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Process mining algorithms discover a process model from an event log. Theresulting process model is supposed to describe all possible event sequences ofthe underlying system. Generalization is a process model quality dimension ofinterest. A generalization metric should quantify the extent to which a processmodel represents the observed event sequences contained in the event log andthe unobserved event sequences of the system. Most of the available metrics inthe literature cannot properly quantify the generalization of a process model.A recently published method [1] called Adversarial System Variant Approximationleverages Generative Adversarial Networks to approximate the underlying eventsequence distribution of a system from an event log. While this methoddemonstrated performance gains over existing methods in measuring thegeneralization of process models, its experimental evaluations have beenperformed under ideal conditions. This paper experimentally investigates theperformance of Adversarial System Variant Approximation under non-idealconditions such as biased and limited event logs. Moreover, experiments areperformed to investigate the originally proposed sampling hyperparameter valueof the method on its performance to measure the generalization. The resultsconfirm the need to raise awareness about the working conditions of theAdversarial System Variant Approximation method. The outcomes of this paperalso serve to initiate future research directions.  [1] Theis, Julian, and Houshang Darabi. "Adversarial System VariantApproximation to Quantify Process Model Generalization." IEEE Access 8 (2020):194410-194427.

On the Performance Analysis of the Adversarial System Variant Approximation Method to Quantify Process Model Generalization