A Data Mining-based Framework for Grid-Workflow Management (Contributo in atti di convegno)

Type
Label
  • A Data Mining-based Framework for Grid-Workflow Management (Contributo in atti di convegno) (literal)
Anno
  • 2005-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1109/QSIC.2005.2 (literal)
Alternative label
  • Congiusta Antonio; Greco Gianluigi; Guzzo Antonella; Manco Giuseppe; Pontieri Luigi; Saccà Domenico; Talia Domenico (2005)
    A Data Mining-based Framework for Grid-Workflow Management
    in First International Workshop on Grid and Peer-to-Peer Based Workflows, Melbourne, Australia, 19-20 September 2005
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Congiusta Antonio; Greco Gianluigi; Guzzo Antonella; Manco Giuseppe; Pontieri Luigi; Saccà Domenico; Talia Domenico (literal)
Pagina inizio
  • 349 (literal)
Pagina fine
  • 356 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.computer.org/portal/web/csdl/doi/10.1109/QSIC.2005.2 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume
  • QSIC, pp.349-356, Fifth International Conference on Quality Software (QSIC'05) (literal)
Note
  • ISI Web of Science (WOS) (literal)
  • Scopu (literal)
  • Google Scholar (literal)
  • DBLP (literal)
  • INSPEC (literal)
  • IEEE Xplore digital library (literal)
  • PubZone (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • University of Calabria, Italy; University of Calabria, Italy; Dept. of Mathematics, University of Calabria, Italy; ICAR - National Research Council, Italy; ICAR - National Research Council, Italy; ICAR - National Research Council, Italy; ICAR - National Research Council, Italy (literal)
Titolo
  • A Data Mining-based Framework for Grid-Workflow Management (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 0-7695-2472-9 (literal)
Abstract
  • In this paper we investigate on the exploitation of data mining techniques to analyze data coming from the enactment of workflow-based processes in a service-oriented Grid infrastructure. The extracted knowledge allows users to better comprehend the behavior of the enacted processes, and can be profitably exploited to provide advanced support to several phases in the life-cycle of workflow processes, including (re-)design, matchmaking, scheduling and performance monitoring. To this purpose, we focus on recent data mining techniques specifically aimed at enabling re- fined analyzes of workflow executions. Moreover, we introduce a comprehensive system architecture that supports the management of Grid workflows by fully taking advantage of such mining techniques. (literal)
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