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Basic Concepts of Data Warehousing
Introduction, Meaning and characteristics of Data Warehousing, Online Transaction Processing (OLTP), Data
Warehousing Models, Data warehouse architecture & Principles of Data Warehousing Data Mining.
Building a Data Warehouse Project
Structure of the Data warehouse, Data warehousing and Operational Systems, Organizing for building data
warehousing, Important considerations – Tighter integration, Empowerment, Willingness Business
Considerations: Return on Investment Design Considerations, Technical Consideration, Implementation
Consideration, Benefits of Data warehousing.
Managing and Implementing a Data Warehouse Project
Project Management Process, Scope Statement, Work Breakdown Structure and Integration, Initiating a data
warehousing project Project Estimation, Analyzing Probability and Risk, Managing Risk: Internal and External,
Critical Path Analysis.
What is Data mining (DM)? Definition and description, Relationship and Patterns, KDD vs Data mining,
DBMS vs Data mining, Elements and uses of Data Mining, Measuring Data Mining Effectiveness :
Accuracy,Speed & Cost Data Information and Knowledge, Data Mining vs. Machine Learning, Data Mining
Models. Issues and challenges in DM, DM Applications Areas.
Techniques of Data Mining
Various Techniques of Data Mining Nearest Neighbour and Clustering Techniques, Decision Trees, Discovery
of Association Rules, Neural Networks, Genetic Algorithm.
Need for OLAP, OLAP vs. OLTP Multidimensional Data Model Multidimensional verses Multirelational
OLAP Characteristics of OLAP: FASMI Test (Fast, Analysis Share, Multidimensional and Information),
Features of OLAP, OLAP Operations Categorization of OLAP Tools: MOLAP, ROLAP
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