<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The paper presents results of spatial analysis of huge volume of AIS data with the goal to detect predefined maritime anomalies. The maritime anomalies analysed have been grouped into: traffic analysis, static anomalies, and loitering detection. The analysis was carried out on data describing movement of tankers worldwide in 2015, using sophisticated algorithms and technology capable of handling big data in a fast and efficient manner. The research was conducted as a follow-up of the EDA-funded SIMMO project, which resulted in a maritime surveillance system based on AIS messages enriched with data acquired from open Internet sources.</p></abstract>

The paper presents results of spatial analysis of huge volume of AIS data with the goal to detect predefined maritime anomalies. The maritime anomalies analysed have been grouped into: traffic analysis, static anomalies, and loitering detection. The analysis was carried out on data describing movement of tankers worldwide in 2015, using sophisticated algorithms and technology capable of handling big data in a fast and efficient manner. The research was conducted as a follow-up of the EDA-funded SIMMO project, which resulted in a maritime surveillance system based on AIS messages enriched with data acquired from open Internet sources.

Big Data for Anomaly Detection in Maritime Surveillance: Spatial AIS Data Analysis for Tankers

Poznan University of Economics and Business

Maritime Technical Journal

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

The paper presents results of spatial analysis of huge volume of AIS data with the goal to detect predefined maritime anomalies. The maritime anomalies...

Big Data for Anomaly Detection in Maritime Surveillance: Spatial AIS Data Analysis for Tankers

Online veröffentlicht: 01. Feb. 2019

Seitenbereich: 5 - 28

Eingereicht: 08. Mai 2018

Akzeptiert: 12. Nov. 2018

DOI: https://doi.org/10.2478/sjpna-2018-0024

Schlüsselwörter
maritime surveillance, AIS data, anomaly detection, big data

© 2018 Dominik Filipiak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Big Data for Anomaly Detection in Maritime Surveillance: Spatial AIS Data Analysis for Tankers

Online veröffentlicht: 01. Feb. 2019

Seitenbereich: 5 - 28

Eingereicht: 08. Mai 2018

Akzeptiert: 12. Nov. 2018

DOI: https://doi.org/10.2478/sjpna-2018-0024

Schlüsselwörtermaritime surveillance, AIS data, anomaly detection, big data

© 2018 Dominik Filipiak et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
maritime surveillance, AIS data, anomaly detection, big data