2014 DetectingMovingObjectOutliersin

• (Yu et al., 2014) ⇒ Yanwei Yu, Lei Cao, Elke A. Rundensteiner, and Qin Wang. (2014). “Detecting Moving Object Outliers in Massive-scale Trajectory Streams.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623735

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Detecting+Moving+Object+Outliers+in+Massive-scale+Trajectory+Streams
• http://dl.acm.org/citation.cfm?id=2623330.2623735&preflayout=flat#citedby

Quotes

Author Keywords

• Data mining; moving object; outlier; trajectory stream

Abstract

The detection of abnormal moving objects over high-volume trajectory streams is critical for real time applications ranging from military surveillance to transportation management. Yet this problem remains largely unexplored. In this work, we first propose classes of novel trajectory outlier definitions that model the anomalous behavior of moving objects for a large range of real time applications. Our theoretical analysis and empirical study on the Beijing Taxi and GMTI (Ground Moving Target Indicator) datasets demonstrate its effectiveness in capturing abnormal moving objects. Furthermore we propose a general strategy for efficiently detecting the new outlier classes. It features three fundamental optimization principles designed to minimize the detection costs. Our comprehensive experimental studies demonstrate that our proposed strategy drives the detection costs 100-fold down into practical realm for applications producing high volume trajectory streams to utilize.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 DetectingMovingObjectOutliersin	Yanwei Yu Lei Cao Elke A. Rundensteiner Qin Wang			Detecting Moving Object Outliers in Massive-scale Trajectory Streams				10.1145/2623330.2623735		2014