Process Profiling based Synthetic Event Log Generation

Eren Esgin, Pinar Karagoz

Abstract

The goal of process mining is to discover the process behavior from the runtime information of process executions. Having labeled process logs is crucial for process mining research. However, real life event logs at process-aware information systems are mostly partially assigned to case identifiers, known as unlabeled event log problem. As a remedy to labeled data need in process mining research, we propose an approach to generate synthetic event logs according to the provided process profile, which outlines the activity vocabulary and structure of the corresponding business process. We evaluate the performance of our prototypical implementation in term of compatible log generation under varying parameter setting complexities.

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