Publications 2022

  • [PDF] [DOI] H. M. W. Verbeek, “The Log Skeleton Visualizer in ProM 6.9: the winning contribution to the Process Discovery Contest 2019,” International Journal on Software Tools for Technology Transfer, vol. 24, iss. 4, pp. 549-561, 2022.
    [Bibtex]
    @Article{Verbeek21,
    author = {Verbeek, H. M. W.},
    date = {Aug, 2022},
    journaltitle = {Int. J. Softw. Tools Technol. Trans.},
    title = {The {Log Skeleton Visualizer} in {ProM 6.9}: The winning contribution to the {Process Discovery Contest 2019}},
    doi = {10.1007/s10009-021-00618-y},
    issuetitle = {TOOLympics 2019},
    note = {Accepted for publication in 'STTT Competitions and Challenges Track - TOOLympics 2019'},
    number = {4},
    pages = {549--561},
    subtitle = {The winning contribution to the process discovery contest 2019},
    volume = {24},
    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 = {2022},
    }
  • [DOI] A. Augusto, J. Carmona, and E. Verbeek, “Advanced process discovery techniques,” in Process mining handbook, W. M. P. van der Aalst and J. Carmona, Eds., Springer, Cham, 2022, vol. 448, pp. 76-107.
    [Bibtex]
    @InBook{Augusto22,
    author = {Augusto, A. and Carmona, J. and Verbeek, E.},
    booktitle = {Process Mining Handbook},
    date = {27 June 2022},
    title = {Advanced Process Discovery Techniques},
    doi = {10.1007/978-3-031-08848-3_3},
    editor = {van der Aalst, W.M.P. and Carmona, J.},
    pages = {76--107},
    publisher = {Springer, Cham},
    series = {Lecture Notes in Business Information Processing},
    volume = {448},
    year = {2022},
    }
  • [PDF] H. M. W. Verbeek, “Discovering an S-Coverable WF-net using DiSCover,” in Proceedings of the 2022 4th International Conference on Process Mining (ICPM 2022), 2022.
    [Bibtex]
    @InProceedings{Verbeek22,
    author = {Verbeek, H. M. W.},
    booktitle = {{Proceedings of the 2022 4th International Conference on Process Mining (ICPM 2022)}},
    date = {2022},
    title = {Discovering an {S-Coverable WF-net} using {DiSCover}},
    isbn = {979-8-3503-9714-7},
    publisher = {IEEE},
    url = {https://www.win.tue.nl/~hverbeek/wp-content/papercite-data/pdf/verbeek22.pdf},
    abstract = {Although many algorithms exist that can discover a WF-net from an event log, only a few (if any at all) can discover advanced routing constructs. As examples, the Inductive miner uses process trees and cannot discover complex loops, or situations where choice and parallel behavior is mixed, and the Hybrid ILP miner cannot discover certain complex routing constructs because it cannot discover silent transitions. This paper introduces the DiSCover miner, a discovery algorithm that can discover these more complex constructs and that is implemented in ProM. The DiSCover miner discovers from the event log a WF-net that corresponds to a collection of state machines that need to synchronize on the visible transitions (that is, on the activities from the event log). As such, it discovers a WF-net that is S-Coverable but not necessarily sound. Initial results show that it can discover complex routing constructs and that it performs well on the data sets of the different Process Discovery Contests. It even preformed better than winners of the 2020 and 2021 contests.},
    }

Leave a Reply