2009 EfficientAnomalyMonitoringoverM

• (Bu et al., 2009) ⇒ Yingyi Bu, Lei Chen, Ada Wai-Chee Fu, and Dawei Liu. (2009). “Efficient Anomaly Monitoring over Moving Object Trajectory Streams.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557043

Subject Headings:

Notes

• Categories and Subject Descriptors: H.2.4 Database Management: Systems — Multimedia Databases;H.2.8 Database Management: Database Application — Data Mining.
• General Terms: Algorithm, Design, Experimentation

Cited By

• http://scholar.google.com/scholar?q=%22Efficient+anomaly+monitoring+over+moving+object+trajectory+streams%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557043&preflayout=flat#citedby

Quotes

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. 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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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 EfficientAnomalyMonitoringoverM	Yingyi Bu Lei Chen Ada Wai-Chee Fu Dawei Liu			Efficient Anomaly Monitoring over Moving Object Trajectory Streams		KDD-2009 Proceedings		10.1145/1557019.1557043		2009