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Overview
Effective decision support systems (DSS) are quickly becoming key to businesses gaining a competitive advantage, and the effectiveness of these systems depends on the ability to construct, maintain, and extract information from data warehouses. While many still perceive data warehousing as a subdiscipline of management information systems (MIS), in fact many of its advances have and will continue to come from the computer science arena.
Intelligent Data Warehousing presents the state of the art in data warehousing research and practice from a perspective that integrates business applications and computer science. It brings the intelligent techniques associated with artificial intelligence (AI) to the entire process of data warehousing, including data preparation, storage, and mining. Part I provides an overview of the main ideas and fundamentals of data mining, artificial intelligence, business intelligence, and data warehousing. Part II presents core materials on data warehousing, and Part III explores data analysis and knowledge discovery in the data warehousing environment, including how to perform intelligent data analysis and the discovery of influential association patterns.
Bridging the gap between theoretical research and business applications, this book summarizes the main ideas behind recent research developments rather than setting forth technical details, and it presents case studies that show the how-to's of implementing these ideas. The result is a practical, first-of-its-kind book that brings together scattered research, unites MIS with computer science, and melds intelligent techniques with data warehousing.
Synopsis
Effective decision support systems (DSS) are quickly becoming key to businesses gaining a competitive advantage, and the effectiveness of these systems depends on the ability to construct, maintain, and extract information from data warehouses. While many still perceive data warehousing as a subdiscipline of management information systems (MIS), in fact many of its advances have and will continue to come from the computer science arena.
Intelligent Data Warehousing presents the state of the art in data warehousing research and practice from a perspective that integrates business applications and computer science. It brings the intelligent techniques associated with artificial intelligence (AI) to the entire process of data warehousing, including data preparation, storage, and mining. Part I provides an overview of the main ideas and fundamentals of data mining, artificial intelligence, business intelligence, and data warehousing. Part II presents core materials on data warehousing, and Part III explores data analysis and knowledge discovery in the data warehousing environment, including how to perform intelligent data analysis and the discovery of influential association patterns.
Bridging the gap between theoretical research and business applications, this book summarizes the main ideas behind recent research developments rather than setting forth technical details, and it presents case studies that show the how-to's of implementing these ideas. The result is a practical, first-of-its-kind book that brings together scattered research, unites MIS with computer science, and melds intelligent techniques with data warehousing.
Booknews
Chen (computer science, University of Nebraska-Omaha) summarizes the main ideas behind recent research and practice in data warehousing from a perspective that integrates business applications and computer science. Fundamentals of data mining, artificial intelligence, business intelligence, and data warehousing are overviewed, then core material on data warehousing is presented, in chapters on data preparation and preprocessing, building data warehouses, basics of materialized views, and advances in materialized views. Further chapters explore data analysis and knowledge discovery in the data warehousing environment, covering intelligent data analysis and integrated OLAP and data mining. Annotation c. Book News, Inc., Portland, OR (booknews.com)
Editorials
Chen (computer science, University of Nebraska-Omaha) summarizes the main ideas behind recent research and practice in data warehousing from a perspective that integrates business applications and computer science. Fundamentals of data mining, artificial intelligence, business intelligence, and data warehousing are overviewed, then core material on data warehousing is presented, in chapters on data preparation and preprocessing, building data warehouses, basics of materialized views, and advances in materialized views. Further chapters explore data analysis and knowledge discovery in the data warehousing environment, covering intelligent data analysis and integrated OLAP and data mining. Annotation c. Book News, Inc., Portland, OR (booknews.com)