Online Discovery of Congregate Groups on Sparse Spatio-temporal Data

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.