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arXiv 2103.13315cs.SE
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连续检验逼近的模型无关误差界估计

Model Independent Error Bound Estimation for Conformance Checking Approximation

Mohammadreza Fani Sani, Martin Kabierski, Sebastiaan J. van Zelst, Wil M. P. van der Aalst

摘要:

一致性检查技术允许我们量化流程执行的一致性,在事件数据中捕获,w.r.t.,参考过程模型。在这种情况下,对齐已被证明是有用的计算一致性统计。然而,对于大量的事件数据和复杂的过程模型,对齐的计算时间相当高,阻碍了它们的实际应用。同时,对于许多应用,它足以近似对齐或其对应的一致性值。最近的工作表明,使用流程模型行为的子集可以得到准确的一致性近似。这种近似的准确性很大程度上取决于所选择的模型行为子集。因此,在本文中,我们表明,我们可以推导出一个先验的误差界的一致性检查近似的基础上任意的活动序列,独立于给定的过程模型。这样的误差界限随后让我们选择过程模型行为的最相关子集用于对齐近似。实验证实,当使用所提出的误差界近似来指导过程模型行为的相关子集的选择时,一致性近似的准确性得到了提高。

英文摘要:

Conformance checking techniques allow us to quantify the correspondence of a process's execution, captured in event data, w.r.t., a reference process model. In this context, alignments have proven to be useful for calculating conformance statistics. However, for extensive event data and complex process models, the computation time of alignments is considerably high, hampering their practical use. Simultaneously, it suffices to approximate either alignments or their corresponding conformance value(s) for many applications. Recent work has shown that using subsets of the process model behavior leads to accurate conformance approximations. The accuracy of such an approximation heavily depends on the selected subset of model behavior. Thus, in this paper, we show that we can derive a priori error bounds for conformance checking approximation based on arbitrary activity sequences, independently of the given process model. Such error bounds subsequently let us select the most relevant subset of process model behavior for the alignment approximation. Experiments confirm that conformance approximation accuracy improves when using the proposed error bound approximation to guide the selection of relevant subsets of process model behavior.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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