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

University of Haifa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › 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	American English
Title of host publication	Business Process Management - 16th International Conference, BPM 2018, Proceedings
Editors	Marco Montali, Ingo Weber, Mathias Weske, Jan vom Brocke
Publisher	Springer Verlag
Pages	322-338
Number of pages	17
ISBN (Print)	9783319986470
DOIs	https://doi.org/10.1007/978-3-319-98648-7_19
State	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

Conference

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

Keywords

Classification of modelers
Model layout
Process modeling

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

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

Cite this

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, 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 et al. / Who is behind the model? classifying modelers based on pragmatic model features. Business Process Management - 16th International Conference, BPM 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)).

@inproceedings{4f15ebe231ac4170ae4cc7738af4e970,

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.",

keywords = "Classification of modelers, Model layout, Process modeling",

author = "Andrea Burattin and Pnina Soffer and Dirk Fahland and Jan Mendling and Reijers, \{Hajo A.\} and Irene Vanderfeesten and Matthias Weidlich and Barbara Weber",

note = "Funding Information: This research was funded by the Austrian Science Fund (FWF): P26140–N15 and P26609N15. Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 16th International Conference on Business Process Management, BPM 2018 ; Conference date: 09-09-2018 Through 14-09-2018",

year = "2018",

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

language = "American English",

isbn = "9783319986470",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "322--338",

editor = "Marco Montali and Ingo Weber and Mathias Weske and \{vom Brocke\}, Jan",

booktitle = "Business Process Management - 16th International Conference, BPM 2018, Proceedings",

address = "Germany",

}

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, 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 et al.
Business Process Management - 16th International Conference, BPM 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 › peer-review

TY - GEN

T1 - Who is behind the model? classifying modelers based on pragmatic model features

AU - Burattin, Andrea

AU - Soffer, Pnina

AU - Fahland, Dirk

AU - Mendling, Jan

AU - Reijers, Hajo A.

AU - Vanderfeesten, Irene

AU - Weidlich, Matthias

AU - Weber, Barbara

N1 - Funding Information: This research was funded by the Austrian Science Fund (FWF): P26140–N15 and P26609N15. Publisher Copyright: © Springer Nature Switzerland AG 2018.

PY - 2018

Y1 - 2018

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.

KW - Classification of modelers

KW - Model layout

KW - Process modeling

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

M3 - Conference contribution

SN - 9783319986470

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

SP - 322

EP - 338

BT - Business Process Management - 16th International Conference, BPM 2018, Proceedings

A2 - Montali, Marco

A2 - Weber, Ingo

A2 - Weske, Mathias

A2 - vom Brocke, Jan

PB - Springer Verlag

T2 - 16th International Conference on Business Process Management, BPM 2018

Y2 - 9 September 2018 through 14 September 2018

ER -

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, Proceedings. Springer Verlag. 2018. p. 322-338. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-98648-7_19

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

Abstract

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Keywords

All Science Journal Classification (ASJC) codes

Access to Document

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