document: Efficient anomaly monitoring over moving object trajectory streams Lately there exist increasing demands for online abnormality monitoring over trajectory streams , which are obtained from moving object tracking devices . This problem is challenging due to the requirement of high speed data processing within limited space cost . In this paper , we present a novel framework for monitoring anomalies over continuous trajectory streams . First , we illustrate the importance of distance-based anomaly monitoring over moving object trajectories . Then , we utilize the local continuity characteristics of trajectories to build local clusters upon trajectory streams and monitor anomalies via efficient pruning strategies . Finally , we propose a piecewise metric index structure to reschedule the joining order of local clusters to further reduce the time cost . Our extensive experiments demonstrate the effectiveness and efficiency of our methods .
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