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Digging deep into the data mine with DataMiningGrid

 
: Stankovski, V.; Swain, M.; Niessen, T.; Wegener, D.; Röhm, M.; Trnkoczy, J.; May, M.; Franke, J.; Schuster, A.; Dubitzky, W.

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Postprint urn:nbn:de:0011-n-841234 (295 KByte PDF)
MD5 Fingerprint: c0552c6ea25712a1b21b03296c1f8049
© 2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Erstellt am: 14.11.2008


IEEE Internet Computing 12 (2008), Nr.6, S.69-76
ISSN: 1089-7801
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
As modern data mining applications increase in complexity, so too do their demands for resources. Grid computing is one of several emerging networked computing paradigms promising to meet the requirements of heterogeneous, large-scale, and distributed data mining applications. Despite this promise, there are still too many issues to be resolved before grid technology is commonly applied to large-scale data mining tasks. To address some of these issues, the authors developed the DataMiningGrid system. It integrates a diverse set of programs and application scenarios within a single framework, and features scalability, flexible extensibility, sophisticated support for relevant standards and different users.

: http://publica.fraunhofer.de/dokumente/N-84123.html