On Recommendation of Process Mining Algorithms

Jianmin Wang, Raymond K. Wong, Jianwei Ding, Qinlong Guo, Lijie Wen

Abstract: While many process mining algorithms have been proposed recently, there does not exist a widely-accepted benchmark to evaluate and compare these process mining algorithms. As a result, it can be difficult to choose a suitable process mining algorithm for a given enterprise or application domain. Some recent benchmark systems have been developed and proposed to address this issue.  However, evaluating available process mining algorithms against a large set of business models (e.g., in a large enterprise) can be computationally expensive, tedious and time-consuming. This paper proposes a novel framework that can efficiently select the process mining algorithms that are  most suitable for a given model set. In particular, it attemptsto investigate how we can avoid evaluating numerous process mining algorithms on all given process models.