Subject Title: INTERNET PROGRAMING AND WEB DESIGNING Course Number: 10CSEAC19 Number of Credits : 4 Subject Description: Thiscourse presents the Internet basics, XML, Java script and ASP concepts. Goal: To enable the students to learn the principles of Internet programming. Objectives: On successful completion of the course the students should have: Gained knowledge in Internet basics and XML Understood Java Script and ASP programming. UNIT I Internet Basics: Introduction – Getting Connection – Services - Mail – News Groups – FTP – Telnet – WAIS – Archive – Gopher – Veronica – HTML: Basic Structural Elements and their usage – Traditional text and formatting – Using tables, images, frames, links and forms – Merging multimedia and plug-Ins – Cookies – Creating dynamic HTML pages – Cascading Style Sheets UNIT II XML: Need for XML – Documentation – Elements and Attributes – Valid Documents – Objects Checking Validity – Links – Advanced Addressing – Viewing – Processing – XML Document – Object Model Using Meta Data – Rendering XML with XSL UNIT III SCRIPTING LANGUAGE- Scripting basics - Java Script programming -Dynamic HTML – validation process Cascading style sheets - Object model and collections - Event model - Filters and Transitions - ActiveX controls - Multimedia - Client side scripting UNIT IV Active server pages: Introduction – client side scripting versus server side scripting – using personal web server and internet information server – active server page objects – an example – server side activex components – file system objects – session tracing and cookies – databases - SQL, Microsoft UDA and ADO – accessing a database from an active server page UNIT V ASP.NET: Introduction to .Net Framework – Components of .Net – ASP.Net - .net Web services - .net Languages - .net Data Services ADO.Net REFERENCE BOOKS 1. Peter Kent, “10 Minute Guide to the Internet“, Prentice Hall of India, 1996. 2. Java How to program, Deitel & Deitel, Prentice Hall 1999. 3. Scott Mitchell and James Atkinson, “Teach Yourself XML in 21 days”, Sams Publishing, 1999. 4. Nicholas Chase, “ASP 3.0 from Scratch”, Prentice Hall India Ltd, 2000. 5. Bill Evjen, Scott Hanselman and Devin Rader, “Professional ASP.NET 3.5”, Wiley Publishing Ltd., 2008. Message Driven Bean – EJB Deployment. William Crawford.” The Complete Reference J2EE”. Unit III Java Remote Method Invocation: Distributed Application Architecture – Client proxy and Server Proxy – Remote Interface and Passing Objects – RMI process . Entity Java Bean. David Flanagan. Phil Kanna. 4.Enterprise Applications Strategy . 3. Jamie Jaworskie.Session Java Bean. REFERENCE BOOKS 1. Contents: Unit I Introduction to Enterprise Java Programming: Distributive Systems – Multi-Tier Architecture of J2EE – Clients and Client Tier – Web Tier – EJB Tier – EIS Tier – J2EE best practices . Unit II Database Programming in Java: Overview of the JDBC Process .Object Serialisation and RMI. O’Reilly SPD.Transaction Processing.Components of a JSP page .JSP tags .Expressions – Scriptlets – Directives – Declarations .Initialization – Deployment – Reading Client Data – Reading HTTP Request Headers – Cookies . Unit IV Java Servlets: Basics – Benefits of Servlets . Java Servlets. Java Server Pages and Enterprise Java Beans. Techmedia SAMS.ResultSet – Interacting with the database . 2008. Unit V Enterprise Java Beans: Introduction – EJB Container – Classes and Interfaces – JAR files .Working with JSP. IV edition. 2003. Database Connectivity in Java.JDBC Driver types – Database Connection.Databases and non-HTML content. Kris Magnusson.” The Complete Reference JSP 2. Objectives: On successful completion of the course the students should have: Gained knowledge in web page designing.” Java Enterprise in a Nutshell”.Remote Object Activation . Jim Keogh. Java Server Pages . Remote Method Invocation in Java.Subject Title: ENTERPRISE JAVA PROGRAMMING Course Number: 10CSEAC20 Number of Credits: 4 Subject Description: This course presents a detailed study on the Multi-tier Enterprise Architecture of Java. 2010. .Deployment Descriptor .Session Management. 2005.JDBC/ODBC Bridge – Statement Objects – The Connection Interface . Tata McGrawHill Publishing Company Ltd. Goal: To enable the students learn the advanced concepts of Java programming.”Java 2 Platform Unleashed”.Session Tracking .JSP Overview . Tata McGrawHill Publishing Company Ltd. 2. Jim Farley. component designing and designing distributed applications using Java.Defining and using Remote objects .JDBC Concepts .0”. 2005 Number of Credits : 4 . Intelligent Miner. Data Mining. Standard Deviation – Regression -Correlation hypothesis Testing . Unit I Statistics for Data Engineers: Mean. problems and trends Unit IV Business Intelligence . 2. Data Warehouse Tools . Wiley. Bruce. Classification and Clustering .Cognos. Data Models. Techniques. 2007. 2007. Joe Casertra . “The Data Warehouse ETL Toolkit: Practical Techniques for Extracting. ‘T’ Test .Star Schema. Business Object. BI Tool. ‘T’ Distribution Unit II Data Base. F Measure . Nitin R.Data Warehouse – Definition . “Data Analytics: The Time Is Now!” .Data Preprocessing-Visualization-Variable reduction.Analyze and predict results based on historical patterns-Apply statistical methods to economic data. Oracle Miner (Any one tool in depth) Reference Books 1. Data Warehouse. Schema .. “Data Mining for Business Intelligence: Concepts. Data Analytics and Business Intelligence Goal: To enable the students to learn the techniques of Data Analytics and methods of applying them to business Objectives: On successful completion of the course the students should have: Gained knowledge in Statistics for Data Engineers.Text Mining Unit III Data Analytics : Use statistical data analysis to drive fact-based decisions. Ralph Kimball. and Applications in Microsoft Office Excel with XLMiner”. Course of Dimensionality-Developing the intelligence model and conducting the analysis Unit V Tools for BI: Overview –Tools. Siebel. Patel and Peter C. Variance.Subject Title: Data Analysis and Business Intelligence Course Number: 10CSEAC21 Subject Description: This course aims at imparting knowledge in the area of Statistics for Data Engineers.definition. Relational Data Model. Wiley. 2004. 4.Definition . Data Mining : Database . Cindi Howson. Data Analytics and Business Intelligence.Data Mining: Stages. Median. Data Mining. Data Base. Galit Shmueli. Cleaning”.Star Schema – Data Warehouse . Principle components. “Successful Business Intelligence: Secrets to Making BI a Killer App” McGraw-Hill Osborne Media. 3.Build descriptive and predictive models and deploy over the organization. Rajendra Chaudhary. Data Warehouse. Gonzalez and Richard E. Algorithms and Architectures”. 1995. 2. . Goal: To enable the students to learn the basic functions.Realization for real time processing – Three-dimensional Filters REFERENCE BOOKS 1. Objectives: On successful completion of the course the students should have: Understood the Image processing. “Digital Image Processing”. principles and concepts of Image processing. “Fundamentals of Digital Image Processing”.Algebriac approach to restoration – Inverse and Wiener filtering – Finite impulse response Wiener filters – Other Fourier Transform Filters – Smoothing splines and Interpolation – Least square filters – Recursive and semirecursive filtering – Maximum entropy restoration – Bayesian methods – Coordinate transformation and Geometric correction – Blind deconvolution – Extrapolation of bandlimited signals UNIT IV Image Data compression: Fundamentals – Image compression models – Elements of information theory – Pixel coding – Predictive techniques – Transform coding theory – Transform coding of images – Hybrid coding and vector DPCM – Inter frame coding – Image coding in the presence of channel errors – Coding of two tone images – color and multi-spectral Image coding – Lossless and lossy compressions standards UNIT V Image Segmentation – Representation and Description – Recognition – Interpretation – Image analysis and Computer vision – Image reconstruction from Projections – Artificial Neural networks for color classification . McGraw Hill. Addison-Wesley Publishing Company. New Delhi. image data compression and image segmentation.Subject Title: IMAGE PROCESSING Course Number: 10CSEAE25 Number of Credits :4 Subject Description: This course presents the Introduction. Newyark. Anil K. image filtering and restoration. Jain. 1995. Rafael C. Newyark. 3. III Edition. image enhancement. Maher A. Woods. Inc. II Edition. Prentice-Hall of India Private Limited. Sid-Ahmed. 2008. image filtering and restoration Contents: UNIT I Introduction: Fundamental Steps in Image processing – Elements – Digital Image Fundamentals – Image representation – Modeling – Image enhancement – Image restoration – Image analysis – Image reconstruction from projections – Image data compression – Two-Dimensional Systems and Mathematical Preliminaries: Notation and definitions – Discrete and Fast Fourier Transform UNIT II Image Enhancement: Point operations – Enhancement by point processing – Histogram modeling – Spatial operations – Enhancement in Frequency Domain – Transform operations – Multispectral Image Enhancement – Color Image Enhancement UNIT III Image Filtering and Restoration: Degradation model – Diagonalization of circulant and block circulant matrices . Understood the image enhancement. “Image Processing – Theory. Subject Title: DATA MINING Course Number: 10CSEAE27 Number of Credits :4 Subject Description: This course presents the introduction to data mining. Data Mining – DBMS vs. “Data Mining”. 2010. Arun K. Goal: To enable the students to learn the basic functions. classification and web mining. Contents: UNIT I Introduction – Data mining as a subject – Data warehousing: Introduction – Definition – Multidimensional data model – OLAP operations – Warehouse schema – Data Warehousing architecture – Warehouse server – Meta data – OLAP engine – Data Warehouse Backend process – Other features UNIT II Data mining: Introduction – Definitions – KDD vs. Dolf Zantinge. principles and concepts of Data Mining Objectives: On successful completion of the course the students should have: Understood the data mining techniques. temporal and sequential data mining. Witold Pedrycz. Hyderabad. . Universities Press (India) Limited. Pieter Adriaans. Krzyszlof J Cios. 2008. 3. Pujari. 2. Springer. “Data mining Techniques”. “Data Mining: A Knowledge Discovery Approach”. 2009. DM – DM techniques – Association Rules: Concepts – Methods to discover Association rules – A priori algorithm – Partition algorithm – Pioneer search algorithm – Dynamic Item set Counting algorithm – FP-tree growth algorithm – Incremental algorithm – Border algorithm – Generalized association rule UNIT III Clustering techniques: Clustering paradigms – Partitining algorithm – K-Medeoid algorithms – CLARA – CLARANS – Hierarchical clustering DBSCAN – BIRCH – CURE – Categorical clustering algorithms – STIRR – ROCK – CACTUS – Other techniques: Introduction to neural network – Learning in NN – Genetic algorithm – Case studies UNIT IV Web mining: Basic concepts – Web content mining – Web structure mining – Web usage mining – text mining – text clustering UNIT V Temporal and Sequential Data mining: Temporal Association rules – Sequence Mining – The GSP algorithm – SPADE – SPIRIT – WUM – Spatial mining – Spatial mining tasks – Spatial clustering – Spatial trends REFERENCE BOOKS 1. clustering techniques. Addison Wesley. web mining.