Abstract:
We can create a process model from an event log of business process execution traces using automated process discovery. These techniques generate process models with several criteria, including fitness, which measures how well the model recognizes traces in the event log, and precision, which measures how well the model’s behavior is observed in the event log.
Literature suggests many fitness and precision measures. Existing measures in this field lack monotonicity and/or scalability when applied to models from real-life event logs. This article introduces a family of fitness and precision measures that compare the kth-order Markovian abstraction of a process model to an event log.
For suitable values of k, this family of measures has the above properties. This family of measures outperforms existing fitness and precision measures in execution times on real-life event logs and yields intuitive results on a synthetic dataset of model-log pairs.
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