Adaptive trace abstraction approach for predicting business process performances (Contributo in atti di convegno)

Type
Label
  • Adaptive trace abstraction approach for predicting business process performances (Contributo in atti di convegno) (literal)
Anno
  • 2013-01-01T00:00:00+01:00 (literal)
Alternative label
  • Bevacqua, Antonio; Carnuccio, Marco; Folino, Francesco; Guarascio, Massimo; Pontieri, Luigi (2013)
    Adaptive trace abstraction approach for predicting business process performances
    in 21st Italian Symposium on Advanced Database Systems, SEBD 2013, Roccella Jonica, Italy, 30 June 2013 - 4 July 2013
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Bevacqua, Antonio; Carnuccio, Marco; Folino, Francesco; Guarascio, Massimo; Pontieri, Luigi (literal)
Pagina inizio
  • 437 (literal)
Pagina fine
  • 444 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url
  • http://www.scopus.com/record/display.url?eid=2-s2.0-84903523723&origin=inward (literal)
Note
  • Scopu (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • Istituto Di Calcolo E Reti Ad Alte Prestazioni, Rende (literal)
Titolo
  • Adaptive trace abstraction approach for predicting business process performances (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn
  • 9781629939490 (literal)
Abstract
  • This paper presents an approach to the discovery of predictive process models, which combines a series of data mining techniques (ranging from pattern mining, to non-parametric regression and to predictive clustering) with ad-hoc data transformation and abstraction mechanisms. As a result, a modular representation of the process is obtained, where different performance-relevant variants of it are provided with separate regression models, and discriminated on the basis of context information. As the approach can look at the given log traces at a proper level of abstraction, in a pretty automatic and transparent fashion, no heavy intervention by the analyst is required (a major drawback of previous solutions in the literature). Tests performed on a real application scenario showed satisfactory results, in terms of both prediction accuracy and robustness. (literal)
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