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• • Title • Data warehousing, data mining, and OLAP /​ Alex Berson, Stephen J. Also Titled • Data warehousing, data mining &​ OLAP Author • Berson, Alex.

Other Authors • Smith, Stephen J. Published • New York: McGraw-Hill, c1997. Physical Description • xxvi, 612 p.: ill.; 25 cm. Series • Subjects • • • Contents • Ch.

Introduction to Data Warehousing • Ch. Client/​Server Computing Model and Data Warehousing • Ch. Parallel Processors and Cluster Systems • Ch. Distributed DBMS Implementations • Ch. Client/​Server RDBMS Solutions • Ch. Data Warehousing Components • Ch. Building a Data Warehouse • Ch.

Mapping the Data Warehouse to a Multiprocessor Architecture • Ch. DBMS Schemas for Decision Support • Ch. Data Extraction, Cleanup, and Transformation Tools • Ch. Metadata • Ch. Reporting and Query Tools and Applications • Ch. On-Line Analytical Processing (OLAP) • Ch.

Patterns and Models • Ch. Statistics • Ch. Artificial Intelligence • Ch. Introduction to Data Mining • Ch. Decision Trees • Ch.

Neural Networks • Ch. Nearest Neighbor and Clustering • Ch. Genetic Algorithms • Ch. Rule Induction • Ch. Selecting and Using the Right Technique • Ch. Data Visualization. Putting It All Together • App.

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Big Data - Better Returns: Leveraging Your Hidden Data Assets to Improve ROI • App. Codd's 12 Guidelines for OLAP • App. 10 Mistakes for Data Warehousing Managers to Avoid.

• Notes • Includes bibliographical references and index. Language • English ISBN •: Dewey Number • 005.74 Libraries Australia ID • Contributed by Get this edition.

Authors: Alex Berson and Stephen J. Smith Publisher: McGRAW-HILL (ISBN 0-07-006272-2) Data Warehousing, Data Mining, & OLAP, written by Alex Berson and Stephen J. Smith (Computing McGraw-Hill 1997), focuses on data delivery as a top priority in business computing today. The authors use the forward to specify the three areas of data warehousing to be covered in the book as 1) bringing data necessary for enhancing traditional information presentation technologies into a single source, 2) supporting online analytical processing (OLAP), and 3) the newest data delivery engine, Data Mining.

The book is broken into five parts, Foundation, Data Warehousing, Business Analysis, Data Mining, and Data Visualization and Overall Perspective. Each part goes into a tremendous amount of detail starting general and moving to the specific, detailing at least five long chapters within each section. The Foundation section begins by introducing the data warehouse, presenting an overview of client/server architectures and presenting parallel processors and cluster systems. The section continues by discussing distributed database management systems, and by individually offering an overview of major client/server RDBMS database environments such as Oracle, Informix, Sybase, IBM’s DB2, and Microsoft MS-SQL Server.

This section builds a tremendous foundation of warehousing technology by detailing hardware architectures, multiprocessing architectures, and RDBMS features and solutions. The second section, Data Warehousing, begins by detailing data warehousing components and the processes of building a data warehouse. This section of the book details mapping the warehouse to the parallel processing architectures, selecting database schemas for decision support, the process of extracting, cleaning, and transforming data, and describes meta data as a key component of supporting the knowledge workers. The chapters go into tremendous details, discussing tool requirements and offering a look at tool-by-tool vendor-based solutions. The Business Analysis section of this book begins by breaking reporting and query tools into categories including reporting tools, managed query tools, executive information system (EIS) tools, OLAP tools, and data mining tools. The authors talk about the need for developing reporting applications and then discuss many of the most recognized reporting and querying tools on the market today.

The chapters in this section also detail OLAP (what it is and and why it is necessary), introduces patterns and models for business analysis, explains different types of statistical analysis, and delves briefly into the technologies of expert systems and artificial intelligence. The fourth section, Data Mining, introduces the topic by discussing its motivation, measuring its effectiveness, and by defining the difference between discovery and prediction.

The first chapter in this section talks about the state of the data mining industry and compares the present technologies to that of days in the recent past. The rest of the chapters in this section discuss decision trees, neural networks, genetic algorithms and rule induction. The section wraps up by helping the reader to select and use the right tools. The final section, Data Visualization and Overall Perspectives pull together the information from the previous sections. In this section, the authors assume a basic understanding of what was delivered in the other sections.