Initially, process mining focused on discovering process models from event data, but in recent years the use and importance of conformance checking has increased. Conformance checking aims to uncover differences between a process model and an event log. Many conformance checking techniques and measures have been proposed. Typically, these take into account the frequencies of traces in the event log, but do not consider the probabilities of these traces in the model. This asymmetry leads to various complications. Therefore, we define conformance for stochastic process models taking into account frequencies and routing probabilities. We use the earth movers’ distance between stochastic languages representing models and logs as an intuitive conformance notion. In this paper, we show that this form of stochastic conformance checking enables detailed diagnostics projected on both model and log. To pinpoint differences and relate these to specific model elements, we extend the so-called ‘reallocation matrix’ to consider paths. The approach has been implemented in ProM and our evaluations show that stochastic conformance checking is possible in real-life settings.

最初，过程挖掘的重点是从事件数据中发现过程模型，但是近年来，一致性检查的用途和重要性不断提高。一致性检查旨在发现流程模型和事件日志之间的差异。已经提出了许多一致性检查技术和措施。通常，这些考虑了事件日志中跟踪的频率，但没有考虑模型中这些跟踪的概率。这种不对称导致各种并发症。因此，我们考虑频率和路由概率为随机过程模型定义一致性。我们将代表模型和日志的随机语言之间的推土机距离用作直观的一致性概念。在本文中，我们表明，这种形式的随机一致性检查可以在模型和日志上进行详细的诊断。为了查明差异并将其与特定的模型元素相关联，我们扩展了所谓的“重新分配矩阵”以考虑路径。该方法已在ProM中实施，我们的评估表明，在现实生活中可以进行随机一致性检查。