Revolutionising Data Utilisation Across Sectors

The PLIADES project

A team of researchers led by the Centre for Research and Technology Hellas/ Information Technologies Institute (Greece), embarked on a ground-breaking journey with the launch of the PLIADES project. Funded by the European Commission under the Horizon Europe research and innovation programme with a budget of €9 million, PLIADES (AI-Enabled Data Lifecycles Optimisation and Data Spaces Integration for Increased Efficiency and Interoperability) aims to redefine how data is utilised across sectors, from mobility to healthcare, manufacturing to energy, and beyond.

The primary mission of PLIADES is to introduce an advanced AI-enabled framework for Full Data Lifecycles Optimisation and Data Spaces Integration. This visionary project envisions a future where diverse sectors are seamlessly interconnected, fostering efficiency and interoperability. By providing cutting-edge data and services, PLIADES aims to drive advancements in Cooperative, Connected, and Automated Mobility (CCAM), Advanced Driver Assistance & Autonomous Driving (ADAS/ AD), and Human-Robot Interaction (HRI).

Under the coordination of Dr. Dimitrios Giakoumis (CERTH-ITI), the PLIADES project brings together a consortium of twenty-seven* partners from 10 EU Member States and Switzerland. This diverse consortium includes 12 Research and Technology Organisations (RTOs), 10 Small and Medium-sized Enterprises (SMEs), and 5 Non-Profit Organisations (NPOs).

The project seeks to research and develop novel models, abstractions, and methods for environmentally friendly, context- and human-factors-aware creation of vast amounts of data for mobility, green deal, energy, industrial, and healthcare dataspaces. Moreover, PLIADES aims to advance data privacy, security, trustworthiness, and sovereignty, enabling data owners to safely determine how their information is collected, stored and used. Additionally, PLIADES seeks to promote advanced data decentralisation and further advance AI-boosted brokers, to generate advanced Data Space connectors to extend the scope of data space interoperability, integrating full data life cycles into existing data reference architectures, such as the IDS Reference Architecture Model. Furthermore, PLIADES is committed to facilitating novel data processing and analytics services to ensure data privacy, trustworthiness, security, resilience, reuse, and disposal.

PLIADES will deploy its proposed framework in six use cases spanning transportation, energy, manufacturing, healthcare, and the green deal sectors. These use cases will leverage different types of data to drive innovations in areas such as smart vehicles, predictive maintenance, personalised healthcare services, and more, solving critical problems and improving everyday life.

*Consortium members include:

Centre for Research and Technology-Hellas CERTH (Greece), I4byDesign Competence Center Private Company (Greece), Antlantis Engineering SA (Greece), Vicomtech (Spain), Eurecat (Spain), Energy@Work (Italy), FinEst Centre for Smart Cities, TalTech (Estonia), Innovalia (Spain) Tecnalia (Spain), denn Industrias Puigjaner (Spain), Blue Ocean Robotics (Denmark), Czech Technical University in Prague (Czech Republic), Technische Hochschule Ingolstadt (Germany), ZERO GmbH (Germany), Universidad Carlos III de Madrid (Spain), Mondragon Goi Eskola Politeknikoa Jose Maria Arizmendiarrieta, S.Coop (Spain), KU Leuven, Mecha(tro)nic System Dynamics (LMSD) research group (Belgium), Ceit – Centro de Estudios e Investigaciones Técnicas (Spain), IDSA – International Data Spaces e.V. (Germany), ΤΝΟ – Netherlands Organisation for Applied Scientific Research (Netherlands), CIC bioGUNE – Asociación Centro de Investigación Cooperativa en Biociencias (Spain), BasqueCCAM – Asociación Centro Vasco de Movilidad Conectada, Cooperativa y Autónoma (Spain), AVL List GmbH (Austria), White Research (Brussels), Hypertech S.A. (Greece), Libattion AG (Switzerland), Sipbb – Switzerland Innovation Park Biel/ Bienne AG (Switzerland)

Press release – Project overview ⇲