Titlebook: Knowledge Discovery for Business Information Systems; Witold Abramowicz,Jozef Zurada Book 2002 Springer Science+Business Media New York 20

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书目名称Knowledge Discovery for Business Information Systems影响因子(影响力)




书目名称Knowledge Discovery for Business Information Systems影响因子(影响力)学科排名




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书目名称Knowledge Discovery for Business Information Systems网络公开度学科排名




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书目名称Knowledge Discovery for Business Information Systems被引频次学科排名




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书目名称Knowledge Discovery for Business Information Systems年度引用学科排名




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书目名称Knowledge Discovery for Business Information Systems读者反馈学科排名

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HERTZ

Amalgamation of Statistics and Data Mining Techniques: Explorations in Customer Lifetime Value Modeal networks) can be understood—and the interesting patterns captured in these models appropriately used in a business context—by bringing to bear statistical formalisms and business domain knowledge. The work will be presented in the context of lifetime value modeling for GTE cellular telephone subscribers.

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Hybrid Methodology of Knowledge Discovery for Business Information,blem of visualization of multidimensional data (further called .). In the last part of the text, using a set of business data extracted from a large anonymous database, various machine learning algorithms are used to exemplify hybrid (combined) extraction of useful knowledge.

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Multidimensional Business Process Analysis with the Process Warehouse,im to analyse and improve business processes continuously. This huge historic database, prepared for analysis purposes, enables process analysts to receive comprehensive information on business processes very quickly, at various granularity levels, from various, multidimensional points of view, over a long period of time.

Myofibrils

Book 2002pensive manner. In addition, the amount of datacollected and warehoused in all industries is growing every year at aphenomenal rate. Nevertheless, our ability to discover critical,non-obvious nuggets of useful information in data that could influenceor help in the decision making process, is still l

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Dealing with Dimensions in Data Warehousing,onship of a correct derivation of tables to so-called join trees known from relational theory is shown. The same principles of correctness testing are applicable on specifications of views over dimensions as well as on queries over DWs designed as constellations with explicit dimension hierarchies.