Process mining starts from event data. The ordering of events is vital for the discovery of process models. However, the timestamps of events may be unreliable or imprecise. To further complicate matters, also causally unrelated events may be ordered in time. The fact that one event is followed by another does not imply that the former causes the latter. This paper explores the relationship between time and order. Moreover, it describes an approach to preprocess event data having timestamp-related problems. This approach avoids using accidental or unreliable orders and timestamps, creates partial orders to capture uncertainty, and allows for exploiting domain knowledge to (re)order events. Optionally, the approach also generates interleavings to be able to use existing process mining techniques that cannot handle partially ordered event data. The approach has been implemented using ProM and can be applied to any event log.

翻译：进程采矿从事件数据开始。 事件顺序对于发现过程模型至关重要。 但是, 事件的时间戳可能不可靠或不准确。 要进一步使问题复杂化, 还可以及时命令发生因果无关的事件。 一个事件之后又发生另一个事件并不意味着前者导致后者。 本文探讨了时间和顺序之间的关系。 此外, 它描述了处理预处理事件数据时标相关问题的方法。 这种方法避免使用意外或不可靠的命令和时间戳, 创建部分命令以捕捉不确定性, 并允许将域知识用于( 重) 顺序事件。 该方法还产生互连性, 以便能够使用无法处理部分定序事件数据的现有进程采矿技术。 该方法已经使用ProM 实施, 并可用于任何事件日志 。