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.
Originalsprache | Englisch |
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Titel | Geospatial Technologies for All : short papers, posters and poster abstracts of the 21th AGILE Conference on Geographic Information Science. Lund University 12-15 June 2018, Lund, Sweden |
Seitenumfang | 1 |
Publikationsstatus | Veröffentlicht - 2018 |
Veranstaltung | AGILE - Dauer: 12 Juni 2018 → 15 Juni 2018 |
Konferenz
Konferenz | AGILE |
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Zeitraum | 12/06/18 → 15/06/18 |
Research Field
- Ehemaliges Research Field - Mobility Systems
Schlagwörter
- AIS
- movement data
- trajectories
- data models
- maritime
- quad tree