Process mining through artificial neural networks and support vector machines: A systematic literature review

Purpose – Process mining is a research area used to discover, monitor and improve real business processes by extracting knowledge from event logs available in process-aware information systems. The purpose of this paper is to evaluate the application of artificial neural networks (ANNs) and support vector machines (SVMs) in data mining tasks in the process mining context. The goal was to understand how these computational intelligence techniques are currently being applied in process mining. Design/methodology/approach – The authors conducted a systematic literature review with three research questions formulated to evaluate the use of ANNs and SVMs in process mining. Findings – The authors identified 11 papers as primary studies according to the criteria established in the review protocol. Most of them deal with process mining enhancement, mainly using ANNs. Regarding the data mining task, the authors identified three types of tasks used: categorical prediction (or classification); numeric prediction, considering the “regression” type, and clustering analysis. Originality/value – Although there is scientific interest in process mining, little attention has been specifically given to ANNs and SVM. This scenario does not reflect the general context of data mining, where these two techniques are widely used. This low use may be possibly due to a relative lack of knowledge about their potential for this type of problem, which the authors seek to reverse with the completion of this study.