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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, the authors present a novel framework for monitoring anomalies over continuous trajectory streams. First, the authors illustrate the importance of distance-based anomaly monitoring over moving object trajectories. Then, they utilize the local continuity characteristics of trajectories to build local clusters upon trajectory streams and monitor anomalies via efficient pruning strategies.
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