TY - GEN
T1 - The VesselAI Methodology for AI-Powered Decision Support Systems for the Maritime Industry
AU - Kontzinos, Christos
AU - Mouzakitis, Spiros
AU - Agostinho, Carlos
AU - Figueiras, Paulo
AU - Askounis, Dimitris
N1 - info:eu-repo/grantAgreement/EC/H2020/822404/EU#
Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2024
Y1 - 2024
N2 - The maritime industry is a major contributor in the European economy, employment, and shipping. However, the industry is facing various challenges that concern the need to increase performance and efficiency, optimise costs, obey the strict regulations imposed and increase its overall sustainability when it comes to fuel consumption and other environmental concerns. Such challenges can be resolved with the help of innovative, emerging technologies such as big data, AI, and HPC, the combination of which can lead the way for the development of the next generation of maritime applications. The use of these technologies has the potential to greatly enhance the performance and competitiveness of the maritime industry, but any such initiative must take into account current maritime challenges and needs, as well as the heterogeneity of the maritime environment, processes, and data in order to develop solutions that tackle real problems and correspond to the actual needs of maritime stakeholders. This can only be achieved through cooperation with maritime and other stakeholders and requires comprehensive methodologies that can guide the development of a solution from the early phases of knowledge generation and requirement elicitation to the latter stages of technical development and testing. As such, the current publication presents the VesselAI methodology for AI-powered decision support systems for the maritime industry. VesselAI is an EU-funded project that aims to combine innovative technologies, mainly big data, AI, and HPC to develop the next generation of maritime applications.
AB - The maritime industry is a major contributor in the European economy, employment, and shipping. However, the industry is facing various challenges that concern the need to increase performance and efficiency, optimise costs, obey the strict regulations imposed and increase its overall sustainability when it comes to fuel consumption and other environmental concerns. Such challenges can be resolved with the help of innovative, emerging technologies such as big data, AI, and HPC, the combination of which can lead the way for the development of the next generation of maritime applications. The use of these technologies has the potential to greatly enhance the performance and competitiveness of the maritime industry, but any such initiative must take into account current maritime challenges and needs, as well as the heterogeneity of the maritime environment, processes, and data in order to develop solutions that tackle real problems and correspond to the actual needs of maritime stakeholders. This can only be achieved through cooperation with maritime and other stakeholders and requires comprehensive methodologies that can guide the development of a solution from the early phases of knowledge generation and requirement elicitation to the latter stages of technical development and testing. As such, the current publication presents the VesselAI methodology for AI-powered decision support systems for the maritime industry. VesselAI is an EU-funded project that aims to combine innovative technologies, mainly big data, AI, and HPC to develop the next generation of maritime applications.
KW - Artificial intelligence
KW - Big data
KW - Decision support
KW - Maritime
KW - Methodology
UR - http://www.scopus.com/inward/record.url?scp=85182504921&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-47721-8_13
DO - 10.1007/978-3-031-47721-8_13
M3 - Conference contribution
AN - SCOPUS:85182504921
SN - 9783031477201
VL - 822
T3 - Lecture Notes in Networks and Systems
SP - 201
EP - 211
BT - Intelligent Systems and Applications - Proceedings of the 2023 Intelligent Systems Conference IntelliSys Volume 1
A2 - Arai, Kohei
PB - Springer Science and Business Media Deutschland GmbH
T2 - Intelligent Systems Conference, IntelliSys 2023
Y2 - 7 September 2023 through 8 September 2023
ER -