Owing to a wide variety of deployment of GPS -enabled devices, tremendous amounts of trajectories have been generated in distributed stream manner. It opens up new opportunities to track and analyze the moving behaviors of the entities. In this work, we focus on the issue of outlier detection over distributed trajectory streams, where the outliers refer to a few entities whose motion behaviors are significantly different from their local neighbors. In view of skewed distribution property and evolving nature of trajectory data, and on-the-fly detection requirement over distributed streams, we first design a high-efficiency outlier detection solution. It consists of identifying abnormal trajectory fragment and exceptional fragment cluster at the remote sites and then detecting abnormal evolving object at the coordinator site. Further, given that outlier detection accuracy would be damaged due to using inappropriate proximity thresholds or a few trajectory data not having sufficient neighbors at the remote sites, we extract proximity thresholds of different regions and spatial context relationship of each region from historical data to improve the precision. Built upon this is an improved version consisting of off-line modeling phase and on-line detection phase. During the on-line phase, the proximity thresholds that are derived from historical trajectories during the off-line phase are leveraged to assist in detecting abnormal trajectory fragments and exceptional fragment clusters at the remote sites. Additionally, at the coordinator site, the detection results of some remote sites can be refined by incorporating those of other remote sites with neighborhood relationship. Extensive experimental results on real data demonstrate that our proposed methods own high detection validity, less communication cost and linear scalability for online identifying outliers over distributed trajectory streams.

中文翻译：

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分布式流上基于特征分组的轨迹异常值检测

由于部署范围广泛全球定位系统启用的设备，大量的轨迹已经以分布式流的方式生成。它为跟踪和分析实体的移动行为开辟了新的机会。在这项工作中，我们关注分布式轨迹流上的异常值检测问题，其中异常值是指一些运动行为与其本地邻居显着不同的实体。鉴于轨迹数据的偏态分布特性和演化特性，以及对分布式流的动态检测需求，我们首先设计了一种高效的异常值检测解决方案。它包括在远程站点识别异常轨迹碎片和异常碎片簇，然后在协调器站点检测异常演化对象。进一步，考虑到使用不适当的邻近阈值或在远程站点没有足够邻居的少数轨迹数据会损害异常值检测的准确性，我们从历史数据中提取不同区域的邻近阈值和每个区域的空间上下文关系以提高精度。在此基础上是一个改进版本，包括离线建模阶段和在线检测阶段。在在线阶段，利用离线阶段从历史轨迹中得出的接近阈值来辅助检测远程站点的异常轨迹片段和异常片段簇。此外，在协调站点，可以通过合并其他具有邻域关系的远程站点的检测结果来细化一些远程站点的检测结果。