Table of contents PART I: FOUNDATION Chapter 1 Introduction to Data Warehousing Chapter 2 Client/Server Computing Model and Data Warehousing Chapter 3 Parallel Processors and Cluster Systems Chapter 4 Distributed DBMS Implementations Chapter 5 Client/Server RDBMS Solutions PART II: DATA WAREHOUSING Chapter 6 Data Warehousing Components Chapter 7 Building a Data Warehouse Chapter 8 Mapping the Data Warehouse to a Multiprocessor Architecture Chapter 9 DBMS Schemas for Decision Support Chapter 10 Data Extraction, Cleanup, and Transformation Tools Chapter 11 Metadata PART III: BUSINESS ANALYSIS Chapter 12 Reporting and Query Tools and Applications Chapter 13 On-Line Analytical Processing (OLAP) Chapter 14 Patterns and Models Chapter 15 Statistics Chapter 16 Artificial Intelligence PART IV: DATA MINING Chapter 17 Introduction to Data Mining Chapter 18 Decision Trees Chapter 19 Neural Networks Chapter 20 Nearest Neighbor and Clustering Chapter 21 Genetic Algorithms Chapter 22 Rule Induction Chapter 23 Selecting and Using the Right Technique PART V: DATA VISUALIZATION AND OVERALL PERSPECTIVE Chapter 24 Data Visualization Chapter 25 Putting It All Together Appendices: A: Data Visualization B: Big Data--Better Returns: Leveraging Your Hidden Data Assets to Improve ROI C: Dr E.F. Codd`s 12 Guidelines for OLAP D: Mistakes for Data Warehousing Managers to Avoid Printed Pages: 638. Bookseller Inventory # 17312