Project description
Energy-efficient data analytics services for industrial AI-based systems
Artificial intelligence (AI) has the potential to contribute to the goals of the European Green Deal. The EU-funded Green.Dat.AI project will develop innovative energy-efficient large-scale data analytics services, ready to use in industrial AI-based systems, which will reduce the environmental impact of data management processes. The project will demonstrate the efficiencies of the new analytics services in the smart energy, smart agriculture/agri-food, smart mobility and smart banking industries as well as six different application scenarios, exploiting the use of European data spaces. Green.Dat.AI envisages exploiting mature (technology readiness level (TRL) 5 or higher) solutions already developed in recent Horizon 2020 projects and delivering 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.
Objective
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.
Fields of science
- engineering and technologyenvironmental engineeringenergy and fuelsrenewable energy
- natural sciencescomputer and information sciencesartificial intelligencemachine learningtransfer learning
- natural sciencescomputer and information sciencesdata sciencebig data
- agricultural sciencesagriculture, forestry, and fisheriesagriculture
- social scienceseconomics and businesseconomicssustainable economy
Programme(s)
Funding Scheme
HORIZON-IA - HORIZON Innovation ActionsCoordinator
145 61 Athina
Greece
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Participants (19)
185 33 PIRAEUS
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00185 Roma
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12 Dublin
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28760 Tres Cantos Madrid
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731 00 Chania
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9000 Murska Sobota
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46002 Valencia
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2000 Maribor
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28050 Madrid
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Legal entity other than a subcontractor which is affiliated or legally linked to a participant. The entity carries out work under the conditions laid down in the Grant Agreement, supplies goods or provides services for the action, but did not sign the Grant Agreement. A third party abides by the rules applicable to its related participant under the Grant Agreement with regard to eligibility of costs and control of expenditure.
28010 MADRID
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38106 Braunschweig
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4350108 RA ANANA
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4200 465 Porto
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1000 Ljubljana
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2685 039 Sacavem E Prior Velho Lisboa
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1253 Luxembourg
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1000 Ljubljana
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
145 61 KIFISIA
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The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.
T12 Cork
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Partners (1)
Partner organisations contribute to the implementation of the action, but do not sign the Grant Agreement.
6300 ZUG
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