Data-driven process discovery: revealing conditional infrequent behavior from event logs

F. Mannhardt, M. de Leoni, H.A. Reijers, W.M.P. van der Aalst

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

25 Citations (Scopus)
192 Downloads (Pure)

Abstract

Process discovery methods automatically infer process models from event logs. Often, event logs contain so-called noise, e.g., infrequent outliers or recording errors, which obscure the main behavior of the process. Existing methods filter this noise based on the frequency of event labels: infrequent paths and activities are excluded. However, infrequent behavior may reveal important insights into the process. Thus, not all infrequent behavior should be considered as noise. This paper proposes the Data-aware Heuristic Miner (DHM), a process discovery method that uses the data attributes to distinguish infrequent paths from random noise by using classification techniques. Data- and control-flow of the process are discovered together. We show that the DHM is, to some degree, robust against random noise and reveals data-driven decisions, which are filtered by other discovery methods. The DHM has been successfully tested on several real-life event logs, two of which we present in this paper.
Original languageEnglish
Title of host publicationAdvanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings
EditorsEric Dubois, Klaus Pohl
Place of PublicationCham
PublisherSpringer
Pages545-560
Number of pages16
ISBN (Electronic)978-3-319-59536-8
ISBN (Print)978-3-319-59535-1
DOIs
Publication statusPublished - 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume10253

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Miners
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Labels

Keywords

  • Process Mining
  • Process Discovery
  • Event Logs
  • Noise
  • Data Rules

Cite this

Mannhardt, F., de Leoni, M., Reijers, H. A., & van der Aalst, W. M. P. (2017). Data-driven process discovery: revealing conditional infrequent behavior from event logs. In E. Dubois, & K. Pohl (Eds.), Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings (pp. 545-560). (Lecture Notes in Computer Science; Vol. 10253). Cham: Springer. https://doi.org/10.1007/978-3-319-59536-8_34
Mannhardt, F. ; de Leoni, M. ; Reijers, H.A. ; van der Aalst, W.M.P. / Data-driven process discovery : revealing conditional infrequent behavior from event logs. Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings. editor / Eric Dubois ; Klaus Pohl. Cham : Springer, 2017. pp. 545-560 (Lecture Notes in Computer Science).
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Mannhardt, F, de Leoni, M, Reijers, HA & van der Aalst, WMP 2017, Data-driven process discovery: revealing conditional infrequent behavior from event logs. in E Dubois & K Pohl (eds), Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10253, Springer, Cham, pp. 545-560. https://doi.org/10.1007/978-3-319-59536-8_34

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.

Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings. ed. / Eric Dubois; Klaus Pohl. Cham : Springer, 2017. p. 545-560 (Lecture Notes in Computer Science; Vol. 10253).

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

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Mannhardt F, de Leoni M, Reijers HA, van der Aalst WMP. Data-driven process discovery: revealing conditional infrequent behavior from event logs. In Dubois E, Pohl K, editors, Advanced Information Systems Engineering: 29th International Conference, CAiSE 2017, Essen, Germany, June 12-16, 2017, Proceedings. Cham: Springer. 2017. p. 545-560. (Lecture Notes in Computer Science). https://doi.org/10.1007/978-3-319-59536-8_34