Author Description

This unique compendium elaborates the basic perceptions of data warehouses and data mining. The former part of the book covers concepts like introduction to data warehouses, the need for using such data warehouses and key terminologies used in this framework. The latter part of the book covers the data mining concepts and the data mining techniques used in various applications and also explains the machine learning techniques in detail with suitable examples wherever essential. The book is written in simple English and is user-friendly. Each chapter is modeled with several sample scenarios and illustrations wherever necessary. The complete contents of each chapter include chapter technical content, summary, key points to remember, and few case studies for class group discussions and problem-solving. This volume clearly benefits professionals, academicians, data analysts, machine-learning community, undergraduate and postgraduate students. Contents: About the Authors Introduction: Thrust Components Operational Databases OnLine Analytical Processing (OLAP) OLTP (OnLine Transaction Processing) Summary Basics of Data Operations: Data Aggregation Data Completeness Data Compression and Data Conversion Data Fragmentation Data Flow Diagram Data Dictionary & Data Dimension Summary Data Warehousing Basic Concepts: Bridging the Knowledge Gap between Operational Databases and Data Warehouses Need for Data Warehouses Subject-Oriented Integrated Non-Volatile Time-Variant Data Marts Decision Support Systems Executive Information Systems Data Warehouse Tools Summary Important Project Related References Data Warehouse Architecture: Design