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：一致性检测技术使我们能够评估由监控事件轨迹表示的展现行为与指定流程模型的一致程度。现代监控与活动识别技术（如依赖传感器、物联网、统计学及人工智能的技术）能够产生大量相关事件数据。然而，与一致性检测算法所需确定性事件日志的假设不同，这些数据通常具有噪声和不确定性的特征。本文将对齐的一致性检测方法扩展至概率事件日志场景。我们引入了加权轨迹模型与加权对齐代价函数，并设置自定义阈值参数，以控制事件数据可信度与流程模型置信度之间的权衡。所提出的算法会考虑概率较低但足够高且与流程模型更匹配的活动。本文从形式化与直观视角阐述了该算法及其动机，并通过真实数据集上的对比实验展示了其与确定性对齐的功能差异。