Process mining techniques are used to extract knowledge about the efficiency and compliance of an organization’s business processes through process models. Real-life processes are unstructured, and applying process mining to discover such processes often results in complex process models that do not provide actionable insights. Several solutions have been presented to overcome this problem. However, the process mining domain lacks an explicit definition of complexity and its measurement. This vagueness results in ad-hoc solutions that vary according to the approach, modelling construct, and process properties. Additionally, the strength and limitations of the proposed solutions have not been adequately highlighted. Therefore, we conducted a systematic literature review on complexity in process mining over six popular scholarly indexing databases. Based on the review results, an explicit definition of complexity, the main contributing factors and their impact on process mining results were identified. We discovered various process complexity matrices and their application context. The analysis of studies led to the development of a taxonomy consisting of four different approaches for addressing the complexity problem, along with their strengths and limitations. Finally, the open research challenges and potential for future research are discussed.
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