Efficient Anomaly Monitoring Over Moving Object Trajectory Streams

　

Yingyi Bu, Lei Chen, Ada Wai-Chee Fu, Dawei Liu

　

In The 15th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'09),  Paris, France, June 28–July 1, 2009

　

　

Abstract

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. To further reduce the time cost, we propose a piecewise metric index structure to reschedule the joining order of local clusters. Finally, our extensive experiments demonstrate the effectiveness and efficiency of our methods.

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Datasets and Descriptions

• GE (description)
• Movement (description)
• Spaceshuttle (description) [Note: slightly different from the original dataset in UCR Time Series Archive, details in descriptions]
• Random-walk (description)
• Altitude (description)
• Latitude (description)
• Longitude (description)

Executable Program and Source Code

　

   [Windows] [Linux] [Readme] [Source Code]