While supporting the execution of business processes, information systems record event logs. Conformance checking relies on these logs to analyze whether the recorded behavior of a process conforms to the behavior of a normative specification. A key assumption of existing conformance checking techniques, however, is that all events are associated with timestamps that allow to infer a total order of events per process instance. Unfortunately, this assumption is often violated in practice. Due to synchronization issues, manual event recordings, or data corruption, events are only partially ordered. In this paper, we put forward the problem of partial order resolution of event logs to close this gap. It refers to the construction of a probability distribution over all possible total orders of events of an instance. To cope with the order uncertainty in real-world data, we present several estimators for this task, incorporating different notions of behavioral abstraction. Moreover, to reduce the runtime of conformance checking based on partial order resolution, we introduce an approximation method that comes with a bounded error in terms of accuracy. Our experiments with real-world and synthetic data reveal that our approach improves accuracy over the state-of-the-art considerably.

在支持业务流程执行的同时，信息系统记录事件日志。一致性检查依靠这些日志来分析过程的记录行为是否符合规范规范的行为。但是，现有一致性检查技术的一个关键假设是，所有事件都与时间戳关联，从而可以推断每个流程实例的事件总顺序。不幸的是，这种假设在实践中经常被违反。由于同步问题，手动事件记录或数据损坏，事件仅部分排序。在本文中，我们提出了事件日志的部分顺序解析问题，以弥补这一差距。它是指在一个实例的所有可能事件的总阶上进行概率分布的构造。为了应对现实世界数据中的顺序不确定性，我们提供了针对此任务的几种估算器，其中纳入了行为抽象的不同概念。此外，为了减少基于偏序分辨率的一致性检查的运行时间，我们引入了一种近似方法，该方法在准确性方面存在一定的误差。我们对现实世界和合成数据的实验表明，我们的方法大大提高了精度。