随机已知日志的一致性检查

Eli Bogdanov, Izack Cohen, A. Gal
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引用次数: 6

摘要

随着设备、传感器和数字系统数量的不断增加,由于传感器读数不准确或处理程序对读数的不正确解释,数据日志可能变得不确定。有时,这种不确定性可以随机捕获,特别是在使用概率数据分类模型时。在这项工作中,我们关注一致性检查,当事件日志随机已知时,一致性检查将流程模型与事件日志进行比较。在现有的基于一致性检查的基础上,我们从数学上定义了一个随机跟踪模型、一个随机同步产品和一个反映日志中事件不确定性的成本函数。然后,我们在随机同步产品的可达性图上寻找模型与随机过程观测之间的最优对齐。通过两个著名的过程挖掘基准的结构化实验,我们探索了建议的随机一致性检查方法的行为,并将其与基于标准对齐的方法以及创建性能下界的方法进行了比较。我们设想提出的随机一致性检查方法作为未来随机事件日志分析的可行过程挖掘组件。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Conformance Checking Over Stochastically Known Logs
With the growing number of devices, sensors and digital systems, data logs may become uncertain due to, e.g., sensor reading inaccuracies or incorrect interpretation of readings by processing programs. At times, such uncertainties can be captured stochastically, especially when using probabilistic data classification models. In this work we focus on conformance checking, which compares a process model with an event log, when event logs are stochastically known. Building on existing alignment-based conformance checking fundamentals, we mathematically define a stochastic trace model, a stochastic synchronous product, and a cost function that reflects the uncertainty of events in a log. Then, we search for an optimal alignment over the reachability graph of the stochastic synchronous product for finding an optimal alignment between a model and a stochastic process observation. Via structured experiments with two well-known process mining benchmarks, we explore the behavior of the suggested stochastic conformance checking approach and compare it to a standard alignment-based approach as well as to an approach that creates a lower bound on performance. We envision the proposed stochastic conformance checking approach as a viable process mining component for future analysis of stochastic event logs.
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