Learning hybrid process models from events: process discovery without faking confidence

Wil M.P. van der Aalst, Riccardo De Masellis, Chiara Di Francescomarino, Chiara Ghidini

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

13 Citations (Scopus)

Abstract

Process discovery techniques return process models that are either formal (precisely describing the possible behaviors) or informal (merely a “picture” not allowing for any form of formal reasoning). Formal models are able to classify traces (i.e., sequences of events) as fitting or non-fitting. Most process mining approaches described in the literature produce such models. This is in stark contrast with the over 25 available commercial process mining tools that only discover informal process models that remain deliberately vague on the precise set of possible traces. There are two main reasons why vendors resort to such models: scalability and simplicity. In this paper, we propose to combine the best of both worlds: discovering hybrid process models that have formal and informal elements. As a proof of concept we present a discovery technique based on hybrid Petri nets. These models allow for formal reasoning, but also reveal information that cannot be captured in mainstream formal models. A novel discovery algorithm returning hybrid Petri nets has been implemented in ProM and has been applied to several real-life event logs. The results clearly demonstrate the advantages of remaining “vague” when there is not enough “evidence” in the data or standard modeling constructs do not “fit”. Moreover, the approach is scalable enough to be incorporated in industrial-strength process mining tools.

Original languageEnglish
Title of host publicationBusiness Process Management - 15th International Conference, BPM 2017, Proceedings
EditorsJeremy Seligman, Tomoyuki Yamada, Alexandru Baltag
Place of PublicationCham
PublisherSpringer
Pages59-76
Number of pages18
ISBN (Electronic)978-3-319-65000-5
ISBN (Print)978-3-319-64999-3
DOIs
Publication statusPublished - 1 Jan 2017
Event15th International Conference on Business Process Management (BPM 2017) - Barcelona, Spain
Duration: 10 Sep 201715 Sep 2017
Conference number: 15
https://bpm2017.cs.upc.edu/

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10445 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference15th International Conference on Business Process Management (BPM 2017)
Abbreviated titleBPM 2017
Country/TerritorySpain
CityBarcelona
Period10/09/1715/09/17
Internet address

Keywords

  • BPM
  • Petri nets
  • Process discovery
  • Process mining

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