The main aim of data mining techniques and tools is that of identify and extract, from a set of (big) data, implicit patterns which can describe static or dynamic phenomena. Among these latter business processes are gaining more and more attention due to their crucial role in modern organizations and enterprises. Being able to identify and model processes inside organizations is for sure a key asset to discover their weak and strong points thus helping them in the improvement of their competitiveness. In this paper we describe a prototype system able to discover business processes from an event log and classify them with a suitable level of abstraction with reference to a related business ontology. The identified process, and its corresponding level of abstraction, depends on the knowledge encoded in the reference ontology which is dynamically exploited at runtime. The tool has been validated by considering examples and case studies from the literature on process mining.

PrOnto: An Ontology Driven Business Process Mining Tool / Bistarelli, Stefano; Di Noia, Tommaso; Mongiello, Marina; Nocera, Francesco. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - 112:(2017), pp. 306-315. (Intervento presentato al convegno 21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017 tenutosi a Marseille, France nel September 6-8 , 2017) [10.1016/j.procs.2017.08.002].

PrOnto: An Ontology Driven Business Process Mining Tool

Di Noia, Tommaso;Mongiello, Marina;Nocera, Francesco
2017-01-01

Abstract

The main aim of data mining techniques and tools is that of identify and extract, from a set of (big) data, implicit patterns which can describe static or dynamic phenomena. Among these latter business processes are gaining more and more attention due to their crucial role in modern organizations and enterprises. Being able to identify and model processes inside organizations is for sure a key asset to discover their weak and strong points thus helping them in the improvement of their competitiveness. In this paper we describe a prototype system able to discover business processes from an event log and classify them with a suitable level of abstraction with reference to a related business ontology. The identified process, and its corresponding level of abstraction, depends on the knowledge encoded in the reference ontology which is dynamically exploited at runtime. The tool has been validated by considering examples and case studies from the literature on process mining.
2017
21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017
PrOnto: An Ontology Driven Business Process Mining Tool / Bistarelli, Stefano; Di Noia, Tommaso; Mongiello, Marina; Nocera, Francesco. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - 112:(2017), pp. 306-315. (Intervento presentato al convegno 21st International Conference on Knowledge - Based and Intelligent Information and Engineering Systems, KES 2017 tenutosi a Marseille, France nel September 6-8 , 2017) [10.1016/j.procs.2017.08.002].
File in questo prodotto:
File Dimensione Formato  
1-s2.0-S1877050917313418-main.pdf

accesso aperto

Tipologia: Versione editoriale
Licenza: Creative commons
Dimensione 781.56 kB
Formato Adobe PDF
781.56 kB Adobe PDF Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/117103
Citazioni
  • Scopus 9
  • ???jsp.display-item.citation.isi??? 5
social impact