Comparing Concept Drift Detection with Process Mining Software


  • Nicolas Jashchenko Omori Universidade Estadual de Londrina (UEL)
  • Gabriel Marques Tavares Università Degli Studi di Milano (UNIMI)
  • Paolo Ceravolo Università Degli Studi di Milano (UNIMI)
  • Sylvio Barbon Jr Universidade Estadual de Londrina (UEL)



Organisations have seen a rise in the volume of data corresponding to business processes being recorded. Handling process data is a meaningful way to extract relevant information from business processes with impact on the company's values. Nonetheless, business processes are subject to changes during their executions, adding complexity to their analysis. This paper aims at evaluating currently available process mining tools and software that handle concept drifts, i.e. changes over time of the statistical properties of the events occurring in a process. We provide an in-depth analysis of these tools, comparing their differences, advantages, and disadvantages by testing against a log taken from a Process Control System. Thus, by highlighting the trade-off between the software, the paper gives the stakeholders the best options regarding their case use.


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How to Cite

Omori, N. J., Tavares, G. M., Ceravolo, P., & Barbon Jr, S. (2020). Comparing Concept Drift Detection with Process Mining Software. ISys - Brazilian Journal of Information Systems, 13(4), 101–125.



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