Swarnandhra College of Engineering & Technology (Autonomous), Seetharampuram, Narsapur-534280 B.TECH/CSE/2014 SIXTH SEMESTER WEB TECHNOLOGIES (BTCS6T01) COURSE OBJECTIVES: 1. Describes the fundamentals of html tags and java script for web designing. 2. Acquire background knowledge on xml data storage. 3. Understands the basic knowledge of web servers with building servlets and jsp web applications. 4. Understands knowledge about database connectivity for web applications. COURSE OUTCOMES: 1. An ability to distinguish various static web pages and dynamic web pages using html, xml and java script. 2. An ability to review on xml data storage. 3. An ability to work on web servers with servlets and jsp web applications. 4. An ability to design web applications by using database connectivity. UNIT-I: HTML Introduction to html, structure of a html page, HTML basic tags, formatting tags, Lists, Tables, Images, image mapping ,links, forms, Frames, Cascading style sheets(T1) UNIT-II: Java script Introduction to java script, advantages, differences between java and java script data types, Objects in Java Script, java script events, form validation using Dynamic HTML. (T1) UNIT-III: XML Introduction to XML and its advantages on web, XML-Document type Definition, XML schemas, Document object model, XSLT, DOM and SAX. (T1) UNIT-IV: Web Servers and Servlets Tomcat web server, Introduction to Servlets: Lifecycle of a Servlet, The Servlet API, The javax.servlet Package, Reading Servlet parameters and Reading Initialization parameters. The javax.servlet HTTP package, Handling Http Request & Responses, Using Cookies-Session Tracking, Security Issues. (T2) UNIT-V: Introduction to JSP The Problem with Servlet. The Anatomy of a JSP Page, JSP Processing. JSP Application Design with MVC. JSP Application Development: Generating Dynamic Content Using Scripting Elements, Implicit JSP Objects, Conditional Processing – Displaying Values Using an Expression to Set an Attribute, Declaring Variables and Methods Error Handling and Debugging Sharing Data between JSP pages.(T2) UNIT-VI: Database Access Database Programming using JDBC, Studying Javax.sql.* package, Accessing a Database from a JSP Page, Application – Specific Database Actions. (T2)
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
Swarnandhra College of Engineering & Technology (Autonomous), Seetharampuram, Narsapur-534280
B.TECH/CSE/2014
SIXTH SEMESTER WEB TECHNOLOGIES (BTCS6T01)
COURSE OBJECTIVES:
1. Describes the fundamentals of html tags and java script for web designing.
2. Acquire background knowledge on xml data storage.
3. Understands the basic knowledge of web servers with building servlets and jsp web
applications.
4. Understands knowledge about database connectivity for web applications.
COURSE OUTCOMES:
1. An ability to distinguish various static web pages and dynamic web pages using html, xml and
java script.
2. An ability to review on xml data storage.
3. An ability to work on web servers with servlets and jsp web applications.
4. An ability to design web applications by using database connectivity.
UNIT-I: HTML
Introduction to html, structure of a html page, HTML basic tags, formatting tags, Lists, Tables,
Basic Behavioral Modeling - II: Use cases, Use case Diagrams, Activity
Diagrams. Advanced Behavioral Modeling: Event s and signals, state machines, processes and
Threads, time and space, state chart diagrams.
UNIT V
Architectural Modeling : Component, Deployment, Component diagrams and Deployment diagrams.
UNIT VI
Case Study: The Unified Library application.
TEXT BOOKS:
1. The Unified Modeling Language User Guide, Grady Booch, James Rumbaugh, Ivar Jacobson, Pearson
Education.
2. UML 2 Toolkit, Hans - Erik Eriksson, Magnus Penker, Brian Lyons, David Fado, WILEY -
Dreamtech India Pvt. Ltd.
Swarnandhra College of Engineering & Technology (Autonomous), Seetharampuram, Narsapur-534280
B.TECH/CSE/2014
DATA MINING AND DATA WAREHOUSING (BTCS6T03)
Course Objectives: Students will be enabled to understand and implement classical models and algorithms in data
warehousing and data mining. They will learn how to analyze the data, identify the problems, and
choose the relevant models and algorithms to apply. They will further be able to assess the strengths
and weaknesses of various methods and algorithms and to analyze their behavior. Course Outcomes: 1. understand why there is a need for data warehouse in addition to traditional operational
database systems; 2. identify components in typical data warehouse architectures; 3. design a data warehouse and understand the process required to construct one; 4. solve real data mining problems by using the right tools to find interesting patterns
UNIT –I: Introduction : What Motivated Data Mining? Why Is It Important, Data Mining—On What Kind of
Data, Data Mining Functionalities—What Kinds of Patterns Can Be Mined? Are All of the Patterns
Interesting? Classification of Data Mining Systems, Data Mining Task Primitives, Integration of a
Data Mining System with a Database or Data Warehouse System, Major Issues in Data Mining. (Han
& Kamber) UNIT –II: Data Pre-processing : Why Pre-process the Data? Descriptive Data Summarization, Data Cleaning,
Data Integration and Transformation, Data Reduction, Data Discretization and Concept Hierarchy
Generation. (Han & Kamber) UNIT –III: Data Warehouse and OLAP Technology: An Overview : What Is a Data Warehouse? A
Multidimensional Data Model, Data Warehouse Architecture, Data Warehouse Implementation, From
Data Warehousing to Data Mining. (Han & Kamber)
UNIT –IV: Classification : Basic Concepts, General Approach to solving a classification problem, Decision Tree
Induction: Working of Decision Tree, building a decision tree, methods for expressing an attribute
test conditions, measures for selecting the best split, Algorithm for decision tree induction. Model Over fitting: Due to presence of noise, due to lack of representation samples, evaluating the
performance of classifier: holdout method, random sub sampling, cross-validation, bootstrap. (Tan &
Vipin) UNIT –V Association Analysis: Basic Concepts and Algorithms : Introduction, Frequent Item Set generation,
UNIT –VI Cluster Analysis: Basic Concepts and Algorithms : What Is Cluster Analysis? Different Types of
Clustering, Different Types of Clusters, K-means, The Basic K-means Algorithm, K-means:
Swarnandhra College of Engineering & Technology (Autonomous), Seetharampuram, Narsapur-534280
B.TECH/CSE/2014
Additional Issues, Bisecting K-means, K-means and Different Types of Clusters, Strengths and
Weaknesses, K-means as an Optimization Problem, Agglomerative Hierarchical Clustering, Basic
Agglomerative Hierarchical Clustering Algorithm, Specific Techniques, DBSCAN, Traditional
Density: Center-Based Approach, The DBSCAN Algorithm, Strengths and Weaknesses. (Tan &
Vipin)
Text Books : 1. Introduction to Data Mining : Pang-Ning Tan & Michael Steinbach, Vipin Kumar, Pearson.
2. Data Mining concepts and Techniques, 3/e, Jiawei Han, Michel Kamber, Elsevier. Reference Books : 1. Data Mining Techniques and Applications: An Introduction, Hongbo Du, Cengage Learning.
2. Data Mining : Introductory and Advanced topics : Dunham, Pearson. 3. Data Warehousing Data Mining & OLAP, Alex Berson, Stephen Smith, TMH.
4. Data Mining Techniques, Arun K Pujari, Universities Press.