Local Cluster Based Biased Sampling of Trajectory Stream
AN Feng-liang · 2011
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 spatiotemporal changes.A novel biased sampling approach based on sliding window mo-del was proposed utilizing the property of local continuity.Firstly,through local clustering,the sliding window was divi-ded 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 quality.The extensive experiments verified the effectiveness of our algorithm.