发表机构
Technical University of Munich; fortiss GmbH; Blekinge Institute of Technology; University of Tartu（慕尼黑工业大学; fortiss有限公司; 布莱金厄理工学院; 塔尔图大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究通过系统文献综述梳理了流程挖掘事件日志生成的数据相关挑战、需求与解决方案，为相关领域研究者和从业者提供优化日志生成及流程挖掘成果的参考。
AI 中文摘要
流程挖掘正成为一项关键技术，不仅支持业务流程的优化，还为未来数字经济的底层软件技术提供支撑。在流程挖掘领域讨论的诸多挑战中，数据质量正成为最紧迫的问题。然而，作为流程挖掘首要阶段的事件日志生成，以及其与数据工程、需求工程的关联却未得到足够关注。为填补这一空白，我们对事件日志生成中与数据相关的挑战、需求及解决方案开展了系统文献综述。结果显示，29项不同的挑战可通过5类基本需求加以应对；现有解决方案已融入各类数据工程及领域知识工程技术用于事件日志生成。本综述可助力研究者与从业者理解流程挖掘领域事件日志生成的当前趋势，推动其利用数据与需求工程技术优化日志生成，最终提升流程挖掘的成果。
英文摘要
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.
CommentsAccepted to the 11th International Conference on Digital Economy (ICDEc), Tallinn, Estonia, May 2026
