A deviation detection aims to detect deviating process instances, e.g.,
patients in the healthcare process and products in the manufacturing process. A
business process of an organization is executed in various contextual
situations, e.g., a COVID-19 pandemic in the case of hospitals and a lack of
semiconductor chip shortage in the case of automobile companies. Thus,
context-aware deviation detection is essential to provide relevant insights.
However, existing work 1) does not provide a systematic way of incorporating
various contexts, 2) is tailored to a specific approach without using an
extensive pool of existing deviation detection techniques, and 3) does not
distinguish positive and negative contexts that justify and refute deviation,
respectively. In this work, we provide a framework to bridge the aforementioned
gaps. We have implemented the proposed framework as a web service that can be
extended to various contexts and deviation detection methods. We have evaluated
the effectiveness of the proposed framework by conducting experiments using 255
different contextual scenarios.

本文提供了一个系统性的框架来整合不同的应用场景和偏差检测技术，实现上下文感知型的检测。该框架可以区分积极和消极的背景条件，并可被扩展至不同的上下文和场景中，并通过对 255 种不同情境下的实验验证了其有效性。

检测过程执行中的上下文感知偏差

Detecting Context-Aware Deviations in Process Executions

We discuss a method that employs a multilayer perceptron to detect deviations
from a reference model in large multivariate datasets. Our data analysis
strategy does not rely on any prior assumption on the nature of the deviation.
It is designed to be sensitive to small discrepancies that arise in datasets
dominated by the reference model. The main conceptual building blocks were
introduced in Ref. [1]. Here we make decisive progress in the algorithm
implementation and we demonstrate its applicability to problems in high energy
physics. We show that the method is sensitive to putative new physics signals
in di-muon final states at the LHC. We also compare our performances on toy
problems with the ones of alternative methods proposed in the literature.

本文介绍一种使用多层感知器在大型多变量数据集中检测偏差的方法，该方法设计灵敏度高且不需要任何先验假设，并在高能物理问题中得到了应用验证。