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重构不可见的偏差事件:一种针对重复事件的一致性检查方法。

Reconstructing invisible deviating events: A conformance checking approach for recurring events.

机构信息

Business Information Systems Ⅱ, University of Trier, Behringstraße 21, Trier, 54292, Germany.

German Research Center for Artificial Intelligence (DFKI), Trier Branch, Behringstraße 21, Trier, 54292, Germany.

出版信息

Math Biosci Eng. 2022 Aug 16;19(11):11782-11799. doi: 10.3934/mbe.2022549.


DOI:10.3934/mbe.2022549
PMID:36124614
Abstract

Conformance checking enables organizations to determine whether their executed processes are compliant with the intended process. However, if the processes contain recurring activities, state-of-the-art approaches unfortunately have difficulties calculating the conformance. The occurrence of complex temporal rules can further increase the complexity of the problem. Identifying this limitation, this paper presents a novel approach towards dealing with recurring activities in conformance checking. The core idea of the approach is to reconstruct the missing events in the event log using defined rules while incorporating specified temporal event characteristics. This approach then enables the use of native conformance checking algorithms. The paper illustrates the algorithmic approach and defines the required temporal event characteristics. Furthermore, the approach is applied and evaluated in a case study on an event log for melanoma surveillance.

摘要

一致性检查使组织能够确定其执行的流程是否符合预期流程。然而,如果流程包含重复活动,那么最先进的方法在计算一致性方面存在困难。复杂的时间规则的出现会进一步增加问题的复杂性。本文针对一致性检查中重复活动的问题,提出了一种新的方法。该方法的核心思想是使用定义的规则在事件日志中重建缺失的事件,同时合并指定的时间事件特征。然后,该方法可以使用本地的一致性检查算法。本文阐述了算法方法并定义了所需的时间事件特征。此外,该方法还在黑色素瘤监测事件日志的案例研究中进行了应用和评估。

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