Fusion-Based Process Discovery

Information systems record the execution of transactions as part of business processes in event logs. Process mining analyses such event logs, e.g., by discovering process models. Recently, various discovery algorithms have been proposed, each with specific advantages and limitations. In this work, we argue that, instead of relying on a single algorithm, the outcomes of different algorithms shall be fused to combine the strengths of individual approaches. We propose a general framework for such fusion and instantiate it with two new discovery algorithms: The Exhaustive Noise-aware Inductive Miner (exNoise), which, exhaustively searches for model improvements; and the Adaptive Noise-aware Inductive Miner (adaNoise), a computationally tractable version of exNoise. For both algorithms, we formally show that they outperform each of the individual mining algorithms used by them. Our empirical evaluation further illustrates that fusion-based discovery yields models of better quality than state-of-the-art approaches.