Publications 2021

  • [PDF] [DOI] H. M. W. Verbeek, “The Log Skeleton Visualizer in ProM 6.9,” International Journal on Software Tools for Technology Transfer, 2021.
    author = {Verbeek, H. M. W.},
    date = {May 17, 2021},
    journaltitle = {Int. J. Softw. Tools Technol. Trans.},
    title = {The {Log Skeleton Visualizer} in {ProM 6.9}},
    doi = {10.1007/s10009-021-00618-y},
    issuetitle = {TOOLympics 2019},
    note = {Accepted for publication in 'STTT Competitions and Challenges Track - TOOLympics 2019'},
    subtitle = {The winning contribution to the process discovery contest 2019},
    abstract = {Process discovery is an important area in the field of process mining. To help advance this area, a Process Discovery Contest (PDC) has been set up, which allows us to compare different approaches. At the moment of writing, there have been three instances of the PDC: In 2016, in 2017, and in 2019. This paper introduces the winning contribution to the PDC 2019, called the Log Skeleton Visualizer. This visualizer uses a novel type of process models called log skeletons. In contrast to many workflow-net-based discovery techniques, these log skeletons do not rely on the directly follows relation. As a result, log skeletons offer circumstantial information on the event log at hand rather than only sequential information. Using this visualizer we were able to classify 898 out of 900 traces correctly for the PDC 2019, and to win this contest.},
    journal = {{International Journal on Software Tools for Technology Transfer}},
    owner = {hverbeek},
    timestamp = {2020.10.26},
    year = {2021},
  • [DOI] H. M. W. Verbeek, “Process discovery contest 2020.”
    author = {H. M. W. Verbeek},
    date = {2021-05-21},
    title = {Process Discovery Contest 2020},
    doi = {10.4121/14626020.v1},
    howpublished = {4TU.ResearchData},
    url = {},
    abstract = {This is the data set that was used for the Process Discovery Contest of 2020 (PDC 2020). The data set contains 192 training logs, 192 corresponding test logs, 192 corresponding ground truth logs, and 96 models. The logs are all stored using the IEEE XES file format (see either or, while the models are workflow nets (a subclass of Petri nets) stored in the PNML file format (see},
  • [DOI] H. M. W. Verbeek, “Process discovery contest 2021.”
    author = {H. M. W. Verbeek},
    date = {2021-10-13},
    title = {Process Discovery Contest 2021},
    doi = {10.4121/16803232.v1},
    howpublished = {4TU.ResearchData},
    url = {},
  • [PDF] H. M. W. Verbeek and D. Fahland, “CPN IDE: an extensible replacement for CPN Tools that uses Access/CPN,” in Proceedings of the icpm doctoral consortium and demo track 2021 co-located with 10th international conference on process mining (icpm 2021), 2021-11-04, pp. 29-30.
    author = {Verbeek, H. M. W. and Fahland, D.},
    booktitle = {Proceedings of the ICPM Doctoral Consortium and Demo Track 2021 co-located with 10th International Conference on Process Mining (ICPM 2021)},
    date = {2021-11-04},
    title = {{CPN IDE}: An Extensible Replacement for {CPN Tools} That Uses {Access/CPN}},
    editor = {Jans, M. and Janssenswillen, G. and Kalenkova, A. and Maggi, F. M.},
    eventtitle = {ICPM 2021 Doctoral Consortium and Demo Track 2021},
    pages = {29--30},
    publisher = {},
    series = {CEUR Workshop Proceedings},
    url = {},
    volumes = {3098},
    abstract = {This extended abstract introduces CPN IDE, which replaces CPN Tools as a tool for editing and simulating (Coloured) Petri Net models. The main advantage of CPN IDE is that it is an extensible tool, which is needed to keep it running and to add new features which are of interest to the process mining community, like easily generating event logs.},
    file = {},
    owner = {hverbeek},
    timestamp = {2022-04-13},

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