Composite Maritime Event Recognition

Pitsikalis, Manolis; Artikis, Alexander

doi:10.1007/978-3-030-61852-0_8

Cited by 5 publications

(3 citation statements)

References 29 publications

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“…The modelling of maritime related concepts relies on the data model defined for the Common Information Sharing Environment [14], proposed on the basis of the EUCISE data model, which has been recently converted in an OWL-2 ontology EUCISE-OWL [5] to facilitate its adoption in a border security European project 7 . In particular, the modelling of maritime events proposed in [5] is leveraged and extended in MSA-HSN.…”

Section: Related Workmentioning

confidence: 99%

“…Example III-C. 1 gives the specification for two related maritime events involving the same vessel, a lotering and a tugging speed event, respectively, which are extracted from the dataset in [7], and of the associated entities. In the example, the loitering event <RTEC-MaritimeEvent/109701> is associated to the speed event that precedes it <RTEC-MaritimeEvent/109625>, using the pattern described above.…”

Section: Maritime Eventsmentioning

confidence: 99%

“…Section III describes and motivates the conceptual design of the MSA-HSN ontology and formalises the information components of a typical information fusion systems. The modelling of the most relevant elements of the ontology design, i.e., maritime sensors and observations, observation quantity and values, information quality and maritime events, will be illustrated using data from the INFORE maritime use case, in particular the maritime robotic network presented in [6], and open maritime data from real heterogeneous sensors and events from the same project [7], [8]. Finally, Section IV concludes the papers and outlines the future developments of MSA-HSN.…”

Section: Introductionmentioning

confidence: 99%

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Maritime Linked Data for Situational Awareness Heterogeneous Sensor Networks

Camossi

Grasso

Ferri

et al. 2021

OCEANS 2021: San Diego – Porto

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Maritime heterogeneous sensor networks (HSN), which are widely used in support of marine monitoring, maritime security and safety applications, combine diversified sensing systems and platforms observing the state of environmental features of interest or the status of anthropogenic objects to achieve awareness on the situation. To model the information characteristics of HSN observations for maritime situational awareness (MSA), we developed the MSA-HSN ontology, an integrated semantic model for maritime linked data, specifically observations suiting the requirements of maritime information fusion systems. MSA-HSN integrates established ontologies and models for sensors, observations, measurements, quantities, and occurrents, tailored to MSA applications and requirements. To support the interoperability with existing maritime data models, MSA-HSN is aligned with the relevant aspects of the EUCISE/e-CISE (Common Information Sharing Environment) data models. As a validation use case, in this paper the MSA-HSN ontology is applied to model the information elements of the maritime surveillance system developed within the Interactive Extreme-Scale Analytics and Forecasting (INFORE) project, where different situational views offered by a variegate suite of sensors and platforms are fused using big data analytics to achieve maritime situational awareness for maritime security. The paper describes the design of the MSA-HSN ontology, illustrating its application through examples taken from the INFORE use case and from relevant datasets developed by recent European projects involving MSA use cases.

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Section: Related Workmentioning

confidence: 99%

Section: Maritime Eventsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Maritime Linked Data for Situational Awareness Heterogeneous Sensor Networks

Camossi

Grasso

Ferri

et al. 2021

OCEANS 2021: San Diego – Porto

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A hierarchical graph-based model for mobility data representation and analysis

Elayam

Ray²,

Claramunt³

2022

Data & Knowledge Engineering

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Human-in-the-Loop: Visual Analytics for Building Models Recognizing Behavioral Patterns in Time Series

Andrienko,

Artikis

et al. 2024

IEEE Comput. Grap. Appl.

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Detecting complex behavioural patterns in temporal data, like moving object trajectories, often relies on precise formal specifications derived from vague domain concepts. However, such methods are sensitive to noise and minor fluctuations, leading to missed pattern occurrences. Conversely, machine learning (ML) approaches require abundant labeled examples, posing practical challenges. Our visual analytics approach enables domain experts to derive, test, and combine interval-based features to discriminate patterns and generate training data for ML algorithms. Visual aids enhance recognition and characterisation of expected patterns and discovery of unexpected ones. Case studies demonstrate feasibility and effectiveness of the approach, which offers a novel framework for integrating human expertise and analytical reasoning with ML techniques, advancing data analytics.

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Composite Maritime Event Recognition

Cited by 5 publications

References 29 publications

Maritime Linked Data for Situational Awareness Heterogeneous Sensor Networks

Maritime Linked Data for Situational Awareness Heterogeneous Sensor Networks

A hierarchical graph-based model for mobility data representation and analysis

Human-in-the-Loop: Visual Analytics for Building Models Recognizing Behavioral Patterns in Time Series

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