Mining hybrid business process models: A quest for better precision

Dennis M.M. Schunselaar, Tijs Slaats, Fabrizio M. Maggi, Hajo A. Reijers, Wil M.P. van der Aalst

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

Abstract

In this paper, we present a technique for the discovery of hybrid process models that combine imperative and declarative constructs. In particular, we first employ the popular Inductive Miner to generate a fully imperative model from a log. Like most imperative miners, the Inductive Miner tends to return so-called flower models for the less structured parts of the process. These parts are often imprecise. To counter these imprecise parts, we replace them with declarative models to increase the precision since declarative models are good at specifying which behavior is disallowed. The approach has been implemented in ProM and tested on several synthetic and real-life event logs. Our experiments show that hybrid models can be found to be more precise without overfitting the data.

Original languageEnglish
Title of host publicationBusiness Information Systems - 21st International Conference, BIS 2018, Proceedings
PublisherSpringer/Verlag
Pages190-205
Number of pages16
ISBN (Electronic)9783319939315
ISBN (Print)9783319939308
DOIs
Publication statusPublished - 2018
Event21st International Conference on Business Information Systems, BIS 2018 - Berlin, Germany
Duration: 18 Jul 201820 Jul 2018

Publication series

NameLecture Notes in Business Information Processing
Volume320
ISSN (Print)1865-1348

Conference

Conference21st International Conference on Business Information Systems, BIS 2018
CountryGermany
CityBerlin
Period18/07/1820/07/18

Fingerprint

Business Model
Business Process
Process Model
Mining
Miners
Hybrid Model
Industry
Overfitting
Model
Tend
Business process model
Experiment
Experiments

Keywords

  • Declare
  • Hybrid process model
  • Process discovery
  • Process mining
  • Process tree

Cite this

Schunselaar, D. M. M., Slaats, T., Maggi, F. M., Reijers, H. A., & van der Aalst, W. M. P. (2018). Mining hybrid business process models: A quest for better precision. In Business Information Systems - 21st International Conference, BIS 2018, Proceedings (pp. 190-205). (Lecture Notes in Business Information Processing; Vol. 320). Springer/Verlag. https://doi.org/10.1007/978-3-319-93931-5_14
Schunselaar, Dennis M.M. ; Slaats, Tijs ; Maggi, Fabrizio M. ; Reijers, Hajo A. ; van der Aalst, Wil M.P. / Mining hybrid business process models : A quest for better precision. Business Information Systems - 21st International Conference, BIS 2018, Proceedings. Springer/Verlag, 2018. pp. 190-205 (Lecture Notes in Business Information Processing).
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Schunselaar, DMM, Slaats, T, Maggi, FM, Reijers, HA & van der Aalst, WMP 2018, Mining hybrid business process models: A quest for better precision. in Business Information Systems - 21st International Conference, BIS 2018, Proceedings. Lecture Notes in Business Information Processing, vol. 320, Springer/Verlag, pp. 190-205, 21st International Conference on Business Information Systems, BIS 2018, Berlin, Germany, 18/07/18. https://doi.org/10.1007/978-3-319-93931-5_14

Mining hybrid business process models : A quest for better precision. / Schunselaar, Dennis M.M.; Slaats, Tijs; Maggi, Fabrizio M.; Reijers, Hajo A.; van der Aalst, Wil M.P.

Business Information Systems - 21st International Conference, BIS 2018, Proceedings. Springer/Verlag, 2018. p. 190-205 (Lecture Notes in Business Information Processing; Vol. 320).

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

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Schunselaar DMM, Slaats T, Maggi FM, Reijers HA, van der Aalst WMP. Mining hybrid business process models: A quest for better precision. In Business Information Systems - 21st International Conference, BIS 2018, Proceedings. Springer/Verlag. 2018. p. 190-205. (Lecture Notes in Business Information Processing). https://doi.org/10.1007/978-3-319-93931-5_14