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

Area of Science:

• Maritime technology
• Data science
• Artificial intelligence

Background:

• The maritime industry generates vast amounts of data, overwhelming traditional systems.
• Existing approaches struggle to effectively capture, store, and process this big data.
• Innovative technologies are crucial for unlocking the full potential of maritime data.

Purpose of the Study:

• To present the architecture of VesselAI, an EU-funded project.
• To develop a holistic framework integrating HPC, Big Data, and AI.
• To enable extreme-scale and distributed analytics for maritime applications.

Main Methods:

• Utilizing state-of-the-art High-Performance Computing (HPC) infrastructure.
• Implementing advanced Big Data processing techniques.
• Leveraging Artificial Intelligence (AI) for predictive analytics and decision support.

Main Results:

• VesselAI framework designed for extreme-scale and distributed analytics.
• Integration of HPC, Big Data, and AI technologies.
• Foundation laid for next-generation maritime digital twins.

Conclusions:

• The VesselAI framework addresses the challenge of integrating innovative technologies for maritime big data.
• It aims to enhance maritime safety, performance, and reduce consumption and emissions.
• The project will validate and demonstrate a novel holistic framework for advanced maritime applications.