We are glad to announce that the 36-month EU-funded project kicked off in January 2023. GREEN.DAT.AI aims to channel the potential of AI towards the goals of the European Green Deal, by developing novel Energy-Efficient Large-Scale Data Analytics Services, ready-to-use in industrial AI-based systems while reducing the environmental impact of data management processes.
Project Overview
GREEN.DAT.AI aims to develop novel Energy-Efficient Large-Scale Data Analytics Services, ready to use in industrial AI-based systems. The project will demonstrate the efficiencies of the new analytics services in four industries: Smart Energy, Smart Agriculture/Agri-food, Smart Mobility, Smart Banking and six different application scenarios, leveraging the use of European Data Spaces.
The ambition is to exploit mature solutions already developed in recent H2020 projects and deliver an efficient, massively distributed, open-source, green, AI/FL ready platform, and a validated go-to-market Toolbox for AI-ready Data Spaces.
The GREEN.DAT.AI Toolbox will be by design compliant with the FAIR data and metadata management principles. In the long-term, the GREEN.DAT.AI platform will allow computing to move from data centers to edge devices, making AI accessible to more people, shift computation from the cloud to personal devices to reduce the flow and potential leakage of sensitive data and enable processing data on the edge to eliminate transmission costs, leading to faster inference with a shorter reaction time and drive innovation in applications where these parameters are critical.
The GREEN.DAT.AI Consortium consists of a multidisciplinary group of 17 partners from 10 different countries (and one associated party), well balanced in terms of expertise. GREEN.DATA.AI applies a multidisciplinary approach to draw on knowledge from different domain experts in energy, transport business and economics as well as Data Science and SW Engineering.
For more information, check: www.greendatai.eu
This project has received funding from the Horizon Europe research and innovation programme under the GA 101070416. Funded by the European Union. Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union or European Commission. Neither the European Union nor the granting authority can be held responsible for them
