Data Warehousing, Data Mining & OLAP,Berson

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This definitive, up-to-the-minute reference provides strategic, theoretical and practical insight into three of the most promising information management technologies--data warehousing, online analytical processing (OLAP), and data mining--showing how these technologies can work together to create a new class of information delivery system: the Information Factory. It comprehensively covers data warehouse design (using various approaches, models and indexing techniques), relational data base mining, data warehousing on the Web, and data replication. Several chapters discuss application development with popular OLAP tools. Key Features: The main premise of this book is that, to date, several technologies have matured independently of one another, and no one as yet has thought a great deal about how to put the pieces together. This book reveals how the technologies and architectures work together adn what value they provide to the end user. This book presents the big picture by showing the business person how a data warehouse can be made useful to him or her. Another unique aspects of this book is that it contains a lot of material, some of which can be found in various vendor publications and in specialized research and trade literature.

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: Alex Berson is a senior information technology architect with over 20 years of experience in various areas of information technology including distributed client/server computing, database systems, parallel computing systems, object technology, data communications, and machine learning. He has successfully designed and implemented several large-scale data warehousing projects for major financial services companies. He is the author of several best-selling McGraw-Hill books, including Client/Server Architecture/2E and Sybase and Client/Server Computing.Stephen J. Smith is a director and architect responsible for the creation and delivery of two data mining products over the last decade?one for parallel supercomputers and the second for data warehouses with multidimensional databases. He is a well-respected expert in the field of data mining and their integration with the data warehouse. Table of Content: 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