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Country: Greece


6 Projects, page 1 of 2
  • Funder: EC Project Code: 825070
    Overall Budget: 4,435,590 EURFunder Contribution: 4,435,590 EUR

    At an increasing rate, industrial and scientific institutions need to deal with massive data flows streaming in from a multitude of sources. For instance, maritime surveillance applications combine high-velocity data streams, including vessel position signals emitted from hundreds of thousands of vessels across the world and acoustic signals of autonomous, unmanned vessels; in the financial domain, stock price forecasting and portfolio management rely on stock tick data combined with real-time information sources on various pricing indicators; at the fight against cancer, complex simulations of multi-cellular systems are used, producing extreme-scale data streams in an effort to predict the effects of drug synergies on cancer cells. In these applications, the data volumes are expected to dramatically grow in the future. Processing this data often requires not only using an HPC infrastructure, but also having data scientists, who are typically not expert programmers, program complex workflows, with a vast number of parameters to tune through time-consuming repeated programming and testing. INFORE will address these challenges and pave the way for real-time, interactive extreme-scale analytics and forecasting. The ability to forecast, as early as possible, a good approximation to the outcome of a time-consuming and resource-demanding computational task allows to quickly identify undesired outcomes and save valuable amount of time, effort and computational resources, which would otherwise be spent in vain. Consider, for example, the ability to forecast the outcome of a complex multi-cellular system simulation for tumor evolution, without the need to wait for the simulation to be completed. INFORE will also design and develop a flexible, pluggable, distributed software architecture that is programmable and set up by graphical data processing workflows. The INFORE prototype will be tested on massive real-world data from the life sciences, financial and maritime domains.

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  • Funder: EC Project Code: 957237
    Overall Budget: 5,998,880 EURFunder Contribution: 5,998,880 EUR

    Shipping is the lifeblood of global economy, consequently one of the leading sources of greenhouse gases and one of the high-incident domains, due to heavy traffic especially in congested waters, therefore facing escalating pressure for safety, energy efficiency improvement and emissions reduction. Meanwhile, shipping generates extremely large amount of data in every minute, which potential, however, still remains untapped due to the involvement of enormous stakeholders and the sophistication of modern vessel design and operation. To address these challenges, VesselAI aims to develop, validate and demonstrate a unique framework to unlock the potential of extreme-scale data and advanced HPC, AI and Digital Twin technologies, and hence to promote the adoption and application of Big Data-driven innovations and solutions in maritime industry and beyond. By combining Digital Twin technologies and practices, VesselAI can efficiently fuse and assimilate huge amount of data, coming from both observations and simulations, to achieve highly accurate modelling, estimation and optimization of design and operation of ships and fleets under various dynamic conditions in near real time. Their technical enhancements and practical performance improvements are further demonstrated in 4 maritime industry pilots, tackling practical challenges for 1) global vessel traffic monitoring and management, 2) globally optimal ship energy system design, 3) short-sea autonomous shipping and 4) global fleet intelligence. VesselAI brings in a consortium of renowned actors in maritime and ICT domains, providing a perfect mix of high-level expertise in both domains and readily accessibility to huge amount of data for industry-leading research and innovation in the project. Together, VesselAI addresses the challenges of implementing extreme-scale analytics in industries and showcase how AI, cloud computing and HPC can encourage, and enable deeper digitalization in the maritime and wider industries.

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  • Funder: EC Project Code: 101092749
    Overall Budget: 8,698,100 EURFunder Contribution: 8,698,100 EUR

    The vision of CREXDATA is to develop a generic platform for real-time critical situation management including flexible action planning and agile decision making over streaming data of extreme scale and complexity. CREXDATA develops the algorithmic apparatus, software architectures and tools for federated predictive analytics and forecasting under uncertainty. The envisioned framework boosts proactive decision making providing highly accurate and transparent short- and long-term forecasts, explainable via advanced visual analytics and accurate, real-time, augmented reality facilities. To achieve its vision, CREXDATA will develop a next generation Prediction-as-a-Service (PaaS) system where action planners will easily register their multimodal data stream sources and compute resource federations and graphically design predictive analytics workflows including (i) data ingestion, fusion, (ii) simulation, (iii) federated learning for pattern extraction and (iv) multiresolution forecasting operators. Decision makers will receive back extremely precise forecasted representations of future worlds reasoned about using transparent AI facilities and with reduced complexity via visual analytics and intuitive augmented reality provided on-site or remotely. The CREXDATA architecture incorporates 10 exploitable assets based on cutting edge research, which will significantly outperform the current state of practice in respective fields. CREXDATA will be evaluated in three use cases where real-time critical action planning and timely decision making are of utmost importance: i) maritime domain, for forecasting hazardous situations at sea and impose safer navigational routes, ii) weather emergency management, to allow authorities and first responders proactively act so as to avoid or reduce the impact and speed up recovery from natural disasters, and iii) health crisis management, to limit pandemic outbreaks and come up with non-pharmaceutical means of patient treatment.

  • Funder: EC Project Code: 101021673
    Overall Budget: 6,065,820 EURFunder Contribution: 4,997,560 EUR

    Maritime Domain Awareness is the combination of activities, events and threats in the maritime environment that could have impact on marine activities and EU territory. During the past decades, advances in Information and Communication Technologies have provided better means to monitor and analyse vessel activity. Today private and public source of data such the Automatic Identification System or space related data can be combined with Vessel Traffic Services, Vessel Traffic Management Systems and Vessel Traffic Monitoring & Information Systems data enabling the development of value added information resulted by the combination of such data. European waters are navigated daily by some 12,000 vessels, which share their positions to avoid collisions, generating a huge number of positional messages every month. It is important that this overabundance of information will not overwhelm the marine operator in charge for decision-making. The challenge is twofold: on one side encourage the exchange of heterogeneous data among administration valorising the CISE network currently in place, on the other exploit at the best these datasets by means of automated processing in a way to minimise false alert that might results by an incorrect processing or interpretation of the results. PROMENADE will improve solutions for the vessel tracking, behaviour analysis and automatic anomaly detection by means of the application Artificial Intelligence (AI) and Big Data (BD) technologies, and to promote collaborative exchange of information between maritime surveillance authorities, shortening the time to market and assuring the compliance with legal and ethical regulations. An open, service-based toolkit implementing “state of art” AI / BD techniques also benefiting of HPC (High Performance Computing) platform is the core activity of the project. The project’s developments will be demonstrated and evaluated in 3 operational scenarios and 1 simulated defined by Border Guards Authorities.

  • Funder: EC Project Code: 101073952
    Overall Budget: 5,379,570 EURFunder Contribution: 4,670,650 EUR

    PERIVALLON aims to provide an improved and comprehensive intelligence picture of organised environmental crime and develop effective and efficient tools and solutions for detecting and preventing such types of criminal activities and for assessing their environmental impact based on geospatial intelligence, remote sensing, scanning, online monitoring, analysis, correlation, risk assessment, and predictive analytics technologies, by leveraging the latest advancements in Artificial Intelligence (AI) in the fields of computer vision and multimodal analytics. As a result, enhanced investigation processes and methodologies will be derived through the capabilities provided by the developed tools and solutions, and the insights obtained though the proposed Environmental Crime Observatory. The capacity of end users (including Police Authorities and Border Guards) will also be improved and will enable them to tackle such criminal activities in an effective manner based on advanced tools and solutions and also on the innovative training curricula developed using physical and/or digital twins of relevant environmental crime scenarios. Moreover, improved international cooperation will be facilitated through improved data sharing enabled by blockchain technologies, while improved regulation shaping and tuning will be supported through relevant policy recommendations. PERIVALLON will be validated in field tests and demonstrations in four operational use cases. Extensive training, hands-on experience, joint exercises, and training material will boost the uptake of PERIVALLON tools and technologies. With a Consortium 5 Police and Border Guard Authorities, 3 authorities related to environmental protection, 6 Research/Academic institutions, 8 industry partners (including seven SMEs), one EU Agency, and one Foundation, PERIVALLON delivers a strong representation of the challenges, requirements and tools to meet its objectives.


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