A Critical Evaluation Study of Model-Log Metrics in Process Discovery

  • Jochen De Weerdt
  • Manu De Backer
  • Jan Vanthienen
  • Bart Baesens
Conference paper

DOI: 10.1007/978-3-642-20511-8_14

Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 66)
Cite this paper as:
De Weerdt J., De Backer M., Vanthienen J., Baesens B. (2011) A Critical Evaluation Study of Model-Log Metrics in Process Discovery. In: zur Muehlen M., Su J. (eds) Business Process Management Workshops. BPM 2010. Lecture Notes in Business Information Processing, vol 66. Springer, Berlin, Heidelberg

Abstract

The development of a well-defined evaluation framework for process discovery techniques is definitely one of the most important challenges within this subdomain of process mining. Any researcher in the field will acknowledge that such a framework is vital. With this paper, we aim to provide a tangible analysis of the currently available model-log evaluation metrics for mined control-flow models. Also, we will indicate strengths and weaknesses of the existing metrics and propose a number of opportunities for future research.

Keywords

process discovery evaluation metrics machine learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Jochen De Weerdt
    • 1
  • Manu De Backer
    • 1
    • 2
    • 3
  • Jan Vanthienen
    • 1
  • Bart Baesens
    • 1
  1. 1.Department of Decision Sciences and Information ManagementKatholieke Universiteit LeuvenLeuvenBelgium
  2. 2.Department of HABE, Hogeschool GentUniversiteit GentGhentBelgium
  3. 3.Department of Management Information SystemsUniversity of AntwerpAntwerpBelgium

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