Sampling business process event logs with guarantees

Event log sampling has emerged as a key research focus in the field of process mining, aiming to enhance the efficiency of various process mining tasks, including model discovery, conformance checking, and process prediction. However, current log sampling techniques often fail to ensure high-quality sample logs. This paper introduces a novel framework to support efficient event log sampling without compromising the quality of the sample log compared to the original one. The approach revolves around the consideration of directly-follows relation (DFR) among business tasks as the fundamental behavior unit of an event log. By ensuring the DFR equivalence between the original and sample logs, the proposed technique addresses the challenge of sample log quality from the model discovery point of view. The framework is instantiated by seven distinct sampling strategies each has its own specialty and is fully implemented in the open-source process mining tool platform ProM. To validate its effectiveness, we conducted a comprehensive experimental evaluation using 12 publicly available real-life event logs against state-of-the-art sampling techniques. The results clearly demonstrate that our technique significantly improves model discovery efficiency while upholding high quality of the discovered models.