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 |
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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 |
Pages | 322-338 |
Number of pages | 17 |
ISBN (Print) | 9783319986470 |
DOIs | |
Publication status | Published - 1 Jan 2018 |
Event | 16th International Conference on Business Process Management (BPM 2018) - Sydney, Australia Duration: 9 Sept 2018 → 14 Sept 2018 Conference number: 16 http://ceur-ws.org/Vol-2196/ |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11080 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 16th International Conference on Business Process Management (BPM 2018) |
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Abbreviated title | BPM 2018 |
Country/Territory | Australia |
City | Sydney |
Period | 9/09/18 → 14/09/18 |
Other | Dissertation Award, Demonstration, and Industrial Track at BPM |
Internet address |
Keywords
- Classification of modelers
- Model layout
- Process modeling