MCA22

Electronic Commerce/Data Mining and Data Warehousing
Year / Semester: 
6th Semester

MCA-22  Electronic Commerce 

Unit 1 : Building Blocks of Electronic Commerce
Introduction, internet and networking technologies, Internet and network protocols, web server scalability, software technologies for building E-commerce applications, distributed objects, object request brokers, component technology, web services, web application architectures, BizTalk
framework Compliant Server

Unit 2 : Security of E-commerce transactions
Review of cryptographic tools, authentication, signatures, observers, anonymity, privacy, traceability, key certification, management and escrow

Unit 3 : Payment protocols and standards
Smart card, e-cash, e-wallet technologies, electronic money and electronic payment systems, business models for electronic commerce, electronic marketplaces, auctions and other market mechanisms, design of auctions, optimization algorithms for marketplaces, multi-agent systems.

Unit 4 : Global eCommerce and Law
Cyber law in India. Comparative evaluation of Cyber laws of certain countries.

 

Suggested readings:

1. Ravi Kalakota, Andrew Winston, “Frontiers of Electronic Commerce”, Addison Wesley.
2. Bajaj and Nag, “E-Commerce the cutting edge of Business”, TMH
3. P. Loshin, John Vacca, “Electronic commerce”, Firewall Media, New Delhi

 


MCA-22 Data Mining and Data Warehousing

Unit 1:  Introduction to Data Mining :
 Motivation and  importance, What is Data Mining, Relational  Databases, Data Warehouses, Transactional Databases, Advanced Database Systems and Advanced Database Applications, Data  Mining  Functionalities,  Interestingness  of  a  pattern  Classification  of  Data  Mining Systems, Major issues in Data Mining.

Unit 2 : Data Warehouse and OLAP Technology for Data Mining
What is a Data Warehouse?  Multi-Dimensional Data Model,  Data Warehouse Architecture, Data   Warehouse   Implementation,   Development   of   Data   Cube   Technology,   Data Warehousing to Data Mining

Unit 3 : Data Preprocessing
Why Pre-process the Data?  Data Cleaning,  Data Integration and Transformation
Data Reduction,  Discretization and Concept Hierarchy Generation

Unit 4 : Data Mining Primitives, Languages and system Architectures
Data Mining Primitives: What defines a Data Mining Task? A Data Mining query language Designing Graphical Use Interfaces Based on a Data Mining Query language  Architectures of Data Mining Systems

Unit 5 : Concept Description: Characterization and comparison
What is Concept Description? Data Generalization and summarization-based Characterization, Analytical Characterization: Analysis of Attribute Relevance, Mining Class Comparisons: Discriminating between different  Classes, Mining Descriptive Statistical Measures in large Databases

Unit 6 : Mining Association rule in large Databases
Association Rule Mining, Mining Single -Dimensional Boolean Association Rules from Transactional Databases,  Mining Multilevel Association Rules from Transaction Databases, Mining Multidimensional Association Rules from Relational Databases and Data Warehouses,  From Association Mining to Correlation Analysis,  Constraint-Based Association Mining

Unit 7 : Classification and prediction
Concepts and Issues regarding  Classification and Prediction, Classification by Decision Tree Induction, Bayesian Classification, Classification by  Backpropagation, Classification Based on Concepts from Association Rule Mining,  Other Classification Methods like k-Nearest Neighbor Classifiers, Case-Based Reasoning, Generic Algorithms, Rough Set Approach, Fuzzy Set Approaches,  Prediction,  Classifier Accuracy

Unit 8 : Cluster Analysis
What is Cluster Analysis?  Types of Data in Cluster Analysis,  A Categorization of Major Clustering Methods

Suggested readings:
1. Berson, “Data Warehousing, Data-Mining & OLAP”, TMH
2. Mallach, “Decision Support and Data Warehousing System”, TMH
3. Bhavani Thura-is-ingham, “Data-Mining Technologies, Techniques Tools & Trends”, CRC Press
4. Navathe, “Fundamental of Database System”, Pearson Education
5. Margaret H. Dunham, “Data-Mining. Introductory & Advanced Topics”, Pearson Education
6. Pieter Adriaans, Dolf Zantinge, “Data-Min ing”, Pearson Education