Abstract
In the context of business process intelligence, along with the need to extract a process model from a log, there is also the need to measure the quality of the extracted process model. Hence, process model quality notions and metrics are required. We present a systematic approach for developing quality metrics for block structured process models, which offer less expressive power than Petri-nets but have easier semantics. The metrics are based on tagging an initial block structured process model with self-loop and optional markings in order to explain all the instances in the given log. Then we transform the marked model to an equivalent maximal model by rewriting the self-loop and optional markings for consistency, and determine a badness score for it, which determines quality. Our approach is compared with related work, and a plan for testing and validation on noise-free and noisy data is discussed.
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Huang, Z., Kumar, A. (2009). New Quality Metrics for Evaluating Process Models. In: Ardagna, D., Mecella, M., Yang, J. (eds) Business Process Management Workshops. BPM 2008. Lecture Notes in Business Information Processing, vol 17. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-00328-8_16
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DOI: https://doi.org/10.1007/978-3-642-00328-8_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-00327-1
Online ISBN: 978-3-642-00328-8
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