Online conformance checking comes with new challenges, especially in terms of time and space constraints. One fundamental challenge of explaining the conformance of a running case is in balancing between making sense at the process level as the case reaches completion and putting emphasis on the current information at the same time. In this paper, we propose an online conformance checking framework that tackles this problem by incorporating the step of estimating the “location” of the case within the scope of the modeled process before conformance computation. This means that conformance checking is broken down into two steps: orientation and conformance. The two steps are related: knowing “where” the case is with respect to the process allows a conformance explanation that is more accurate and coherent at the process level and such conformance information in turn allows better orientations. Based on Hidden Markov Models (HMM), the approach works by alternating between orienting the running case within the process and conformance computation. An implementation is available as a Python package and experimental results show that the approach yields results that correlate with prefix alignment costs under both conforming and non-conforming scenarios while maintaining constant time and space complexity per event.

在线一致性检查带来了新的挑战，特别是在时间和空间限制方面。解释正在运行的案例的一致性的一个基本挑战是在案例达到完成时在流程级别上有意义和同时强调当前信息之间取得平衡。在本文中，我们提出了一个在线一致性检查框架，该解决方案通过在一致性计算之前将估计案例“位置”的步骤纳入建模过程的范围内来解决此问题。这意味着一致性检查分为两个步骤：方向和一致性。这两个步骤相关：知道案例相对于过程的“位置”可以使一致性说明在过程级别上更加准确和连贯，而这种一致性信息又可以实现更好的定位。基于隐马尔可夫模型（HMM），该方法通过在流程中确定运行案例的方向和一致性计算之间交替工作。提供了一个Python包实现，实验结果表明，该方法所产生的结果与符合和不符合情况下的前缀对齐成本相关，同时每个事件的时间和空间保持不变。