrvanPepelnjk, Jim Guichard and Jeff Apcar, “MPLS and VPN architecture”, CiscoPress,

Volume 1 and 2, 2003.

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ADHOC SENSOR NETWORKS

14CS804/C



UNIT - I (20 periods)

ROUTING:Cellular and Ad hoc wireless networks – Issues of MAC layer and Routing – Proactive, Reactive and Hybrid Routing protocols – Multicast Routing – Tree based and Meshbased protocols – Multicast with Quality of Service Provision. QUALITY OF SERVICE: Real-time traffic support – Issues and challenges in providing QoS –Classification ofQoS Solutions – MAC layer classifications – QoS Aware Routing Protocols –Ticketbased and Predictive location based Qos Routing Protocols.

UNIT - II

(13 Periods) ENERGY MANAGEMENT AD HOC NETWORKS: Need for Energy Management – Classification of Energy Management Schemes –Battery Management and Transmission Power ManagementSchemes – Network Layerand Data Link Layer Solutions – System power Managementschemes.

UNIT - III (14 periods)

MESH NETWORKS: Necessity for Mesh Networks – MAC enhancements – IEEE 802.11s Architecture –Opportunistic Routing – Self Configuration and Auto Configuration - Capacity Models –Fairness – Heterogeneous Mesh Networks – Vehicular Mesh Networks.

UNIT - IV

(15 periods) SENSOR NETWORKS: Introduction – Sensor Network architecture – Data Dissemination – Data Gathering –MAC Protocols for sensor Networks – Location discovery – Quality of SensorNetworks– Evolving Standards – Other Issues – Recent trends in Infrastructure less Networks.

TEXT BOOK: 1. C. Siva Ram Murthy and B.S.Manoj, “Ad hoc Wireless Networks – Architectures and

Protocols’, Pearson Education, 2004. REFERENCES: 1. Feng Zhao and Leonidas Guibas, “Wireless Sensor Networks”, Morgan Kaufman

Publishers, 2004. 2. C.K.Toh, “Adhoc Mobile Wireless Networks”, Pearson Education, 2002. 3. Thomas Krag and SebastinBuettrich, ‘Wireless Mesh Networking’, O’Reilly

Publishers, 2007.

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STORAGE AREA NETWORKS

14CS804/D

Lectures : 4 Periods/Week, Continuous Assessment : 40


UNIT – I

(14 periods) Introduction. What Storage Networking Is and What it can mean to you. Benefits: What to Expect from SANs. Leading up to SANs: One view of Data center evolution. KillerApps for SANs.

UNIT – II (14 periods)

Storage Networking Architecture.The Storage in Storage Networking. The Network in Storage Networking

UNIT – III (14 periods)

Basic Software for Storage Networking.Advanced Software for Storage Networking. Enterprise Backup Software for Storage Area Networks.

UNIT –IV (14 periods)

Adopting Storage Networking.Managing SANs. TEXT BOOK: 1. Storage Area Network Essentials:A complete Guide to Understanding and Implementing SANs(HardCover) By Richard Barker, Paul MassigliarBy Wiley 2001. REFERENCE BOOKS: 1. Storage Networks Explained: Basics and Application of Fibre Channel SAN, NAS iSCSI andInfiniBand By Ulf Troppens, Rainer Erkens, Wolfgang Miiller Wiley 2004. 2. Using SANs and NAS ByW.Curtis Preston, Mike Loukides.

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PROJECT WORK 14CSPR801

Lectures : 4 Periods/Week, Tutorial: 1 Continuous Assessment : 40


The Project work shall be carried out by a batch consisting not more than four students for one semester. It should help the students to comprehend and apply different theories and technologies that they have learnt through and are learning. It should lead to a substantial result as a comparative study, a new application of the technologies available or some extension to the works carried out by some researcher and published in referred journals. Each batch must carryout the analysis, design, implementation and testing of the entire project basing on the Software Engineering principles. There shall be a total of four reviews made by the batch regarding:

1. 0th

Review : The idea/concept which forms the basis for their

project shall be presented to the guide, concerned in

charge and classmates and shall get the approval for

1

st Review

continuation.

2. : The analysis and design carried out.

3. 2nd

Review : The implementation and the testing done.

4. 3rd

Review : Over all Presentation of the work carried out and the

results found out for the valuation under the internal

assessment.

A comprehensive report on the lines of IEEE Format is to be submitted at the end of the semester, which is certified by the concerned guide and the HOD.

There shall be an external guide appointed by the University to make an assessment and to carryout the Viva-Voce examination.

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BIG DATA ANALATICSLAB

14CSL801

Lectures : 4 Periods/Week, Tutorial: 1 Continuous Assessment : 40


1.Write the Steps to Installation of Hadoop software 2. Demonstration of Hadoop map reduce concept 3. Demonostration of NO SQL Database 4. Demonostration of Hbase example 5.Demonostration of Hive example 6.Demonostration of Pig example 7.Demonostration of Cassandra example

industrial management& entrepreneurship development ... · introduction to hadoop –open...

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