Process mining, as with any form of data analysis, relies heavily on the quality of input data to generate accurate and reliable results. A fit-for-purpose event log nearly always requires time-consuming, manual pre-processing to extract events from source data, with data quality dependent on the analyst's domain knowledge and skills. Despite much being written about data quality in general, a generalisable framework for analysing event data quality issues when extracting logs for process mining remains unrealised. Following the DSR paradigm, we present RDB2Log, a quality-aware, semi-automated approach for extracting event logs from relational data. We validated RDB2Log's design against design objectives extracted from literature and competing artifacts, evaluated its design and performance with process mining experts, implemented a prototype with a defined set of quality metrics, and applied it in laboratory settings and in a real-world case study. The evaluation shows that RDB2Log is understandable, of relevance in current research, and supports process mining in practice.

与任何形式的数据分析一样，过程挖掘在很大程度上依赖于输入数据的质量来生成准确而可靠的结果。适合目的的事件日志几乎总是需要耗时的手动预处理才能从源数据中提取事件，而数据质量取决于分析师的领域知识和技能。尽管关于数据质量的书面论述很多，但是在提取用于流程挖掘的日志时分析事件数据质量问题的通用框架仍然没有实现。遵循DSR范式，我们介绍RDB2Log，这是一种质量感知的半自动化方法，用于从关系数据中提取事件日志。我们根据从文献和竞争工件中提取的设计目标对RDB2Log的设计进行了验证，并与流程挖掘专家一起评估了其设计和性能，实施了具有一组定义的质量指标的原型，并将其应用于实验室设置和实际案例研究中。评估表明，RDB2Log是可理解的，与当前研究相关，并且在实践中支持过程挖掘。