Data-Related Challenges and Requirements for Event Log Generation in Process Mining: A Systematic Literature Review

Abstract

Process mining is becoming an essential technology that not only supports the improvement of business processes but also enables the software technologies underlying the future digital economy. Among the multiple challenges discussed by the process mining community, data quality is emerging as the most pressing one. However, insufficient attention has been paid to event log generation as the very first phase of process mining and to its relationship with data engineering and requirements engineering. To address this gap, we conducted a systematic literature review of data-related challenges, requirements, and solutions in event log generation. Our results show that a wide range of 29 challenges can be addressed with 5 basic categories of requirements. Existing solutions already embed some form of data engineering and domain knowledge engineering techniques for event log generation. This review can support researchers and practitioners in understanding current trends in event log generation for process mining and in leveraging data and requirements engineering techniques to improve log generation and, ultimately, process mining outcomes.