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

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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
PublisherSpringer
Publication date2018
Pages322-338
ISBN (Print)978-3-319-98647-0
DOIs
Publication statusPublished - 2018
Event16th International Conference on Business Process Management - University of Technology in Sydney, Sydney, Australia
Duration: 9 Sep 201814 Sep 2018
Conference number: 16
http://bpm2018.web.cse.unsw.edu.au/

Conference

Conference16th International Conference on Business Process Management
Number16
LocationUniversity of Technology in Sydney
CountryAustralia
CitySydney
Period09/09/201814/09/2018
Internet address
SeriesLecture Notes in Computer Science
Volume11080
ISSN0302-9743

Keywords

  • Process Modeling
  • Classification of modelers
  • Model layout

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