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Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture

The modern maritime industry is producing data at an unprecedented rate. The capturing and processing of such data is integral to create added value for maritime companies and other maritime stakeholders, but their true potential can only be unlocked by innovative technologies such as extreme-scale...

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Autores principales: Ilias, Loukas, Tsapelas, Giannis, Kapsalis, Panagiotis, Michalakopoulos, Vasilis, Kormpakis, Giorgos, Mouzakitis, Spiros, Askounis, Dimitris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413560/
https://www.ncbi.nlm.nih.gov/pubmed/37576115
http://dx.doi.org/10.3389/fdata.2023.1220348
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author Ilias, Loukas
Tsapelas, Giannis
Kapsalis, Panagiotis
Michalakopoulos, Vasilis
Kormpakis, Giorgos
Mouzakitis, Spiros
Askounis, Dimitris
author_facet Ilias, Loukas
Tsapelas, Giannis
Kapsalis, Panagiotis
Michalakopoulos, Vasilis
Kormpakis, Giorgos
Mouzakitis, Spiros
Askounis, Dimitris
author_sort Ilias, Loukas
collection PubMed
description The modern maritime industry is producing data at an unprecedented rate. The capturing and processing of such data is integral to create added value for maritime companies and other maritime stakeholders, but their true potential can only be unlocked by innovative technologies such as extreme-scale analytics, AI, and digital twins, given that existing systems and traditional approaches are unable to effectively collect, store, and process big data. Such innovative systems are not only projected to effectively deal with maritime big data but to also create various tools that can assist maritime companies, in an evolving and complex environment that requires maritime vessels to increase their overall safety and performance and reduce their consumption and emissions. An integral challenge for developing these next-generation maritime applications lies in effectively combining and incorporating the aforementioned innovative technologies in an integrated system. Under this context, the current paper presents the architecture of VesselAI, an EU-funded project that aims to develop, validate, and demonstrate a novel holistic framework based on a combination of the state-of-the-art HPC, Big Data and AI technologies, capable of performing extreme-scale and distributed analytics for fuelling the next-generation digital twins in maritime applications and beyond.
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spelling pubmed-104135602023-08-11 Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture Ilias, Loukas Tsapelas, Giannis Kapsalis, Panagiotis Michalakopoulos, Vasilis Kormpakis, Giorgos Mouzakitis, Spiros Askounis, Dimitris Front Big Data Big Data The modern maritime industry is producing data at an unprecedented rate. The capturing and processing of such data is integral to create added value for maritime companies and other maritime stakeholders, but their true potential can only be unlocked by innovative technologies such as extreme-scale analytics, AI, and digital twins, given that existing systems and traditional approaches are unable to effectively collect, store, and process big data. Such innovative systems are not only projected to effectively deal with maritime big data but to also create various tools that can assist maritime companies, in an evolving and complex environment that requires maritime vessels to increase their overall safety and performance and reduce their consumption and emissions. An integral challenge for developing these next-generation maritime applications lies in effectively combining and incorporating the aforementioned innovative technologies in an integrated system. Under this context, the current paper presents the architecture of VesselAI, an EU-funded project that aims to develop, validate, and demonstrate a novel holistic framework based on a combination of the state-of-the-art HPC, Big Data and AI technologies, capable of performing extreme-scale and distributed analytics for fuelling the next-generation digital twins in maritime applications and beyond. Frontiers Media S.A. 2023-07-27 /pmc/articles/PMC10413560/ /pubmed/37576115 http://dx.doi.org/10.3389/fdata.2023.1220348 Text en Copyright © 2023 Ilias, Tsapelas, Kapsalis, Michalakopoulos, Kormpakis, Mouzakitis and Askounis. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Ilias, Loukas
Tsapelas, Giannis
Kapsalis, Panagiotis
Michalakopoulos, Vasilis
Kormpakis, Giorgos
Mouzakitis, Spiros
Askounis, Dimitris
Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture
title Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture
title_full Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture
title_fullStr Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture
title_full_unstemmed Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture
title_short Leveraging extreme scale analytics, AI and digital twins for maritime digitalization: the VesselAI architecture
title_sort leveraging extreme scale analytics, ai and digital twins for maritime digitalization: the vesselai architecture
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10413560/
https://www.ncbi.nlm.nih.gov/pubmed/37576115
http://dx.doi.org/10.3389/fdata.2023.1220348
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