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

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-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 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
PublisherSpringer/Verlag
Pages322-338
Number of pages17
ISBN (Electronic)9783319986487
ISBN (Print)9783319986470
DOIs
Publication statusPublished - 2018
Event16th International Conference on Business Process Management, BPM 2018 - Sydney, Australia
Duration: 9 Sep 201814 Sep 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

Conference

Conference16th International Conference on Business Process Management, BPM 2018
CountryAustralia
CitySydney
Period9/09/1814/09/18

Fingerprint

Feature Model
Performance Prediction
Process Modeling
Expertise
Modeling
Process Model
Profit
Learning systems
Profitability
Machine Learning
Classify
Evaluation
Model

Keywords

  • Classification of modelers
  • Model layout
  • Process modeling

Cite this

Burattin, A., Soffer, P., Fahland, D., Mendling, J., Reijers, H. A., Vanderfeesten, I., ... 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)).
@inproceedings{26e4ba6dce04456ca15644babbea8f1d,
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",
year = "2018",
doi = "10.1007/978-3-319-98648-7_19",
language = "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",

}

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 proceedingConference contributionAcademicpeer-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

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

UR - http://www.scopus.com/inward/record.url?scp=85053632158&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053632158&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-98648-7_19

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

A2 - Montali, Marco

A2 - Weber, Ingo

A2 - Weske, Mathias

A2 - vom Brocke, Jan

PB - Springer/Verlag

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, 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