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.

中文翻译：

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轨迹的可扩展空间扫描统计

我们定义了几个新模型来定义大量轨迹中的异常区域。这些基于空间扫描统计数据，并识别一个几何区域，该区域捕获轨迹子集，这些轨迹子集的测量特征与背景人口显着不同。模型定义取决于某个重叠轨迹对几何区域的贡献程度。这种贡献可以是完整的轨迹，与空间区域内的长度成比例，或者取决于穿过该空间区域边界的通量。我们的方法基于并显着扩展了最近的两级采样方法，该方法可在海量数据下提供高精度。