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

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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
Number of pages16
ISBN (Electronic)978-3-319-59536-8
ISBN (Print)978-3-319-59535-1
Publication statusPublished - 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10253 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


  • Event logs
  • Noise
  • Process discovery
  • Process mining
  • Rules


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