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ARTÍCULO
TITULO

Simplification and Detection of Outlying Trajectories from Batch and Streaming Data Recorded in Harsh Environments

Iq Reviessay Pulshashi    
Hyerim Bae    
Hyunsuk Choi    
Seunghwan Mun and Riska Asriana Sutrisnowati    

Resumen

Analysis of trajectory such as detection of an outlying trajectory can produce inaccurate results due to the existence of noise, an outlying point-locations that can change statistical properties of the trajectory. Some trajectories with noise are repairable by noise filtering or by trajectory-simplification. We herein propose the application of a trajectory-simplification approach in both batch and streaming environments, followed by benchmarking of various outlier-detection algorithms for detection of outlying trajectories from among simplified trajectories. Experimental evaluation in a case study using real-world trajectories from a shipyard in South Korea shows the benefit of the new approach.

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