An Information-Theoretic Framework for Process Structure and Data Mining (Articolo in rivista)

Type
Label
  • An Information-Theoretic Framework for Process Structure and Data Mining (Articolo in rivista) (literal)
Anno
  • 2007-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.4018/jdwm.2007100106 (literal)
Alternative label
  • Gianluigi Greco; Antonella Guzzo; Luigi Pontieri (2007)
    An Information-Theoretic Framework for Process Structure and Data Mining
    in International journal of data warehousing and mining; Idea Group Publishing, Hershley [PA] (Stati Uniti d'America)
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Gianluigi Greco; Antonella Guzzo; Luigi Pontieri (literal)
Pagina inizio
  • 99 (literal)
Pagina fine
  • 119 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.igi-global.com/article/international-journal-data-warehousing-mining/1796 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume
  • 3 (literal)
Rivista
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#pagineTotali
  • 21 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroFascicolo
  • 4 (literal)
Note
  • Google Scholar (literal)
  • Scopu (literal)
  • PubZone (literal)
  • ISI Web of Science (WOS) (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • University of Calabria, Italy; University of Calabria, Italy; Institute of High Performance Computing and Networks, Italy (literal)
Titolo
  • An Information-Theoretic Framework for Process Structure and Data Mining (literal)
Abstract
  • Mining process logs has been increasingly attracting the data mining community, due to the chances the development of process mining techniques can offer to the analysis and design of complex processes. Currently, these techniques focus on \"structural\" aspects by only considering which activities were executed and in which order, and disregard any other kind of data usually kept by real systems (e.g., activity executors, parameter values, and time-stamps). In this article, we aim at discovering different process variants by clustering process logs. To this purpose, an information-theoretic framework is used to simultaneously cluster the logged process traces, encoding structural information, as well as a number of performance metrics associated with them. Each cluster is equipped with a specific model, so providing the analyst with a compact and handy description of major execution scenarios for the process (literal)
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