Period: 01.01.2023 – 31.12.2025

Funding SOURCE: Horizon EUROPE

TOTAL BUDGET:  6.658.970 €

PROJECT COORDINATOR: INLECOM INNOVATION ASTIKI MI KERDOSKOPIKI ETAIREIA

GEMMA COORDINATOR: prof. dr. Domen Mongus

PROJECT PARTNERS: University of Piraeus Research Center, Consiglio Nazionale delle Ricerche, Konnecta Systems Limited, Atos IT Solutions and Services Iberia SL, Polytechneio Kritis, ITC – Inovacijsko Tehnoloski Grozd Murska Sobota, Caixabank SA, Univerza V Mariboru, Serveo Servicios SA, Grupo Serveo SL, Aegis IT Research GmbH, Red Hat Israel Ltd, INESC TEC – Instituto de Engenharia de Sistemas e Computadores, Tecnologia e Ciencia, Waboost Razvoj Tehnologij D.O.O., CNET Centre for New Energy Technologies SA, Netcompany-Intrasoft SA, Sunesis, Inovativne Tehnologije in Storitve, D.O.O., Konnecta Systems IKE, Red Hat Limited, Sphynx Technology Solutions AG

Project website: Green.DAT.AI

LINKEDIN: Green.DAT.AI

Energy-efficient AI-Ready Data Spaces

Abstract:

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/Agrifood, 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.