Data Warehousing and Data Mining

Unit No.

CONTENT

1

Introduction to Data Warehousing ⇗:

• Introduction to Data Warehousing, Concept of Data Warehouse, DBMS versus Data Warehouse, Data Marts, Metadata, Multidimensional Data Model, Multidimensional Database, Data Warehouse Measures (their categorization and computation), and Multidimensional Database Hierarchies.

2

Data Warehouse System ⇗:

• Data Warehouse Architecture, Three-Tier Data Warehouse Architecture, Operations in OLAP, Advantages of OLAP over OLTP, OLAP Guidelines, Multidimensional versus Multirelational OLAP, Categories of Tools, and OLAP Tools and the Internet.

3

Introduction to Data Mining ⇗:

• Basic concepts of Data Mining; Data Mining primitives: Task-relevant data, mining objectives, measures, and identification of patterns; KDD versus Data Mining; Data Mining tools and applications. Data Mining Query Languages: data specification, knowledge specification, hierarchy specification, pattern presentation and visualization, Data Mining languages, and standardization of Data Mining. Architectures of Data Mining Systems.

4

Data Mining Techniques ⇗:

• Association rules from transaction and relational databases, correlation analysis, classification and prediction using decision tree induction. Introduction to clustering techniques: partitioning method and hierarchical method.

5

Overview of Advanced Features of Data Mining ⇗:

• Mining complex data objects, spatial databases, multimedia databases, time-series and sequence data, mining text databases, and mining the World Wide Web.