PEM4PPM: A Cognitive Perspective on the Process of Process Mining

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

doi:10.1007/978-3-031-41620-0_27

PEM4PPM: A Cognitive Perspective on the Process of Process Mining

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

Department of Information Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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 language	English
Title of host publication	Business Process Management - 21st International Conference, BPM 2023, Proceedings
Editors	Chiara Di Francescomarino, Andrea Burattin, Christian Janiesch, Shazia Sadiq
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	465-481
Number of pages	17
ISBN (Print)	9783031416194
DOIs	https://doi.org/10.1007/978-3-031-41620-0_27
State	Published - 2023
Event	Proceedings of the 21st International Conference on Business Process Management , BPM 2023 - Utrecht, Netherlands Duration: 11 Sep 2023 → 15 Sep 2023

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	14159 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	Proceedings of the 21st International Conference on Business Process Management , BPM 2023
Country/Territory	Netherlands
City	Utrecht
Period	11/09/23 → 15/09/23

Bibliographical note

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

Keywords

Analysis Strategies
Mixed Methods
Prediction Error Minimization
Predictive Processing
Process Mining

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-41620-0_27

Cite this

Sorokina, E., Soffer, P., Hadar, I., Leron, U., Zerbato, F., & Weber, B. (2023). PEM4PPM: A Cognitive Perspective on the Process of Process Mining. In C. Di Francescomarino, A. Burattin, C. Janiesch, & S. Sadiq (Eds.), Business Process Management - 21st International Conference, BPM 2023, Proceedings (pp. 465-481). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14159 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-41620-0_27

Sorokina, Elizaveta ; Soffer, Pnina ; Hadar, Irit et al. / PEM4PPM : A Cognitive Perspective on the Process of Process Mining. Business Process Management - 21st International Conference, BPM 2023, Proceedings. editor / Chiara Di Francescomarino ; Andrea Burattin ; Christian Janiesch ; Shazia Sadiq. Springer Science and Business Media Deutschland GmbH, 2023. pp. 465-481 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "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.",

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Sorokina, E, Soffer, P , Hadar, I, Leron, U, Zerbato, F & Weber, B 2023, PEM4PPM: A Cognitive Perspective on the Process of Process Mining. in C Di Francescomarino, A Burattin, C Janiesch & S Sadiq (eds), Business Process Management - 21st International Conference, BPM 2023, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 14159 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 465-481, Proceedings of the 21st International Conference on Business Process Management , BPM 2023, Utrecht, Netherlands, 11/09/23. https://doi.org/10.1007/978-3-031-41620-0_27

PEM4PPM: A Cognitive Perspective on the Process of Process Mining. / Sorokina, Elizaveta; Soffer, Pnina ; Hadar, Irit et al.
Business Process Management - 21st International Conference, BPM 2023, Proceedings. ed. / Chiara Di Francescomarino; Andrea Burattin; Christian Janiesch; Shazia Sadiq. Springer Science and Business Media Deutschland GmbH, 2023. p. 465-481 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 14159 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - PEM4PPM

T2 - Proceedings of the 21st International Conference on Business Process Management , BPM 2023

AU - Sorokina, Elizaveta

AU - Soffer, Pnina

AU - Hadar, Irit

AU - Leron, Uri

AU - Zerbato, Francesca

AU - Weber, Barbara

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Analysis Strategies

KW - Mixed Methods

KW - Prediction Error Minimization

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KW - Process Mining

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SN - 9783031416194

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Business Process Management - 21st International Conference, BPM 2023, Proceedings

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A2 - Burattin, Andrea

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Sorokina E, Soffer P , Hadar I, Leron U, Zerbato F, Weber B. PEM4PPM: A Cognitive Perspective on the Process of Process Mining. In Di Francescomarino C, Burattin A, Janiesch C, Sadiq S, editors, Business Process Management - 21st International Conference, BPM 2023, Proceedings. Springer Science and Business Media Deutschland GmbH. 2023. p. 465-481. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-41620-0_27

PEM4PPM: A Cognitive Perspective on the Process of Process Mining

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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