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.
GREEN.DAT.AI 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 (TRL5 or higher) 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 TRL7/8 Toolbox for AI-ready Data Spaces. The services will cover AI-enabled data enrichment, Incentive mechanisms for Data Sharing, Synthetic Data Generation, Large-scale learning at the Edge/Fog, Federated & Auto ML at the edge/fog, Explainable AI/Feature Learning with Privacy Preservation, Federated & Automatic Transfer Learning, Adaptive FL for Digital Twin Applications, Automated IoT event-based change detection/forecasting.
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. The vast majority of partners already have key roles in a number of projects funded under the Big Data PPP (ICT-16-2017) topic, namely BigDataStack, CLASS, Track & Know, and I-BiDaaS and are serving as active members of the BDVA/DAIRO Association, FIWARE, AIOTI, and ETSI. In addition, partners come from a variety of sectors, such as banking, mobility, energy, and agriculture, constituting a representative workforce of their respective domains, which will contribute to industry adoption and stimulate uptake in other sectors as well.
| Web resources: |
https://cordis.europa.eu/project/id/101070416
https://greendatai.eu/ |
| Start date: | 01-01-2023 |
| End date: | 31-12-2025 |
| Total budget - Public funding: | 6 702 187,00 Euro - 5 507 269,00 Euro |
Original description
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.GREEN.DAT.AI 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 (TRL5 or higher) 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 TRL7/8 Toolbox for AI-ready Data Spaces. The services will cover AI-enabled data enrichment, Incentive mechanisms for Data Sharing, Synthetic Data Generation, Large-scale learning at the Edge/Fog, Federated & Auto ML at the edge/fog, Explainable AI/Feature Learning with Privacy Preservation, Federated & Automatic Transfer Learning, Adaptive FL for Digital Twin Applications, Automated IoT event-based change detection/forecasting.
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. The vast majority of partners already have key roles in a number of projects funded under the Big Data PPP (ICT-16-2017) topic, namely BigDataStack, CLASS, Track & Know, and I-BiDaaS and are serving as active members of the BDVA/DAIRO Association, FIWARE, AIOTI, and ETSI. In addition, partners come from a variety of sectors, such as banking, mobility, energy, and agriculture, constituting a representative workforce of their respective domains, which will contribute to industry adoption and stimulate uptake in other sectors as well.
Status
SIGNEDCall topic
HORIZON-CL4-2021-DATA-01-03Update Date
09-02-2023
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| INLECOM INNOVATION ASTIKI MI KERDOSKOPIKI ETAIREIA (INLECOM INNOVATION) (Coördinator)
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| AEGIS IT RESEARCH GMBH
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| ATOS IT SOLUTIONS AND SERVICES IBERIA SL
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| CAIXABANK SA
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| CNET CENTRE FOR NEW ENERGY TECHNOLOGIES SA
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| CONSIGLIO NAZIONALE DELLE RICERCHE (CNR)
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| EREVNITIKO PANEPISTIMIAKO INSTITOUTO TILEPIKONONIAKON SYSTIMATON
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| FERROVIAL SERVICIOS SA
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| GRUPO SERVEO SL
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| INESC TEC
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| INTRASOFT INTERNATIONAL SA
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| ITC - INOVACIJSKO TEHNOLOSKI GROZD MURSKA SOBOTA
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| KONNECTA SYSTEMS IKE
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| KONNECTA SYSTEMS LIMITED
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| POLYTECHNEIO KRITIS
The standardisation work combines the topics of AI and energy efficiency, being focused on the development of the recommendations of standardisation for these fields.