BINet：多视角的业务流程异常分类
BINet: Multi-perspective Business Process Anomaly Classification
摘要：
在本文中，我们介绍BINet，一个神经网络架构的实时多角度异常检测业务流程事件日志。BINet设计用于处理业务流程的控制流和数据透视图。此外，我们提出了一套自动设置异常检测算法的阈值的算法。我们证明，BINet可以用来检测事件日志中的异常，不仅在一个案件的水平，但也对事件属性级别。最后，我们证明了一组简单的规则可以用来利用BINet的输出进行异常分类。我们将BINet与其他八种最先进的异常检测算法进行比较，并在29个合成和15个真实事件日志的精心制作的数据语料库上评估其性能。BINet在合成数据集和真实数据集上都优于所有其他方法。
英文摘要：
In this paper, we introduce BINet, a neural network architecture for real-time multi-perspective anomaly detection in business process event logs. BINet is designed to handle both the control flow and the data perspective of a business process. Additionally, we propose a set of heuristics for setting the threshold of an anomaly detection algorithm automatically. We demonstrate that BINet can be used to detect anomalies in event logs not only on a case level but also on event attribute level. Finally, we demonstrate that a simple set of rules can be used to utilize the output of BINet for anomaly classification. We compare BINet to eight other state-of-the-art anomaly detection algorithms and evaluate their performance on an elaborate data corpus of 29 synthetic and 15 real-life event logs. BINet outperforms all other methods both on the synthetic as well as on the real-life datasets.
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。
