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.

翻译：本文提出了一种从随机已知日志中恢复轨迹的算法，此类场景随着生成不确定数据的传感器与预测模型日益增多而愈发常见。所提出的方法计算过程模型与随机已知轨迹之间的一致性，并将该随机轨迹中的最佳对齐作为真实轨迹进行恢复。论文分析了不同成本模型对轨迹恢复准确性的影响，并利用乘积多图来比较替代性的轨迹恢复方案。通过在两个公开数据集上进行评估，我们的方法平均恢复准确率达到了90–97%，显著优于那种为每个不确定活动选择最可能取值的常见启发式方法。我们相信，所提出的算法在从随机已知日志中恢复正确轨迹方面的有效性，可为开发不确定性环境下的可信决策工具提供有力支持。