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

doi:10.1007/978-3-319-98648-7_19

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 proceeding › Conference contribution › Academic › peer-review

Abstract

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 language	English
Title of host publication	Business Process Management
Subtitle of host publication	16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9–14, 2018, Proceedings
Editors	Marco Montali, Ingo Weber, Mathias Weske, Jan vom Brocke
Publisher	Springer/Verlag
Pages	322-338
Number of pages	17
ISBN (Electronic)	9783319986487
ISBN (Print)	9783319986470
DOIs	https://doi.org/10.1007/978-3-319-98648-7_19
Publication status	Published - 2018
Event	16th International Conference on Business Process Management, BPM 2018 - Sydney, Australia Duration: 9 Sep 2018 → 14 Sep 2018

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11080 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	16th International Conference on Business Process Management, BPM 2018
Country	Australia
City	Sydney
Period	9/09/18 → 14/09/18

Keywords

Classification of modelers
Model layout
Process modeling

Access to Document

10.1007/978-3-319-98648-7_19

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Burattin, A., Soffer, P., Fahland, D., Mendling, J., Reijers, H. A., Vanderfeesten, I., Weidlich, M., & Weber, B. (2018). Who is behind the model? classifying modelers based on pragmatic model features. In M. Montali, I. Weber, M. Weske, & J. vom Brocke (Eds.), Business Process Management: 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9–14, 2018, Proceedings (pp. 322-338). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11080 LNCS). Springer/Verlag. https://doi.org/10.1007/978-3-319-98648-7_19

Burattin, Andrea ; Soffer, Pnina ; Fahland, Dirk ; Mendling, Jan ; Reijers, Hajo A. ; Vanderfeesten, Irene ; Weidlich, Matthias ; Weber, Barbara. / Who is behind the model? classifying modelers based on pragmatic model features. Business Process Management: 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9–14, 2018, Proceedings. editor / Marco Montali ; Ingo Weber ; Mathias Weske ; Jan vom Brocke. Springer/Verlag, 2018. pp. 322-338 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Who is behind the model? classifying modelers based on pragmatic model features",

abstract = "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.",

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doi = "10.1007/978-3-319-98648-7_19",

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Burattin, A, Soffer, P, Fahland, D, Mendling, J, Reijers, HA, Vanderfeesten, I, Weidlich, M & Weber, B 2018, Who is behind the model? classifying modelers based on pragmatic model features. in M Montali, I Weber, M Weske & J vom Brocke (eds), Business Process Management: 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9–14, 2018, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11080 LNCS, Springer/Verlag, pp. 322-338, 16th International Conference on Business Process Management, BPM 2018, Sydney, Australia, 9/09/18. https://doi.org/10.1007/978-3-319-98648-7_19

Who is behind the model? classifying modelers based on pragmatic model features. / Burattin, Andrea; Soffer, Pnina; Fahland, Dirk; Mendling, Jan; Reijers, Hajo A.; Vanderfeesten, Irene; Weidlich, Matthias; Weber, Barbara.

Business Process Management: 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9–14, 2018, Proceedings. ed. / Marco Montali; Ingo Weber; Mathias Weske; Jan vom Brocke. Springer/Verlag, 2018. p. 322-338 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11080 LNCS).

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

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AU - Soffer, Pnina

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AU - Mendling, Jan

AU - Reijers, Hajo A.

AU - Vanderfeesten, Irene

AU - Weidlich, Matthias

AU - Weber, Barbara

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N2 - 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.

AB - 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.

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Burattin A, Soffer P, Fahland D, Mendling J, Reijers HA, Vanderfeesten I et al. Who is behind the model? classifying modelers based on pragmatic model features. In Montali M, Weber I, Weske M, vom Brocke J, editors, Business Process Management: 16th International Conference, BPM 2018, Sydney, NSW, Australia, September 9–14, 2018, Proceedings. Springer/Verlag. 2018. p. 322-338. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-319-98648-7_19