Mining predictive process models out of low-level multidimensional logs (Contributo in atti di convegno)

Type

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

Label

• Mining predictive process models out of low-level multidimensional logs (Contributo in atti di convegno) (literal)

Anno

• 2014-01-01T00:00:00+01:00 (literal)

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

• 10.1007/978-3-319-07881-6_36 (literal)

Alternative label

• Folino, Francesco; Guarascio, Massimo; Pontieri, Luigi (2014)
  Mining predictive process models out of low-level multidimensional logs
  in 26th International Conference on Advanced Information Systems Engineering, CAiSE 2014, Thessaloniki, Greece, 16-20 June 2014
  (literal)

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

• Folino, Francesco; Guarascio, Massimo; Pontieri, Luigi (literal)

Pagina inizio

• 533 (literal)

Pagina fine

• 547 (literal)

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

• http://www.scopus.com/record/display.url?eid=2-s2.0-84903134990&origin=inward (literal)

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

• Proceedings of the 26th International Conference on Advanced Information Systems Engineering, CAiSE 2014. (literal)

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

• 8484 (literal)

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

• 8484 (literal)

Rivista

• Lecture notes in computer science (Rivista)

Note

• Scopu (literal)

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

• Consiglio Nazionale delle Ricerche (literal)

Titolo

• Mining predictive process models out of low-level multidimensional logs (literal)

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

• 9783319078809 (literal)

Abstract

• Process Mining techniques have been gaining attention, especially as concerns the discovery of predictive process models. Traditionally focused on workflows, they usually assume that process tasks are clearly specified, and referred to in the logs. This limits however their application to many real-life BPM environments (e.g. issue tracking systems) where the traced events do not match any predefined task, but yet keep lots of context data. In order to make the usage of predictive process mining to such logs more effective and easier, we devise a new approach, combining the discovery of different execution scenarios with the automatic abstraction of log events. The approach has been integrated in a prototype system, supporting the discovery, evaluation and reuse of predictive process models. Tests on real-life data show that the approach achieves compelling prediction accuracy w.r.t. state-of-the-art methods, and finds interesting activities' and process variants' descriptions. © 2014 Springer International Publishing. (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

• Parole chiave di "Mining predictive process models out of low-level multidimensional logs" (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)

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

• Lecture notes in computer science (Rivista)

Insieme di parole chiave di

• Parole chiave di "Mining predictive process models out of low-level multidimensional logs" (Insieme di parole chiave)