Who is behind the model? classifying modelers based on pragmatic model features

Andrea Burattin*, Pnina Soffer, Dirk Fahland, Jan Mendling, Hajo A. Reijers, Irene Vanderfeesten, Matthias Weidlich, Barbara Weber

*Corresponding author for this work

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


Process modeling tools typically aid end users in generic, non-personalized ways. However, it is well conceivable that different types of end users may profit from different types of modeling support. In this paper, we propose an approach based on machine learning that is able to classify modelers regarding their expertise while they are creating a process model. To do so, it takes into account pragmatic features of the model under development. The proposed approach is fully automatic, unobtrusive, tool independent, and based on objective measures. An evaluation based on two data sets resulted in a prediction performance of around 90%. Our results further show that all features can be efficiently calculated, which makes the approach applicable to online settings like adaptive modeling environments. In this way, this work contributes to improving the performance of process modelers.

Original languageEnglish
Title of host publicationBusiness Process Management
Subtitle of host publication16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9–14, 2018, Proceedings
EditorsMarco Montali, Ingo Weber, Mathias Weske, Jan vom Brocke
Number of pages17
ISBN (Electronic)9783319986487
ISBN (Print)9783319986470
Publication statusPublished - 2018
Event16th International Conference on Business Process Management, BPM 2018 - Sydney, Australia
Duration: 9 Sept 201814 Sept 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11080 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference16th International Conference on Business Process Management, BPM 2018


Acknowledgements. This research was funded by the Austrian Science Fund (FWF): P26140–N15 and P26609N15.

FundersFunder number
Austrian Science FundP26609N15, P26140–N15


    • Classification of modelers
    • Model layout
    • Process modeling


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