One aim of Process Mining (PM) is the discovery of process models from event logs of information systems. PM has been successfully applied to process-oriented enterprise systems but is less suited for communication- and document-oriented Enterprise Collaboration Systems (ECS). ECS event logs are very fine-granular and PM applied to their logs results in spaghetti models. A common solution for this is event abstraction, i.e., converting low-level logs into more abstract high-level logs before running discovery algorithms. ECS logs come with special characteristics that have so far not been fully addressed by existing event abstraction approaches. We aim to close this gap with a tailored ECS event abstraction (ECSEA) approach that trains a model by comparing recorded actual user activities (high-level traces) with the system-generated low-level traces (extracted from the ECS). The model allows us to automatically convert future low-level traces into an abstracted high-level log that can be used for PM. Our evaluation shows that the algorithm produces accurate results. ECSEA is a preprocessing method that is essential for the interpretation of collaborative work activity in ECS, which we call Social Process Mining.

翻译：过程挖掘（Process Mining, PM）的目标之一是从信息系统的日志中发现过程模型。PM已成功应用于面向过程的企业系统，但较不适用于以通信和文档为导向的企业协作系统（Enterprise Collaboration Systems, ECS）。ECS事件日志的粒度非常细，对其日志直接应用PM会导致“意大利面条式”模型。解决此问题的常见方法是事件抽象，即在运行发现算法前将低层级日志转换为更抽象的高层级日志。ECS日志具有现有事件抽象方法尚未完全解决的特殊特征。我们旨在通过一种定制的ECS事件抽象（ECSEA）方法填补这一空白，该方法通过比较记录的实际用户活动（高层级轨迹）与系统生成的低层级轨迹（从ECS中提取）来训练模型。该模型使我们能够自动将未来的低层级轨迹转换为可用于PM的抽象化高层级日志。我们的评估表明，该算法能生成准确的结果。ECSEA是一种预处理方法，对于解读ECS中的协作工作活动（我们称之为“社会过程挖掘”）至关重要。