Scalable Spatial Scan Statistics for Trajectories
Scalable Spatial Scan Statistics for Trajectories
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DOI：
10.1145/3394046
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发表时间：
2019-06
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通讯作者：
Michael Matheny;Dong Xie;J. M. Phillips
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作者：
Michael Matheny;Dong Xie;J. M. Phillips
我们定义了几个新的模型，用于如何在巨大的轨迹集中定义异常区域。这些基于空间扫描统计，并确定一个几何区域，该区域捕获轨迹子集，这些轨迹子集在测量特征上与背景种群显着不同。模型的定义取决于一个几何区域有多少是由一些重叠的轨迹贡献的。这种贡献可以是整个轨迹，与空间区域内的长度成正比，也可以取决于穿越该空间区域边界的通量。我们的方法基于并显著扩展了最近的两级采样方法，该方法在大量数据中提供了高精度。为了支持这些新的模型和算法，我们对数百万个轨迹进行了大量的实验，并提供了理论保证。
We define several new models for how to define anomalous regions among enormous sets of trajectories. These are based on spatial scan statistics, and identify a geometric region which captures a subset of trajectories which are significantly different in a measured characteristic from the background population. The model definition depends on how much a geometric region is contributed to by some overlapping trajectory. This contribution can be the full trajectory, proportional to the length within the spatial region, or dependent on the flux across the boundary of that spatial region. Our methods are based on and significantly extend a recent two-level sampling approach which provides high accuracy at enormous scales of data. We support these new models and algorithms with extensive experiments on millions of trajectories and also theoretical guarantees.