The PLIADES project continues to strengthen its scientific impact with the publication of two new peer-reviewed research articles by project partners. These works address two core pillars of PLIADES: secure and interoperable data spaces and sustainable AI-enabled data lifecycles, reinforcing the project’s contribution to Europe’s data and AI strategy.
🔹 Security and interoperability of European data space architectures
The first article, “A Security Analysis of European Data Space Architectures”, published in Data Science and Engineering (Springer), delivers a comprehensive review and comparative analysis of major European data space frameworks, including IDS-RAM, Gaia-X, FIWARE, and IHAN.
The study examines how these architectures address security, privacy, and interoperability, highlighting their strengths, limitations, and common challenges. It provides structured insights into governance models, trust mechanisms, identity management, and usage control, while also proposing recommendations to enhance data sovereignty, cross-platform interoperability, and privacy protection.
This work directly supports PLIADES’ objective to enable trusted, compliant, and interoperable data spaces, contributing valuable scientific foundations for the next generation of European data ecosystems.
🔹 Toward sustainable data collection processes for autonomous vehicles
The second article, “Toward Sustainable Data Collection Processes for Autonomous Vehicles”, published in IEEE Access, focuses on a critical emerging challenge: how to manage the exploding volumes of autonomous vehicle data in a sustainable, efficient, and privacy-compliant way.
The authors propose and validate a scalable, modular data post-processing pipeline that integrates techniques such as deduplication, anonymization, event-based filtering, and compression. The results demonstrate that the approach can reduce storage requirements by over 50%, lower computational overhead, and simplify downstream AI training workflows, without compromising data quality.
This research aligns closely with PLIADES’ vision of AI-enabled data lifecycle optimisation, showing in practice how sustainability principles can be embedded directly into data creation and management pipelines.
