Empirical Studies of Three Commonly Used Process Mining Algorithms

Abstract

Process mining aims to extract useful process knowledge and provide valuable insights to better understand, monitor, and improve current business processes. The most critical learning task in process mining is process discovery. Process discovery takes an event log as an input and generates a process model as an output. In the last two decades, processing mining communities have proposed several process discovery algorithms. Many of these algorithms are based on or are extensions of three commonly used process mining algorithms. These algorithms are known as the α algorithm, the Heuristic algorithm and the Inductive algorithm. This study provides an evaluation of these three algorithms using both artificial event logs and real-life event logs. We study the impact of dependency patterns, noise, and complexity. Our work aims to provide clear guidelines for academics or business organizations that are interested in using process mining algorithms to discover their hidden process models and choose the most appropriate process discovery algorithm.