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Process mining extracts value from the traces recorded in the event logs ofIT-systems, with process discovery the task of inferring a process model for alog emitted by some unknown system. Generalization is one of the qualitycriteria applied to process models to quantify how well the model describesfuture executions of the system. Generalization is also perhaps the leastunderstood of those criteria, with that lack primarily a consequence of itmeasuring properties over the entire future behavior of the system when theonly available sample of behavior is that provided by the log. In this paper,we apply a bootstrap approach from computational statistics, allowing us todefine an estimator of the model's generalization based on the log it wasdiscovered from. We show that standard process mining assumptions lead to aconsistent estimator that makes fewer errors as the quality of the logincreases. Experiments confirm the ability of the approach to supportindustry-scale data-driven systems engineering.

Bootstrapping Generalization of Process Models Discovered From Event Data