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Online Discovery of Congregate Groups on Sparse Spatio-temporal Data

机译:在稀疏的时空数据上在线发现聚集组

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The pervasiveness of location-acquisition technologies leads to large amounts of spatio-temporal data, which brings us opportunities and challenges to discover interesting group patterns from these individual's trajectories. In this work, firstly, we propose a novel group pattern called congregate group, which captures various congregations by exploiting trajectory streams. Then, we design a discovery framework which contains three main stages including trajectory preprocessing, crowds generation and congregate groups discovery to detect congregations. Meanwhile, an interpolation method is proposed to handle missing points on sparse data. Besides, a set of optimization techniques is applied to reduce computational costs. Finally, our extensive experiments based on real cellular network dataset and real taxicab trajectory dataset demonstrate the effectiveness, efficiency and scalability of our proposed approach.
机译:地点采集技术的普及能力导致大量的时空数据,为我们提供了来自这些个人轨迹的有趣组模式的机会和挑战。在这项工作中,首先,我们提出了一种名为“聚集集团的新型集团模式,通过利用轨迹流来捕获各种会众。然后,我们设计一个发现框架,其中包含三个主要阶段,包括轨迹预处理,人群生成和聚集团发现来检测会众。同时,提出了一种在稀疏数据上处理缺失点的插值方法。此外,应用了一组优化技术以降低计算成本。最后,我们基于真正的蜂窝网络数据集和真正的出租车轨迹数据集的广泛实验证明了我们所提出的方法的有效性,效率和可扩展性。

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