PrOnto: an Ontology Driven Business Process Mining Tool | Zendy

Stefano Bistarelli | Zendy; Tommaso Di Noia | Zendy; Marina Mongiello | Zendy; Francesco Nocera | Zendy

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PrOnto: an Ontology Driven Business Process Mining Tool

Author(s) -

Stefano Bistarelli,

Tommaso Di Noia,

Marina Mongiello,

Francesco Nocera

Publication year - 2017

Publication title -

procedia computer science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.334

H-Index - 76

ISSN - 1877-0509

DOI - 10.1016/j.procs.2017.08.002

Subject(s) - computer science , ontology , abstraction , process mining , business process discovery , asset (computer security) , business process , business process modeling , process (computing) , data mining , set (abstract data type) , data science , event (particle physics) , key (lock) , business process management , artifact centric business process model , semantics (computer science) , software engineering , work in process , programming language , computer security , philosophy , physics , epistemology , marketing , quantum mechanics , business

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.

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