Decomposing Petri nets for process mining : a generic approach

W.M.P. Aalst, van der

Onderzoeksoutput: Bijdrage aan tijdschriftTijdschriftartikelAcademicpeer review

162 Citaten (Scopus)
4 Downloads (Pure)

Samenvatting

The practical relevance of process mining is increasing as more and more event data become available. Process mining techniques aim to discover, monitor and improve real processes by extracting knowledge from event logs. The two most prominent process mining tasks are: (i) process discovery: learning a process model from example behavior recorded in an event log, and (ii) conformance checking: diagnosing and quantifying discrepancies between observed behavior and modeled behavior. The increasing volume of event data provides both opportunities and challenges for process mining. Existing process mining techniques have problems dealing with large event logs referring to many different activities. Therefore, we propose a generic approach to decompose process mining problems. The decomposition approach is generic and can be combined with different existing process discovery and conformance checking techniques. It is possible to split computationally challenging process mining problems into many smaller problems that can be analyzed easily and whose results can be combined into solutions for the original problems. Keywords: Process mining · Process decomposition · Distributed conformance checking · Distributed process discovery · Petri nets
Originele taal-2Engels
Pagina's (van-tot)471-507
Aantal pagina's37
TijdschriftDistributed and Parallel Databases
Volume31
Nummer van het tijdschrift4
DOI's
StatusGepubliceerd - 2013

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