By Zhengxin Chen
Potent selection aid platforms (DSS) are fast turning into key to companies gaining a aggressive virtue, and the effectiveness of those structures is determined by the facility to build, continue, and extract info from information warehouses. whereas many nonetheless understand facts warehousing as a subdiscipline of administration details platforms (MIS), in reality a lot of its advances have and should proceed to come back from the pc technological know-how arena.
Intelligent information Warehousing provides the cutting-edge in info warehousing study and perform from a viewpoint that integrates enterprise purposes and desktop technological know-how. It brings the clever options linked to man made intelligence (AI) to the complete technique of facts warehousing, together with facts guidance, garage, and mining. half I presents an outline of the most principles and basics of knowledge mining, synthetic intelligence, company intelligence, and information warehousing. half II provides center fabrics on info warehousing, and half III explores facts research and information discovery within the info warehousing setting, together with tips on how to practice clever facts research and the invention of influential organization patterns.
Bridging the space among theoretical examine and enterprise purposes, this publication summarizes the most rules in the back of contemporary study advancements instead of environment forth technical information, and it provides case reports that exhibit the how-to's of imposing those principles. the result's a pragmatic, first-of-its-kind ebook that brings jointly scattered study, unites MIS with laptop technology, and melds clever strategies with info warehousing.
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Additional resources for Intelligent data warehousing: from data preparation to data mining
Mining Your Web Site, Digital Press, Woburn, MA, 1999. , Frequently asked questions about data warehousing, DM Rev. Online, June, 2000. O’Leary, D. E. and R. Studer, Knowledge management: an interdisciplinary approach, IEEE Intell. , 16(1), 24–25, Jan/Feb, 2001. , DataMines for datawarehouses — data mining above, beside and within the warehouse to avoid the paradox of warehouse patterns (an Information Discovery, Inc. White Paper), 1999. , WCB McGraw-Hill, Boston, 2000. , Java in the database, Intell.
For example, one analysis framework presumes a desired outcome and looks for patterns that lead to that outcome, while a different analysis framework looks for behaviors that are not known a priori. 9 The future of data warehouses Although the area of data warehousing thrives and the potential for further growth is promising, a data warehouse project could be a great risk and is endangered by several factors. The reasons for the failure of a data warehousing project can be grouped into four categories (Vassiliadis, 2000): 1.
They are defined by logical rules and are actually views. For example, a table named “parent” with schema (child name, parent name) forms an EDB.
Intelligent data warehousing: from data preparation to data mining by Zhengxin Chen