Discovering an S-Coverable WF-net using DiSCover

Presented at the ICPM 2022 International Conference on Process Mining in Bolzano, Italy, on October 25, 2022


H. M. W. Verbeek: Discovering an S-Coverable WF-net using DiSCover. In: Burattin, A.; Polyvyannyy, A.; Weber, B. (Ed.): Proceedings of the 2022 4th International Conference on Process Mining (ICPM 2022), pp. 64–71, IEEE, 2022, ISBN: 979-8-3503-9714-7.



  • DiSCover_6.12.50_1_3
    • ZIP archive, 292.3 MB
    • Uses a tailored classification algorithm with token-based replay.
  • DiSCover_6.12.50_1_3_AR
    • ZIP archive, 348.2 MB
    • Uses the default classification algorithm for Petri nets (PNML) (that is, with alignment-based replay).

Both algorithms use an absolute threshold that equals 1, a relative threshold that equals 3, and default values for all other parameters. These thresholds are set in the “Scripts/Discover.txt” file, lines 31 and 32.

After setting the proper parameters values and making sure the data set and all other required algorithms are in place (see the PDC 2022 page), the entire experiment can be run by calling the “Run.bat” batch file. Before running the experiment, please remove any files “scores[123].csv” and the folders “Logs[123]” and “Models[123]”. These files and folders will be created by the experiment, but to do so they should not exist yet.

The classification results of the experiment are stored in the “scores1.csv”, “scores2.csv”, and “score3.csv” files. The contents of these files can be copy-pasted in the respective tabs of the “DiSCover_6.12.50_1_3_AR.xlsx” spreadsheet. The tab “pdc 2022” then shows the end results (see cells H485:M510).