EigenEvent：一种从复杂数据流中检测事件的算法
EigenEvent: An Algorithm for Event Detection from Complex Data Streams in Syndromic Surveillance
摘要：
症状监测系统持续监测来自不同区域的多种诊断前每日指标流，目的是及早发现疾病爆发。这些系统的主要目标是在临床和实验室确认前数小时或数天发现疫情。通过这些系统生成的数据类型通常是具有空间和时间维度的多变量和季节性数据。最近事件的奇怪之处（WSARE）算法是解决此类问题的最先进方法。它详尽地搜索多变量数据中的对比集，并在发现统计上显著的规则时发出警报。与现有的自上而下的方法相比，这种自下而上的方法具有更低的检测延迟。然而，WSARE对小规模的变化非常敏感，因此误报率相对较高。我们提出了一种新的方法，称为EigenEvent，既不是完全自上而下，也不是自下而上。在该方法中，我们通过特征空间技术跟踪数据相关结构的变化，而不是自顶向下或自底向上的搜索。这种新方法使我们能够检测整体变化（通过特征值）和维度级别的变化（通过特征向量）。在数百组基准数据上的实验结果表明，与最先进的方法相比，EigenEvent具有更好的整体性能，特别是在虚警率方面。
英文摘要：
Syndromic surveillance systems continuously monitor multiple pre-diagnostic daily streams of indicators from different regions with the aim of early detection of disease outbreaks. The main objective of these systems is to detect outbreaks hours or days before the clinical and laboratory confirmation. The type of data that is being generated via these systems is usually multivariate and seasonal with spatial and temporal dimensions. The algorithm What's Strange About Recent Events (WSARE) is the state-of-the-art method for such problems. It exhaustively searches for contrast sets in the multivariate data and signals an alarm when find statistically significant rules. This bottom-up approach presents a much lower detection delay comparing the existing top-down approaches. However, WSARE is very sensitive to the small-scale changes and subsequently comes with a relatively high rate of false alarms. We propose a new approach called EigenEvent that is neither fully top-down nor bottom-up. In this method, we instead of top-down or bottom-up search, track changes in data correlation structure via eigenspace techniques. This new methodology enables us to detect both overall changes (via eigenvalue) and dimension-level changes (via eigenvectors). Experimental results on hundred sets of benchmark data reveals that EigenEvent presents a better overall performance comparing state-of-the-art, in particular in terms of the false alarm rate.
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