Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events

  title={Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events},
  author={George A. Vouros and Akrivi Vlachou and Georgios M. Santipantakis and Christos Doulkeridis and Nikos Pelekis and Harris V. Georgiou and Yannis Theodoridis and Kostas Patroumpas and Elias Alevizos and A. Artikis and Georg Fuchs and Michael Mock and Gennady L. Andrienko and Natalia V. Andrienko and Christophe Claramunt and Cyril Ray and Elena Camossi and Anne-Laure Jousselme},
The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories’ detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration… 
Big Data Analytics for Time-Critical Mobility Forecasting: From Raw Data to Trajectory-Oriented Mobility Analytics in the Aviation and Maritime Domains
This chapter overviews maritime operational situations and underlying challenges that the automated processing of maritime mobility data would support with the detection of threats and abnormal activities, and presents relevant data sources to be exploited for operational purposes in the maritime domain.
CRISIS: Integrating AIS and Ocean Data Streams Using Semantic Web Standards for Event Detection
  • A. Soares, R. Dividino, S. Matwin
  • Computer Science
    2019 International Conference on Military Communications and Information Systems (ICMCIS)
  • 2019
This paper presents an agile data architecture for real-time data representation, integration, and querying situations over heterogeneous data streams using Semantic Web Technologies, with the goal of improved knowledge interoperability.
Moving Objects Analytics: Survey on Future Location & Trajectory Prediction Methods
This paper focuses on predictive analytics for moving objects and surveys the state-of-the-art in the context of future location and trajectory prediction and proposes a novel taxonomy of predictive algorithms over moving objects.
Event Processing for Maritime Situational Awareness
These efforts to combine two stream reasoning technologies for detecting illegal activities in real time are presented: a formal, computational framework for composite maritime event recognition, based on the Event Calculus, and an industry-strong maritime anomaly detection service, capable of processing daily real-world data volumes.
Near Real-time S-AIS
Complementing with terrestrial AIS and other technologies, near real-time S-AIS can further enhance all areas of the global maritime monitoring domain with emerging possibilities for maritime industry.


Online event recognition from moving vessel trajectories
Extensive tests validate the performance, efficiency, and robustness of the system against scalable volumes of real-world and synthetically enlarged datasets, but its deployment against online feeds from vessels has also confirmed its capabilities for effective, real-time maritime surveillance.
Web Architecture for Monitoring and Visualizing Mobile Objects in Maritime Contexts
This paper introduces a modular and experimental web GIS framework applied to maritime navigation, and where mobile objects behave in a maritime environment, including a web-based wireless access and interface to a traffic monitoring application.
Specification of Semantic Trajectories Supporting Data Transformations for Analytics: The datAcron Ontology
The paper presents the ontology in detail, in connection to other well-known ontologies, and demonstrates how data is represented at varying levels of analysis, enabling the required data transformations.
Event Forecasting with Pattern Markov Chains
A system for online probabilistic event forecasting that can consume streams of events and forecast when the pattern is expected to be fully matched, in the form of intervals within which a full match is expected.
Radon - Rapid Discovery of Topological Relations
This paper presents Radon – efficient solution for the discovery of topological relations between geospatial resources according to the DE9-IM standard and shows that it outperform the state of the art significantly and by several orders of magnitude.
Trajectory Simplification: On Minimizing the Direction-based Error
A problem called Min-Error is defined and two exact algorithms and one 2-factor approximate algorithm for the problem are developed and extensive experiments on real datasets verified the algorithms.
One-Pass Error Bounded Trajectory Simplification
This study develops a one-pass error bounded trajectory simplification algorithm (OPERB), which scans each data point in a trajectory once and only once, and proposes an aggressive one- pass error bounded trajectories simplifying algorithm (operB-A), which allows interpolating new data points into a trajectory under certain conditions.
Discovering Spatial and Temporal Links among RDF Data
This paper proposes new methods for Spatial and Temporal Link Discovery and provides the first implementation of these techniques based on the well-known framework Silk, which allows data publishers to generate a wide variety of spatial, temporal and spatiotemporal relations between their data and other Linked Open Data.
ORCHID - Reduction-Ratio-Optimal Computation of Geo-spatial Distances for Link Discovery
This paper presents Orchid, a reduction-ratio-optimal link discovery approach designed especially for geo-spatial data, and indicates that Orchid scales to large datasets while outperforming the state of the art significantly.
Bounded Quadrant System: Error-bounded trajectory compression on the go
This work proposes a novel online algorithm for error-bounded trajectory compression called the Bounded Quadrant System (BQS), which compresses trajectories with extremely small costs in space and time using convex-hulls, and demonstrates the effectiveness of this algorithm in significantly reducing the time and space complexity of trajectory compression.