PEM4PPM: A Cognitive Perspective on the Process of Process Mining

Elizaveta Sorokina, Pnina Soffer, Irit Hadar, Uri Leron, Francesca Zerbato, Barbara Weber

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


During the last decades, process mining (PM) has matured and rapidly increased in its adoption. Making sense of data is a main part of the work of PM analysts, which involves cognitive processes. Recent work has leveraged behavioral data to explain these processes. Still, the process of process mining (PPM) is yet to be well understood and a theoretical foundation for explaining how these processes unfold is missing. This paper attempts to fill this gap by understanding how PPM data can be analyzed in a theory-guided manner and what insights can be gained from this analysis. To investigate these aspects, we analyzed verbal data and interaction traces obtained from analysis sessions with 29 participants performing a PM task. The analysis was based on the Predictive Processing (PP) theory and the derived Prediction Error Minimization (PEM) process, anchored in cognitive science. The results include (1) a theoretical adaptation of the PEM theory to the PPM context, (2) four strategies utilized by PM analysts, identified, and validated based on the adapted theory, and (3) an understanding of the differences in performance between analysts using different strategies and independence of the expertise level and the strategy choice.

Original languageEnglish
Title of host publicationBusiness Process Management - 21st International Conference, BPM 2023, Proceedings
EditorsChiara Di Francescomarino, Andrea Burattin, Christian Janiesch, Shazia Sadiq
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783031416194
StatePublished - 2023
EventProceedings of the 21st International Conference on Business Process Management , BPM 2023 - Utrecht, Netherlands
Duration: 11 Sep 202315 Sep 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14159 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


ConferenceProceedings of the 21st International Conference on Business Process Management , BPM 2023

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • Analysis Strategies
  • Mixed Methods
  • Prediction Error Minimization
  • Predictive Processing
  • Process Mining

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


Dive into the research topics of 'PEM4PPM: A Cognitive Perspective on the Process of Process Mining'. Together they form a unique fingerprint.

Cite this