Discovering stochastic Petri nets with arbitrary delay distributions from event logs

A. Rogge-Solti, W.M.P. Aalst, van der, M.H. Weske

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

30 Citations (Scopus)
1 Downloads (Pure)

Abstract

Capturing the performance of a system or business process as accurately as possible is important, as models enriched with performance information provide valuable input for analysis, operational support, and prediction. Due to their computationally nice properties, memoryless models such as exponentially distributed stochastic Petri nets have earned much attention in research and industry. However, there are cases when the memoryless property is clearly not able to capture process behavior, e.g., when dealing with fixed time-outs. We want to allow models to have generally distributed durations to be able to capture the behavior of the environment and resources as accurately as possible. For these more expressive process models, the execution policy has to be specified in more detail. In this paper, we present and evaluate process discovery algorithms for each of the execution policies. The introduced approach uses raw event execution data to discover various classes of stochastic Petri nets. The algorithms are based on the notion of alignments and have been implemented as a plug-in in the process mining framework ProM. Keywords: Process mining; Stochastic Petri nets; Generally distributed transitions
Original languageEnglish
Title of host publicationBusiness Process Management Workshops : BPM 2013 International Workshops, Beijing, China, August 26, 2013, Revised Papers
EditorsN. Lohmann, M. Song, P. Wohed
Place of PublicationBerlin
PublisherSpringer
Pages15-27
ISBN (Print)978-3-319-06256-3
DOIs
Publication statusPublished - 2014
Event9th International Workshop on Business Process Intelligence (BPI 2013) - Beijing, China
Duration: 26 Aug 201326 Aug 2013
Conference number: 9

Publication series

NameLecture Notes in Business Information Processing
Volume171
ISSN (Print)1865-1348

Workshop

Workshop9th International Workshop on Business Process Intelligence (BPI 2013)
Abbreviated titleBPI 2013
CountryChina
CityBeijing
Period26/08/1326/08/13
OtherWorkshop held in conjunction with the 11th International Conference on Business Process Management (BPM 2013)

Fingerprint Dive into the research topics of 'Discovering stochastic Petri nets with arbitrary delay distributions from event logs'. Together they form a unique fingerprint.

Cite this