Data Warehousing

Course Title: Data Warehousing and Data Mining
Course No. CSC 410
Nature of Course: Theory (3Hrs.)+Lab (3Hrs.)
Semester: VII
    

1. Unit - 1
• Concepts of Data Warehouse and Data Mining including its functionalities
• stages of Knowledge discovery in database(KDD)
• Setting up a KDD environment
• ssues in Data Warehouse and DataMining
• Application of Data Warehouse and Data Mining
2. Unit - 2
• DBMS vs. Data Warehouse
• Data marts
• Metadata
• Multidimensional data model
• Data Cubes
• Schemas for Multidimensional Database: Stars, Snowflakes and Fact Constellations
3. Unit - 3
• Data Warehouse Architecture
• Distributedand Virtual Data Warehouse
• Data Warehouse Manage
• OLTP
• OLAP
• MOLAP
• HOLAP
• types of OLAP
• servers
4. Unit - 4
• Computation of Data Cubes
• modeling: OLAPdata, OLAP queries
• Data Warehouse back end tools
• tuning and testing of Data Warehouse
5. Unit - 5
• Data Mining definitionand Task
• KDD versus Data Mining
• Data Mining techniques
• tools and application
6. Unit - 6
• Data mining query languages
• data specification
• specifying knowledge
• hierarchy specification
• pattern presentation & visualization specification
• data mining languages and standardization of data mining
7. Unit - 7
• Mining Association Rules in Large Databases:Association Rule Mining
• why Association Mining is necessary
• Pros and Cons of Association Rules
• Apriori Algorithm
8. Unit - 8
• Classification and Prediction: Issues Regarding Classification and Prediction
• Classification by Decision Tree Induction
• Introduction to Regression
• Types of Regression
• Introduction to clustering
• K-mean and K-Mediod Algorithms
9. Unit - 9
• Mining Complex Types of Data:Mining Text Databases
• Mining the World Wide Web
• Mining Multimedia and Spatial Databases

Laboratory Works: Cover all the concept of datawarehouse and mining mention in a course
Samples

1. Creating a simple data warehouse
2. OLAP operations: Roll Up, Drill Down, Slice, Dice throughSQL-Server
3. Concepts of data cleaning and preparing for operation
4. Association rule mining though data mining tools
5. Data Classification through data mining tools
6. Clustering through data mining tools
7. Data visualization through data mining tools