http://www.cnr.it/ontology/cnr/individuo/prodotto/ID14523

Mining Taxonomies of Process Models (Articolo in rivista)

Type

Articolo in rivista (Classe)
Prodotto della ricerca (Classe)

Label

Mining Taxonomies of Process Models (Articolo in rivista) (literal)

Anno

2008-01-01T00:00:00+01:00 (literal)

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

10.1016/j.datak.2008.06.010 (literal)

Alternative label

Greco Gianluigi; Guzzo Antonella; Pontieri Luigi (2008)
Mining Taxonomies of Process Models
in Data & knowledge engineering; ELSEVIER SCIENCE BV, PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS, AMSTERDAM (Paesi Bassi)
(literal)

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

Greco Gianluigi; Guzzo Antonella; Pontieri Luigi (literal)

Pagina inizio

74 (literal)

Pagina fine

102 (literal)

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

http://www.sciencedirect.com/science/article/pii/S0169023X08000839 (literal)

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

67 (literal)

Rivista

Data & knowledge engineering (Rivista)

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

29 (literal)

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

1 (literal)

Note

Scopu (literal)
ISI Web of Science (WOS) (literal)
DBLP (literal)
ACM DL (literal)
Google Scholar (literal)
Science direct - Elsevier (literal)
PubZone (literal)

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

Department of Mathematics, University of Calabria, Via P.Bucci 30B, 87036 Rende, Italy; Institute ICAR, CNR, Via P.Bucci 41C, 87036 Rende, Italy; Department of DEIS, University of Calabria, Via P.Bucci 41C, 87036 Rende, Italy (literal)

Titolo

Mining Taxonomies of Process Models (literal)

Abstract

Process mining techniques have been receiving great attention in the literature for their ability to automatically support process (re)design. Typically, these techniques discover a concrete workflow schema modelling all possible execution patterns registered in a given log, which can be exploited subsequently to support further-coming enactments. In this paper, an approach to process mining is introduced that extends classical discovery mechanisms by means of an abstraction method aimed at producing a taxonomy of workflow models. The taxonomy is built to capture the process behavior at different levels of detail. Indeed, the most-detailed mined models, i.e., the leafs of the taxonomy, are meant to support the design of concrete workflows, as it happens with existing techniques in the literature. The other models, i.e., non-leaf nodes of the taxonomy, represent instead abstract views over the process behavior that can be used to support advanced monitoring and analysis tasks. All the techniques discussed in the paper have been implemented, tested, and made available as a plugin for a popular process mining framework (ProM). A series of tests, performed on different synthesized and real datasets, evidenced the capability of the approach to characterize the behavior encoded in input logs in a precise and complete way, achieving compelling conformance results even in the presence of complex behavior and noisy data. Moreover, encouraging results have been obtained in a real-life application scenario, where it is shown how the taxonomical view of the process can effectively support an explorative ex-post analysis, hinged on the different kinds of process execution discovered from the logs. (literal)

Editore