Data Warehousing Data Mining And Olap Alex Berson Pdf Merge
Fas t, accu rat e and sca lab le dat a ana lys is tec hni que s are nee ded to ext ract use ful information from huge pile of data. Data warehouse is a single, integrated source of decision su pp or t in fo rm at io n fo rm ed by co ll ect in g da ta fr om mu lt ip le so ur ce s, in te rn al to th e organization as well as external, and transforming and summarizing this information to enable improved decision making. Data warehouse is designed for easy access by users to large amounts of information, and data access is typically supported by specialized analytical tools and app lica tio ns. Typ ica l app lic ati ons inc lud e dec isi on su ppo rt sy ste ms and exe cut ion information system. Data mining is the exploration and analysis of large quantities of data in order to disco ver valid, nove l, poten tially useful, and ultim ately understan dable patterns in data.
• • 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.
Data warehousing, data mining, and OLAP / Alex Berson, Stephen J. McGraw-Hill series on data warehousing and data management.
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. Hindi malayalam dictionary pdf. 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. 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.