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Complex Event Refinement by Statistical Augmentation Model

机译:统计增强模型对复杂事件的细化

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摘要

The uncertainty ofdecision making in event hierarchies of CEP can be due to unreliable data sources, lack of conformance that the event which is reported has actually occurred Also the Complex Event models which are used to define complex events are inaccurate. When the uncertain event is used for deriving complex event, it propagates its uncertainty to a higher level of event hierarchy and causes uncertainty in reasoning. This paper proposes an event refinement model based on statistical approach to augment the events to minimize the error due to uncertainty for better decision making. The proposed augmented CEP (a-CEP) is found to perform better in terms of reduction in false alarm for continuous monitoring ofpatient in a remote health care application. The proposed model is implemented on Drools Fusion CEP Engine using Java and it is found that the proposed a-CEP gives better results in terms of accuracy.
机译:CEP事件层次结构中决策制定的不确定性可能是由于数据源不可靠,缺乏所报告事件已实际发生的一致性以及用于定义复杂事件的复杂事件模型也不准确所致。当不确定事件用于导出复杂事件时,它将不确定性传播到更高的事件层次结构,并导致推理中的不确定性。本文提出了一种基于统计方法的事件细化模型,以增加事件数量,以最大程度地减少由于不确定性导致的错误,从而更好地制定决策。发现在减少误报方面,建议的增强型CEP(a-CEP)表现更好,可在远程医疗保健应用中对患者进行连续监视。所提出的模型是使用Java在Drools Fusion CEP Engine上实现的,发现所提出的a-CEP在准确性方面给出了更好的结果。

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