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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Dealing With Concept Drifts in Process Mining
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Dealing With Concept Drifts in Process Mining

机译：处理过程挖掘中的概念漂移

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摘要

Although most business processes change over time, contemporary process mining techniques tend to analyze these processes as if they are in a steady state. Processes may change suddenly or gradually. The drift may be periodic (e.g., because of seasonal influences) or one-of-a-kind (e.g., the effects of new legislation). For the process management, it is crucial to discover and understand such concept drifts in processes. This paper presents a generic framework and specific techniques to detect when a process changes and to localize the parts of the process that have changed. Different features are proposed to characterize relationships among activities. These features are used to discover differences between successive populations. The approach has been implemented as a plug-in of the ProM process mining framework and has been evaluated using both simulated event data exhibiting controlled concept drifts and real-life event data from a Dutch municipality.
机译：尽管大多数业务流程会随着时间而变化，但是现代流程挖掘技术倾向于将这些流程分析为处于稳定状态。过程可能会突然或逐渐改变。漂移可能是周期性的（例如由于季节性影响）或一种（例如新法规的影响）。对于流程管理，至关重要的是发现和理解流程中的此类概念漂移。本文提出了一个通用框架和特定技术，以检测流程何时发生更改并本地化已更改的部分。提出了不同的特征来表征活动之间的关系。这些特征用于发现连续种群之间的差异。该方法已实现为ProM流程挖掘框架的插件，并已使用显示受控概念偏差的模拟事件数据和来自荷兰市政当局的真实事件数据进行了评估。

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