Abstract
In the domain of process discovery, there are four quality dimensions for evaluating process models of which simplicity is one. Simplicity is often measured using the size of a process model, the structuredness and the entropy. It is closely related to the process model understandability. Researchers from the domain of business process management (BPM) proposed several metrics for measuring the process model understandability. A part of these understandability metrics focus on the control-flow perspective, which is important for evaluating models from process discovery algorithms. It is remarkable that there are more of these metrics defined in the BPM literature compared to the number of proposed simplicity metrics. To research whether the understandability metrics capture more understandability dimensions than the simplicity metrics, an exploratory factor analysis was conducted on 18 understandability metrics. A sample of 4450 BPMN models, both manually modelled and artificially generated, is used. Four dimensions are discovered: token behaviour complexity, node IO complexity, path complexity and degree of connectedness. The conclusion of this analysis is that process analysts should be aware that the measurement of simplicity does not capture all dimensions of the understandability of process models.
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Lieben, J., Jouck, T., Depaire, B., Jans, M. (2018). An Improved Way for Measuring Simplicity During Process Discovery. In: Pergl, R., Babkin, E., Lock, R., Malyzhenkov, P., Merunka, V. (eds) Enterprise and Organizational Modeling and Simulation. EOMAS 2018. Lecture Notes in Business Information Processing, vol 332. Springer, Cham. https://doi.org/10.1007/978-3-030-00787-4_4
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