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Data Mining in Grid Computing Environments by Werner Dubitzky

By Werner Dubitzky

Dependent round 11 foreign actual existence case reviews and together with contributions from prime specialists within the box this groundbreaking ebook explores the necessity for the grid-enabling of knowledge mining functions and offers a entire learn of the know-how, suggestions and administration talents essential to create them. This ebook presents a simultaneous layout blueprint, consumer consultant, and study schedule for present and destiny advancements and may attract a wide viewers; from builders and clients of knowledge mining and grid expertise, to complicated undergraduate and postgraduate scholars attracted to this box.

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Chapter 5 is entitled ‘Mining for misconfigured machines in grid systems’. This chapter describes the Grid Monitoring System (GMS) – a system that adopts a distributed data mining approach to detection of misconfigured grid machines. Chapter 6 is entitled ‘FAEHIM: federated analysis environment for heterogeneous intelligent mining’. It describes the FAEHIM toolkit, which makes use of Web services composition, with the widely deployed Triana workflow environment. Most of the Web services are derived from the Weka data mining library of algorithms.

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G. g. ). 4. r KDD applications. Knowledge discovery applications built upon the functionalities provided by the underlying grid environments, the Knowledge Grid framework or higher-level data analysis services. 5. The discussed architectural approach is flexible in allowing KDD applications to be built upon data analysis services and upon Knowledge Grid services. Moreover, these services can be composed together with basic services provided by a generic grid toolkit to develop KDD applications.

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