发表机构
University of Birmingham; Queen’s University Belfast（伯明翰大学; 贝尔法斯特女王大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究证明基于事件日志随机语言的标准过程挖掘方法无法区分并发与顺序行为，并提出通过记录时间或对象中心信息来恢复区分，从而影响资源规划决策。
AI 中文摘要
一家医院同时进行血液检查和影像检查，另一家则按顺序进行，顺序随机且频率相同。了解实际发生的情况以及其在数据中的记录方式，对运营管理者至关重要。在过程挖掘中，标准方法是构建事件日志，并尝试以数据驱动的方式发现并发和顺序过程。我们证明，这种基于事件日志随机语言的标准方法，仅报告其发现算法的假设，因为每个此类日志都能被一个完全没有并发性的模型同样好地解释。此外，在获取任何数据之前，我们刻画了数据何时能区分并发行为、何时不能。在无法区分的情况下，这种区别可以从随机语言丢弃的证据中恢复，例如活动开始和结束的时间，或固定执行内顺序的对象中心记录。因此，解决方法是选择记录什么，而非获取更大样本。这影响决策制定，因为为真正并发的服务规划资源与顺序服务截然不同。
英文摘要
One hospital runs bloods and imaging at the same time. Another runs them one after the other, in either order, equally often. Knowing which actually happened, and how it is recorded in data, is critical for all operational managers. In process mining, the standard approach is to construct an event log, and attempt to discover concurrent and sequential processes in a data-driven way. We show this standard approach, built on the stochastic language of an event log, reports only the assumptions of its discovery algorithm, because every such log is explained equally well by a model with no concurrency at all. Further, before any data is acquired, we characterise when data can and cannot distinguish concurrent behaviour. Where it cannot, the distinction is recoverable from evidence the stochastic language discards, such as the times at which activities start and end, or object-centric records that fix an order within an execution. The remedy is therefore a choice of what is recorded, rather than a larger sample. This impacts decision making, as planning resource for truly concurrent services is very different from sequential services.
Comments35 pages, 4 figures; 13-page supplementary material as an ancillary file
