Energy-efficient AI-ready Data Spaces

Informazioni relative al progetto

Green.Dat.AI

ID dell’accordo di sovvenzione: 101070416

DOI

10.3030/101070416

Progetto chiuso

Data della firma CE 14 Novembre 2022

Data di avvio 1 Gennaio 2023

Data di completamento 31 Dicembre 2025

Finanziato da

• Digital, Industry and Space

Costo totale

€ 6 658 970,09

Contributo UE

€ 5 507 269,00

Coordinato da

INLECOM INNOVATION ASTIKI MI KERDOSKOPIKI ETAIREIA
Greece

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Documents, reports (3)

Stakeholder Engagement and Dissemination Plan

Document registering the key activities per target group, pertaining to maximizing the dissemination of GREEN.DAT.AI outcomes during and after the project’s lifecycle.

Pilots' Scoping Document

Report outlining the demonstrators’ implementation plans, milestones. The report will identify key stakeholders for the testing/validation phase and include the User Acceptance test templates.

Use Case Requirements, KPIs & Reference Architecture

Report consolidating results of work carries out in WP1 tasks T1.1 - T1.4. More specifically, it sets out the business & technical requirements, specifies the overall solution architecture based on the outputs of the activities in T1.2 and T1.3, and lays out the testing framework for all the pilots.

Other (2)

First Version of Energy-Efficient Large-Scale Data Analytics Services

D3.1 presents the first iteration of the work employed towards the design and development of nine different services that aspire to provide energy-efficient large-scale data analytics. The software library and report 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.

Data Management infrastructure and tools to support Dynamic Ecosystems

D2.1 showcases the design and development of the BDA infrastructure including the Federated Data Sovereignty services and the plan for data pipelines management and continuous integration. It also delivers the visualisation tools needed for the pilots, the workflow management engine required to schedule distributed analytics pipelines, and, finally, wrappers that secure computations & sensitive data at the edge/fog/cloud. A report will detail or the platform components, their interfaces, and data pipelines management mechanisms. The deliverable will include the first prototype of the visualisation workbench, interfaces, distributed analytics services, workflow management engine, and the wrappers for Securing Computations and Sensitive Data at the Edge/Fog/Cloud.

Pubblicazioni

Other (4)

GREEN.DAT.AI: an energy-efficient, AI-ready data space

Autori: Ben Capper
Pubblicato in: Red Hat Research Quarterly (Q3 25), 2025
Editore: Red Hat Research

AI-ready Data Products

Autori: Pezuela Robles, C. M., De Majo, C., Alonso, D., Curry, E., Simperl, E., Laatikainen, G., Fidan, G., Chrysakis, I., Giner Miguelez, J., Aas, K., Majithia, N., Plebani, P., & Carey-Wilson, T. (2025, December). AI-ready data products. Big Data Value Associat
Pubblicato in: BDVA Publications, 2025
Editore: BDVA

Connecting Data Spaces, a practical approach using the Sovity Connector

Autori: Panos Protopapas, Despina Brasinika, Ioanna Fergadiotou, Yaroslav Yavornytskyi, Martin Wagner, Arturo Medela
Pubblicato in: 2025
Editore: Zenodo
DOI: 10.5281/ZENODO.18030959

GREEN.DAT.AI: Enabling energy-efficient AI services

Autori: Ioanna Fergadioou
Editore: Innovation News Network

Conference proceedings (12)

Fingerprinting the Shadows: Unmasking Malicious Servers with Machine Learning-Powered TLS Analysis

Autori: Andreas Theofanous, Eva Papadogiannaki, Alexander Shevtsov, Sotiris Ioannidis
Pubblicato in: Proceedings of the ACM Web Conference 2024, 2024
Editore: ACM
DOI: 10.1145/3589334.3645719

Dataset2Graph: A GNN-based Methodology for AutoML for Clustering

Autori: E. Dilmperis, Y. Poulakis, D. Petratos, C. Doulkeridis
Pubblicato in: 9th Workshop of Data Management for End-to-End Machine Learning (DEEM’25)
Editore: DEEM

Geolet: An Interpretable Model for Trajectory Classification

Autori: Cristiano Landi, Francesco Spinnato, Riccardo Guidotti, Anna Monreale, Mirco Nanni
Pubblicato in: Lecture Notes in Computer Science, Advances in Intelligent Data Analysis XXI, 2024
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-30047-9_19

An AutoML Approach for Bike Demand Forecasting and Redistribution

Autori: Dimitris Petratos, Yannis Poulakis, Irene Gimenez Pedralba, Cristina Aragon Garcia, Christos Doulkeridis
Pubblicato in: Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Intelligent Transport Systems, 2025
Editore: Springer Nature Switzerland
DOI: 10.1007/978-3-031-86370-7_9

Pythia: Distributed Pattern-based Future Location Prediction of Moving Objects

Autori: Panagiotis Tampakis, Nikos Pelekis
Pubblicato in: ISSN 1613-0073
Editore: CEUR Workshop Proceedings (CEUR-WS.org)

Interpretable Data Partitioning Through Tree-Based Clustering Methods

Autori: Riccardo Guidotti, Cristiano Landi, Andrea Beretta, Daniele Fadda, Mirco Nanni
Pubblicato in: ISSN 2193-1801
Editore: Springer Science and Business Media Deutschland GmbH
DOI: 10.1007/978-3-031-45275-8_33

A Protocol for Continual Explanation of SHAP

Autori: Andrea Cossu, Francesco Spinnato, Riccardo Guidotti, Davide Bacciu
Pubblicato in: ESANN 2023 proceesdings, 2023
Editore: Ciaco - i6doc.com
DOI: 10.14428/ESANN/2023.ES2023-41

PyClust: Building Meta-learning Repositories for Clustering

Autori: Y. Poulakis, D. Petratos, C. Doulkeridis
Pubblicato in: 2025
Editore: 25th IEEE International Conference on Data Mining (ICDM'25)

FAIRness in Dataspaces: The Role of Semantics for Data Management

Autori: Marco Hauff, Lina Molinas Comet, Paul Moosmann, Christoph Lange, Ioannis Chrysakis, Johannes Theissen-Lipp
Pubblicato in: ISSN 1613-0073
Editore: CEUR Workshop Proceedings (CEUR-WS.org)

A Shape-Based Map Matching Approach for Geographic Transferability of Discriminative Subtrajectories

Autori: Cristiano Landi, Riccardo Guidotti
Pubblicato in: ISSN 1613-0073
Editore: CEUR Workshop Proceedings (CEUR-WS.org)

Electric Vehicle Charging Load Forecasting: An Experimental Comparison of Machine Learning Methods

Autori: Iason Kyriakopoulos, Yannis Theodoridis
Pubblicato in: 2025, ISSN 1112-3455
Editore: arXiv
DOI: 10.48550/ARXIV.2512.17257

Path-based traffic flow prediction

Autori: Efstratios Karkanis, Nikos Pelekis, Eva Chondrodima, Yannis Theodoridis
Pubblicato in: ISSN 1613-0073
Editore: CEUR Workshop Proceedings (CEUR-WS.org)

Peer reviewed articles (4)

High-resolution spatiotemporal assessment of solar potential from remote sensing data using deep learning

Autori: Mitja Žalik, Domen Mongus, Niko Lukač
Pubblicato in: Renewable Energy, ISSN 1879-0682
Editore: Elsevier
DOI: 10.1016/J.RENENE.2023.119868

A Survey on AutoML Methods and Systems for Clustering

Autori: Yannis Poulakis, Christos Doulkeridis, Dimosthenis Kyriazis
Pubblicato in: ACM Transactions on Knowledge Discovery from Data, ISSN 1556-4681
Editore: Association for Computing Machinery
DOI: 10.1145/3643564

From Fossil Fuel to Electricity: Studying the Impact of EVs on the Daily Mobility Life of Users

Autori: Mirco Nanni, Omid Isfahani Alamdari, Agnese Bonavita, Paolo Cintia
Pubblicato in: IEEE Transactions on Intelligent Transportation Systems, ISSN 1558-0016
Editore: IEEE
DOI: 10.1109/TITS.2023.3

Multi-Partner Project: Green.Dat.AI: A Data Spaces Architecture for Enhancing Green AI Services

Autori: Ioannis Chrysakis, Evangelos Agorogiannis, Nikoleta Tsampanaki, Michalis Vourtzoumis, Eva Chondrodima, Yannis Theodoridis, Domen Mongus, Ben Capper, Martin Wagner, Aris Sotiropoulos, Fábio André Coelho, Cláudia Vanessa Brito, Panos Protopapas, Despina Brasinika, Ioanna Fergadiotou, Christos Doulkeridis
Pubblicato in: 2025 Design, Automation & Test in Europe Conference (DATE), 2025, ISSN 1530-1591
Editore: IEEE
DOI: 10.23919/DATE64628.2025.10992729