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Anomaly detection in performance management

机译:异常绩效管理中的检测

摘要

Methods and systems for detecting anomalous behavior include performing a principal component analysis on a plurality of key performance indicators (KPIs) to determine a set of principal axes. The KPIs are clustered in a space defined by the set of principal axes. Local and structural anomalies are determined in the clustered KPIs. The structural and local anomalies are classified based on historical information. A transformation is performed from a space based on the principal axes to an original space. It is determined whether each of the local and structural anomalies is a global or a local anomaly. A management action is performed based on the classified structural and local anomalies.
机译:用于检测异常行为的方法和系统包括对多个关键性能指示符(KPI)执行主成分分析以确定一组主轴。 KPI集聚在由一组主轴定义的空间中。局部和结构异常在聚类的KPI中确定。基于历史信息,结构和局部异常分类。从基于主轴到原始空间的空间执行变换。确定每个局部和结构异常是全球性还是局部异常。基于分类的结构和局部异常执行管理行动。

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