Local Cluster Based Biased Sampling of Trajectory Stream
基于局部聚类的轨迹数据流偏倚采样

Managing trajectories of moving objects is a research focus in mobile computing. Building data synopses by sampling technologies is one of the widely used method. But traditional uniform sampling usually discard some significant points that reveal relative spatiotcmporal changes. A novel biased sampling approach based on sliding window model was proposed utilizing the property of local continuity. Firstly, through local clustering, the sliding window was divided into various sized basic windows and sampling the data elements of a basic window using biased sampling rate, then forming trajectory stream synopses. This algorithm takes advantage of the intrinsic characteristics of trajectory stream and achieves superior approximation cauality. The extensive experiments verified the effectiveness of our algorithm.