Business & Information Systems Engineering

, Volume 61, Issue 6, pp 695–712 | Cite as

Generating Artificial Data for Empirical Analysis of Control-flow Discovery Algorithms

A Process Tree and Log Generator
  • Toon JouckEmail author
  • Benoît Depaire
Research Paper


Within the process mining domain, research on comparing control-flow (CF) discovery techniques has gained importance. A crucial building block of empirical analysis of CF discovery techniques is obtaining the appropriate evaluation data. Currently, there is no answer to the question of how to collect such evaluation data. The paper introduces a methodology for generating artificial event data (GED) and an implementation called the Process Tree and Log Generator. The GED methodology and its implementation provide users with full control over the characteristics of the generated event data and an integration within the ProM framework. Unlike existing approaches, there is no tradeoff between including long-term dependencies and soundness of the process. The contributions of the paper provide a solution for a necessary step in the empirical analysis of CF discovery algorithms.


Artificial event logs Process discovery Empirical analysis 



The authors would like to thank Massimiliano de Leoni and Alfredo Bolt for their advice and support to implement the PTandLogGenerator.


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Copyright information

© Springer Fachmedien Wiesbaden GmbH, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Faculty of Business EconomicsHasselt UniversityDiepenbeekBelgium

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