Conformance checking techniques allow us to evaluate how well some exhibited behaviour, represented by a trace of monitored events, conforms to a specified process model. Modern monitoring and activity recognition technologies, such as those relying on sensors, the IoT, statistics and AI, can produce a wealth of relevant event data. However, this data is typically characterised by noise and uncertainty, in contrast to the assumption of a deterministic event log required by conformance checking algorithms. In this paper, we extend alignment-based conformance checking to function under a probabilistic event log. We introduce a weighted trace model and weighted alignment cost function, and a custom threshold parameter that controls the level of confidence on the event data vs. the process model. The resulting algorithm considers activities of lower but sufficiently high probability that better align with the process model. We explain the algorithm and its motivation both from formal and intuitive perspectives, and demonstrate its functionality in comparison with deterministic alignment using real-life datasets.

翻译：一致性检查技术可以评估被一系列监测事件所表示的某些行为与已指定的过程模型相符的程度。现代监测和活动识别技术，如传感器、物联网、统计和人工智能，可以产生大量相关的事件数据。然而，与一致性检查算法所需的确定性事件日志假设相反，这些数据通常表现为噪声和不确定性。在本文中，我们将基于对齐的一致性检查扩展到在概率事件日志下工作。我们引入加权跟踪模型以及加权对齐成本函数和定制阈值参数，该参数控制对事件数据和进程模型的置信水平。所得到的算法考虑了具有较低但足够高概率的活动，这些活动更好地对齐于流程模型。我们从形式和直观角度解释了该算法及其动机，并使用真实数据集说明了其功能与确定性对齐的比较。