Project R-6412

Evaluating process model quality: do discovered process models only contain system behavior and nothing more? (Research)

Process mining concerns the discovery of process models based on observed process behavior. Over the last decades, many process discovery algorithms have been developed, each with their own strengths. To support further scientific progress in this domain, the community is in need of a strong evaluation framework for process discovery techniques. Currently, four building blocks for such a framework can be identified, i.e. a set of evaluation measures, an evaluation methodology, benchmark data sets and a programming environment to automate algorithm evaluation and comparison. The set of evaluation measures is the building block which has received most attention so far. The four most studied and applied quality dimensions are replay fitness, precision, generalization and simplicity. Until today, quantifying generalization, which measures the alignment of the discovered model with the true process, constitutes a persistent problem within process mining. The objective of this research proposal is therefore to improve a recently developed metric that aims at closing this research gap. This metric will estimate the likelihood that the discovered model produced the observed event log. In particular, the metric will allow both academia and practitioners to judge whether a model does not contain too much behavior, and thereby is suffering from a lack of realism.

01 October 2015 - 30 September 2017