Andrea Burattin, Ph.D.

Assistant Professor

Who is behind the Model? Classifying Modelers based on Pragmatic Model Features

Abstract

Journal cover

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.

Paper Information and Files

Published in Proceedings of BPM 2018; Sydney, Australia; September 2018.

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