Data-driven Process Discovery - artificial event log

    Dataset

    Description

    A synthetic event log with 100,000 traces and 900,000 events that was generated by simulating a simple artificial process model. There are three data attributes in the event log: Priority, Nurse, and Type. Some paths in the model are recorded infrequently based on the value of these attributes. Noise is added by randomly adding one additional event to an increasing number of traces.
    CPN Tools (http://cpntools.org) was used to generate the event log and inject the noise.
    Date made available8 Dec 2016
    PublisherEindhoven University of Technology
    Date of data production8 Dec 2016
    • Data-driven process discovery: revealing conditional infrequent behavior from event logs

      Mannhardt, F., de Leoni, M., Reijers, H. A. & van der Aalst, W. M. P., 2017, Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings. Dubois, E. & Pohl, K. (eds.). Cham: Springer, p. 545-560 16 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 10253 LNCS).

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

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