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 | |
| 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 |
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Keywords
- Classification of modelers
- Model layout
- Process modeling
Cite this
}
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
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 -