In this work we propose an algorithm for trace recovery from stochastically
known logs, a setting that is becoming more common with the increasing number
of sensors and predictive models that generate uncertain data. The suggested
approach calculates the conformance between a process model and a
stochastically known trace and recovers the best alignment within this
stochastic trace as the true trace. The paper offers an analysis of the impact
of various cost models on trace recovery accuracy and makes use of a product
multi-graph to compare alternative trace recovery options. The average accuracy
of our approach, evaluated using two publicly available datasets, is
impressive, with an average recovery accuracy score of 90-97%, significantly
improving a common heuristic that chooses the most likely value for each
uncertain activity. We believe that the effectiveness of the proposed algorithm
in recovering correct traces from stochastically known logs may be a powerful
aid for developing credible decision-making tools in uncertain settings.

本文提出了一种从随机已知日志中恢复跟踪的算法，该方法基于过程模型计算跟踪与日志的符合度，并利用产品多图比较替代的跟踪恢复方案。在两个公开数据集上的平均精度为 90-97％，在这些不确定性数据的不确定环境中提供了正确轨迹恢复的有效算法。