Outlier Detection Techniques for Process Mining Applications (Contributo in atti di convegno)

Type

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

Label

• Outlier Detection Techniques for Process Mining Applications (Contributo in atti di convegno) (literal)

Anno

• 2008-01-01T00:00:00+01:00 (literal)

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

• 10.1007/978-3-540-68123-6_17 (literal)

Alternative label

• Ghionna Lucantonio; Greco Gianluigi; Guzzo Antonella; Pontieri Luigi (2008)
  Outlier Detection Techniques for Process Mining Applications
  in 17th Intl Symposium on Foundations of Intelligent Systems (ISMIS 2008), Toronto, Canada, May 20-23, 2008
  (literal)

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

• Ghionna Lucantonio; Greco Gianluigi; Guzzo Antonella; Pontieri Luigi (literal)

Pagina inizio

• 150 (literal)

Pagina fine

• 159 (literal)

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

• http://dx.doi.org/10.1007/978-3-540-68123-6_17 (literal)

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

• Proceedings of Foundations of Intelligent Systems, 17th International Symposium, ISMIS 2008 (literal)

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

• 4994 (literal)

Note

• ACM DL (literal)
• ISI Web of Science (WOS) (literal)
• Scopu (literal)
• Google Scholar (literal)
• PubZone (literal)
• DBLP (literal)

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

• Dept. of Mathematics, UNICAL, Rende, Italy; Dept. of Mathematics, UNICAL, Rende, Italy; DEIS, UNICAL, Rende, Italy; ICAR-CNR, Rende, Italy (literal)

Titolo

• Outlier Detection Techniques for Process Mining Applications (literal)

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

• 978-3-540-68122-9 (literal)

Abstract

• Classical outlier detection approaches may hardly fit process mining applications, since in these settings anomalies emerge not only as deviations from the sequence of events most often registered in the log, but also as deviations from the behavior prescribed by some (possibly unknown) process model. These issues have been faced in the paper via an approach for singling out anomalous evolutions within a set of process traces, which takes into account both statistical properties of the log and the constraints associated with the process model. The approach combines the discovery of frequent execution patterns with a cluster based anomaly detection procedure; notably, this procedure is suited to deal with categorical data and is, hence, interesting in its own, given that outlier detection has mainly been studied on numerical domains in the literature. All the algorithms presented in the paper have been implemented and integrated into a system prototype that has been thoroughly tested to assess its scalability and effectiveness. (literal)

Editore

• Springer-Verlag (Editore)

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)

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)

Editore di

• Springer-Verlag (Editore)