Modelling massive AIS streams with quad trees and Gaussian Mixtures

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Authors

Anita Graser , Peter Widhalm

Abstract

Pressing issues related to the movement of people and goods can be tackled today thanks to improvements in tracking and communications technology that have made it possible to collect movement data on a big scale. Maritime data from the Automatic Identification System (AIS) is one of the fast growing sources of movement data. Existing approaches for AIS data analysis suffer from scalability issues. Therefore, scalable distributed modelling and analysis approaches are needed. This paper presents a novel scalable movement data model that takes advantage of an adaptive grid based on quad trees. Our data model supports anomaly detection in massive movement data streams by combining advantages of both grid and vector-based approaches. We demonstrate the applicability of this approach for anomaly detection in AIS datasets comprising 560 million location records.

DOI

https://doi.org/10.31223/osf.io/sz34w

Subjects

Computer Sciences, Other Computer Sciences, Physical Sciences and Mathematics

Keywords

data model, AGILEGIS, AGILE short paper, AIS, maritime, movement data, quad tree, trajectories

Dates

Published: 2020-06-29 16:16

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License

GNU Lesser General Public License (LGPL) 2.1

Additional Metadata

Conflict of interest statement:
Note on manuscript status: This short paper has been previously published in AGILE conference proceedings without DOI. It is a new AGILE policy that authors have permission to deposit AGILE short papers in a public repository, such as a preprint server or institutional repositories to ensure that the works are preserved and citable with a DOI, see: https://agile-online.org/conference/proceedings

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