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Discovering Process Models from Uncertain Event Data

  • Marco PegoraroEmail author
  • Merih Seran Uysal
  • Wil M. P. van der Aalst
Conference paper
  • 141 Downloads
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 362)

Abstract

Modern information systems are able to collect event data in the form of event logs. Process mining techniques allow to discover a model from event data, to check the conformance of an event log against a reference model, and to perform further process-centric analyses. In this paper, we consider uncertain event logs, where data is recorded together with explicit uncertainty information. We describe a technique to discover a directly-follows graph from such event data which retains information about the uncertainty in the process. We then present experimental results of performing inductive mining over the directly-follows graph to obtain models representing the certain and uncertain part of the process.

Keywords

Process mining Process discovery Uncertain data 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Process and Data Science Group (PADS), Department of Computer ScienceRWTH Aachen UniversityAachenGermany

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