A data-adaptive trace abstraction approach to the prediction of business process performances (Contributo in atti di convegno)

Type

• Contributo in atti di convegno (Classe)
• Prodotto della ricerca (Classe)

Label

• A data-adaptive trace abstraction approach to the prediction of 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)
  A data-adaptive trace abstraction approach to the prediction of business process performances
  in 15th International Conference on Enterprise Information Systems, ICEIS 2013, Angers, France, 4-7 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

• 56 (literal)

Pagina fine

• 65 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#url

• http://www.scopus.com/record/display.url?eid=2-s2.0-84887723562&origin=inward (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#titoloVolume

• Proceedings of the 15th International Conference on Enterprise Information Systems, ICEIS 2013 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#numeroVolume

• 1 (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#volumeInCollana

• 1 (literal)

Note

• Scopu (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni

• ICAR-CNR, Rende, Italy University of Calabria, Rende, Italy (literal)

Titolo

• A data-adaptive trace abstraction approach to the prediction of business process performances (literal)

Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#isbn

• 9789898565594 (literal)

Abstract

• This paper presents a novel approach to the discovery of predictive process models, which are meant to support the run-time prediction of some performance indicator (e.g., the remaining processing time) on new ongoing process instances. To this purpose, we combine 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 performancerelevant variants of it are provided with separate regression models, and discriminated on the basis of context information. Notably, the approach is capable to look at the given log traces at a proper level of abstraction, in a pretty automatic and transparent fashion, which reduces the need for heavy intervention by the analyst (which is, indeed, a major drawback of previous solutions in the literature). The approach has been validated on a real application scenario, with satisfactory results, in terms of both prediction accuracy and robustness. (literal)

Prodotto di

• Institute for high performance computing and networking (ICAR) (Istituto)
• Gestire, Estrarre Conoscenza e Ragionare sui Dati (ICT.P08.002.001) (Modulo)

Autore CNR

• LUIGI PONTIERI (Unità di personale interno)
• MASSIMO GUARASCIO (Unità di personale esterno)
• FRANCESCO PAOLO FOLINO (Unità di personale esterno)

Insieme di parole chiave

• Keywords of "A data-adaptive trace abstraction approach to the prediction of business process performances" (Insieme di parole chiave)

Incoming links:

Prodotto

• Institute for high performance computing and networking (ICAR) (Istituto)
• Gestire, Estrarre Conoscenza e Ragionare sui Dati (ICT.P08.002.001) (Modulo)

Autore CNR di

• LUIGI PONTIERI (Unità di personale interno)
• MASSIMO GUARASCIO (Unità di personale esterno)
• FRANCESCO PAOLO FOLINO (Unità di personale esterno)

Insieme di parole chiave di

• Keywords of "A data-adaptive trace abstraction approach to the prediction of business process performances" (Insieme di parole chiave)