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Scalable distributed change detection and its application to maritime traffic

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Abstract
Building on a novel methodology based on the Ornstein-Uhlenbeck (OU) process to perform accurate long-term predictions of future positions of ships at sea, we present a statistical approach to the detection of abrupt changes in the process parameter that represents the desired velocity of a ship. Proceeding from well-established change detection techniques, the proposed strategy is also computationally efficient and fit well with big data processing models and paradigms. We report results with a large real-world Automatic Identification System (AIS) data set collected by a network of terrestrial receivers in the Mediterranean Sea from June to August 2016.

URI
http://hdl.handle.net/20.500.12489/793

Report Number
CMRE-PR-2019-053

Source
In: 2017 IEEE International Conference on Big Data (Big Data), 11-14 December 2017, Boston, MA, USA, pp. 1650-1657, doi: 10.1109/BigData.2017.8258101

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Date
2019/06

Author(s)
Millefiori, Leonardo
; 
Braca, Paolo
; 
Arcieri, Gianfranco

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CMRE-PR-2019-053.pdf (4.260Mb)

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