Fast conformance analysis based on activity log abstraction

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

2 Citations (Scopus)

Abstract

Process mining techniques focus on bridging the gap between activity logs and business process management. Process discovery is a sub-field of process mining which uses activity logs in order to discover process models. Some process discovery techniques, such as interactive process discovery and genetic algorithms, rely on the so-called conformance analysis. In such techniques, process models are discovered in an incremental way, and the quality of the process models is quantified by the results of conformance analysis. State-of-the-art conformance analysis techniques are typically optimized and devised for one-time use. However, in process discovery settings which are incremental in nature, it is imperative to have fast conformance analysis. Moreover, the activity logs used for conformance analysis at each stage remain the same. In this paper, we propose an approach that exploits this fact in order to expedite conformance analysis by approximating the conformance results. We use an abstracted version of an activity log, which can be used to compare with the changing (or new) process models in an incremental process discovery setting. Our results show that the proposed technique is able to outperform traditional conformance techniques in terms of performance by approximating conformance scores.

Original languageEnglish
Title of host publicationProceedings - 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference, EDOC 2018
Place of PublicationPiscataway
PublisherInstitute of Electrical and Electronics Engineers
Pages135-144
Number of pages10
ISBN (Electronic)9781538641392
DOIs
Publication statusPublished - Oct 2018
Event22nd IEEE International Enterprise Distributed Object Computing Conference, EDOC 2018 - Stockholm, Sweden
Duration: 16 Oct 201819 Oct 2018

Conference

Conference22nd IEEE International Enterprise Distributed Object Computing Conference, EDOC 2018
Abbreviated titleIEEE EDOC 2018
CountrySweden
CityStockholm
Period16/10/1819/10/18

Fingerprint

Genetic algorithms
Industry
Process model
Incremental
Process mining
Business process management
Genetic algorithm

Keywords

  • conformance analysis
  • interactive
  • interactive process discovery
  • process mining

Cite this

Dixit, P. M., Verbeek, H. M. W., & van der Aalst, W. M. P. (2018). Fast conformance analysis based on activity log abstraction. In Proceedings - 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference, EDOC 2018 (pp. 135-144). [8536157] Piscataway: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/EDOC.2018.00026
Dixit, P.M. ; Verbeek, H.M.W. ; van der Aalst, W.M.P. / Fast conformance analysis based on activity log abstraction. Proceedings - 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference, EDOC 2018. Piscataway : Institute of Electrical and Electronics Engineers, 2018. pp. 135-144
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Dixit, PM, Verbeek, HMW & van der Aalst, WMP 2018, Fast conformance analysis based on activity log abstraction. in Proceedings - 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference, EDOC 2018., 8536157, Institute of Electrical and Electronics Engineers, Piscataway, pp. 135-144, 22nd IEEE International Enterprise Distributed Object Computing Conference, EDOC 2018, Stockholm, Sweden, 16/10/18. https://doi.org/10.1109/EDOC.2018.00026

Fast conformance analysis based on activity log abstraction. / Dixit, P.M.; Verbeek, H.M.W.; van der Aalst, W.M.P.

Proceedings - 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference, EDOC 2018. Piscataway : Institute of Electrical and Electronics Engineers, 2018. p. 135-144 8536157.

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

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Dixit PM, Verbeek HMW, van der Aalst WMP. Fast conformance analysis based on activity log abstraction. In Proceedings - 2018 IEEE 22nd International Enterprise Distributed Object Computing Conference, EDOC 2018. Piscataway: Institute of Electrical and Electronics Engineers. 2018. p. 135-144. 8536157 https://doi.org/10.1109/EDOC.2018.00026