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
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.
Adoption Roadmap, Standardisation Recommendations and Capacity Building (si apre in una nuova finestra)D5.3 Adoption Roadmap, Standardisation Recommendations and Capacity Building (ITC, R, M36)
Pilots’ interim progress monitoring report (si apre in una nuova finestra)Report outlining the outcomes of each demonstrator, mapping the models and services deployed, datasets/data pipelines of each use case, alignment with the user requirements and architecture captured in D1.1 and detailed in D4.7. Will report on results of first testing cycle and will consolidate feedback for fixes on the tools developed in WP2 and WP3.
D4.9 Validation of GREEN.DAT.AI Platform and Toolbox in Real Life Operational Demonstrators (si apre in una nuova finestra)Report on end-user feedback, impact assessment and vulnerability assessment in all pilots. Consolidates strengths and limitations of the GREEN.DAT.AI platform and toolbox.
Pilots' Scoping Document (si apre in una nuova finestra)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 (si apre in una nuova finestra)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.
Innovation Management Report (si apre in una nuova finestra)This report presents the GREEN.DAT.AI Innovations Registry, patents elicitation and registration. The scientific and technical progress will be an integral part of the periodic reports.
Dissemination and Communication Activities (si apre in una nuova finestra)Report presenting the dissemination and communication activities. Presentation of key impact metrics and effectiveness of D&C measures.
D4.4 presents the results of the Agri-food demonstrator. It delivers AI-enabled services that monitor water quality from different sources in real time, perform prognosis and optimise overall water management.
Energy Grids: Smart electric vehicle charging (si apre in una nuova finestra)D4.2 delivers the outcomes of the Energy Grids demonstrator. It presents a novel platform that collects EV-related data and includes forecasting algorithms that manage EV charging needs.
Energy Marketplaces: Data sharing across the renewable energy sector (si apre in una nuova finestra)D4.1 consolidates the results of the Energy Marketplaces demonstrator. The report delivers a collaborative analytics framework on top of an IOTA-based data marketplace. It also presents the models developed and evaluated in several collaborative analytics use cases.
Agriculture: Smart Farming Optimisation through Digital Twins (si apre in una nuova finestra)D4.3 consolidates the results of the Agriculture demonstrator, which consists of a digital twin solution that employs different farming optimisation techniques.
Banking: Fraud detection and explainable AI (si apre in una nuova finestra)The report brings together the outcomes of the Banking demonstrator. It includes models for efficient and near-real time fraud detection and interpretable feature learning for the discovery of hidden patterns.
Smart Green Mobility: Energy Demand Response in EDVs & Infrastructure Maintenance (si apre in una nuova finestra)D4.5 puts together the results of the Smart Green Mobility demonstrator. It presents a solution that offers energy demand learning-based prediction in EDVs using state-of-the-art ML/FL models.
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.
Final version of ready to use Energy-Efficient Large-Scale Data Analytics Toolbox (si apre in una nuova finestra)D3.2 constitutes the updated and final version of the work performed in WP3 and consolidates all nine services developed into an energy-efficient large-scale data analytics services toolbox, which will be integrated in the platform and tested in the pilots.
Integrated BDA Services supporting the Pilot Applications (si apre in una nuova finestra)D2.2 delivers the integrated platform that unifies the different services developed in WP3 ready to support the pilot use cases. Final version of integrated services in the platform environment, deployed visualisation workbench, analytics services and wrappers. The deliverable will include the testing tool for measuring the energy efficiency of AI Services developed in Task 2.5 developed by INESC TEC.
Data Management infrastructure and tools to support Dynamic Ecosystems (si apre in una nuova finestra)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.
Report presenting the open data that will be made available and accessible to all relevant communities at the end of the project.
Pubblicazioni
Autori:
Ben Capper
Pubblicato in:
Red Hat Research Quarterly (Q3 25), 2025
Editore:
Red Hat Research
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
Autori:
Panos Protopapas, Despina Brasinika, Ioanna Fergadiotou, Yaroslav Yavornytskyi, Martin Wagner, Arturo Medela
Pubblicato in:
2025
Editore:
Zenodo
DOI:
10.5281/ZENODO.18030959
Autori:
Ioanna Fergadioou
Editore:
Innovation News Network
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
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
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
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
Autori:
Panagiotis Tampakis, Nikos Pelekis
Pubblicato in:
ISSN 1613-0073
Editore:
CEUR Workshop Proceedings (CEUR-WS.org)
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
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
Autori:
Y. Poulakis, D. Petratos, C. Doulkeridis
Pubblicato in:
2025
Editore:
25th IEEE International Conference on Data Mining (ICDM'25)
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)
Autori:
Cristiano Landi, Riccardo Guidotti
Pubblicato in:
ISSN 1613-0073
Editore:
CEUR Workshop Proceedings (CEUR-WS.org)
Autori:
Iason Kyriakopoulos, Yannis Theodoridis
Pubblicato in:
2025, ISSN 1112-3455
Editore:
arXiv
DOI:
10.48550/ARXIV.2512.17257
Autori:
Efstratios Karkanis, Nikos Pelekis, Eva Chondrodima, Yannis Theodoridis
Pubblicato in:
ISSN 1613-0073
Editore:
CEUR Workshop Proceedings (CEUR-WS.org)
Autori:
Mitja Žalik, Domen Mongus, Niko Lukač
Pubblicato in:
Renewable Energy, ISSN 1879-0682
Editore:
Elsevier
DOI:
10.1016/J.RENENE.2023.119868
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
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
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
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