Modeling massive AIS streams with quad trees and Gaussian Mixtures

Anita Graser (Vortragende:r), Peter Widhalm

Publikation: Beitrag in Buch oder TagungsbandVortrag mit Beitrag in TagungsbandBegutachtung

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.
OriginalspracheEnglisch
TitelGeospatial 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
Seitenumfang1
PublikationsstatusVeröffentlicht - 2018
VeranstaltungAGILE -
Dauer: 12 Juni 201815 Juni 2018

Konferenz

KonferenzAGILE
Zeitraum12/06/1815/06/18

Research Field

  • Ehemaliges Research Field - Mobility Systems

Schlagwörter

  • AIS
  • movement data
  • trajectories
  • data models
  • maritime
  • quad tree

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