Comparing Concept Drift Detection with Process Mining Tools

  • Nicolas Jashchenko Omori Universidade Estadual de Londrina
  • Gabriel Marques Tavares Universidade Estadual de Londrina
  • Paolo Ceravolo Università degli Studi di Milano
  • Sylvio Barbon Jr. Universidade Estadual de Londrina


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 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 briefly comparing their differences, advantages, and disadvantages.
Palavras-chave: Process Mining, Online, Concept Drift
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OMORI, Nicolas Jashchenko; TAVARES, Gabriel Marques; CERAVOLO, Paolo; BARBON JR., Sylvio. Comparing Concept Drift Detection with Process Mining Tools. In: SIMPÓSIO BRASILEIRO DE SISTEMAS DE INFORMAÇÃO (SBSI), 15. , 2019, Aracajú. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2019 . p. 239-246.