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.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.