by Andras Benczur (HUN-REN SZTAKI) and Dominik Ślęzak (University of Warsaw)

Large-scale data analytics empowers organizations to harness the full potential of the vast amounts of data they generate and collect. By driving innovation, enhancing business operations, personalizing customer experiences, and improving risk management, insights derived from large-scale data analytics are critical for gaining a competitive advantage and making informed, data-driven decisions. With the exponential growth of data generated by businesses, consumers, and connected devices, it is essential to address key challenges in handling Big Data, processing real-time information, and enabling timely, actionable insights.

by Cristóbal Barba-González, José F. Aldana-Montes, and Ismael Navas-Delgado (ITIS, University of Málaga) 

This article presents TITAN, a platform designed to enable the creation and execution of Big Data analytics workflows. Using semantic technologies, TITAN ensures the integration, validation, and reusability of data-driven components, empowering researchers and industries to handle large-scale data challenges more effectively. Through real-world case studies, we demonstrate its potential in transforming data processing workflows across various domains. 

by Massimiliano Assante (CNR-ISTI), Marco Lettere (Nubisware srl), Alfredo Oliviero (CNR-ISTI), and Pasquale Pagano (CNR-ISTI)

The D4Science platform is advancing reproducible research by providing scientists with robust, cloud-based tools for large-scale data analysis such as the Cloud Computing Platform (CCP). CCP enhances collaboration, allowing researchers to share, reuse, and build on each other’s work across diverse scientific disciplines.

by George Tzagkarakis (FORTH-ICS), Rommert Dekker (EUR-DE), and Themis Palpanas (UPC- LIPADE)

Effective decision-making in large-scale, uncertain systems faces growing challenges in today’s complex, data-rich environments. Traditional systems struggle to process vast datasets in real time while balancing conflicting objectives and ensuring fairness. The TwinODIS project introduces a transformative approach by combining Artificial Intelligence (AI) and Operations Research (OR) to create next-generation Decision Intelligence systems. This integration leverages advanced analytics, optimisation techniques, and AI-driven insights to transform large-scale decision-making, enabling sustainable development and economic growth through smarter, data-driven solutions. 

by Giulia Millitarì (University of Pisa and CNR-ISTI), Alessio Ferrari (CNR-ISTI) and Giorgio O. Spagnolo (CNR-ISTI)

We describe the initial and crucial phase of an analysis for a project belonging to the Spoke 4 on “Railway Transportation” of the Italian National Center for Sustainable Mobility (MOST) [L1], which is part of the National Recovery and Resilience Plan (PNRR). The objective of the project is the implementation of a predictive maintenance strategy within the decision-making process of Trenord [L2], a railway company responsible for the operation of regional passenger trains mostly in Lombardy. Before conducting the analysis, it was essential to perform extensive data mining procedures to make the data from the remote diagnostic system truly usable for extracting meaningful insights and apply machine learning techniques effectively.

by Stelios Sartzetakis (ATHENA RC) and Chamanara, Javad (ΤΙΒ)

In an era when data availability and AI technologies advance rapidly, the European data economy is poised for substantial growth, unlocking new opportunities and innovations. The DataBri-X project [L1] is motivated by the need to foster the development of trustworthy, “made in Europe” AI that embodies European values and ethical standards. DataBri-X focuses on transforming data-sharing ecosystems by advancing data lifecycle practices, tools, and governance frameworks.

by Andrea Manzi, Raul Bardaji and Ivan Rodero (EGI.eu) 

The paper reviews the need to create a ‘Digital Twin Engine’ to support developers in reusing modular components to speed up the building process of Scientific Digital Twins. It highlights how the interTwin project leads this effort by providing a robust framework that cuts development time, supports scalability, and promotes collaboration between domains. Furthermore, the article highlights some ongoing use cases based on this Digital Twin Engine, showing the use and potential impacts.

by Gergely Sipos (EGI Foundation) and Dick Schaap (MARIS)

iMagine’s AI-driven platform and modules empower researchers to analyse vast amounts of images, accelerating scientific discoveries from the micro to the macro level.

by Karina Medwenitsch, Markus Schindler, and Christoph Klikovits (Forschung Burgenland GmbH) 

How can advanced data analysis reshape agriculture in Austria’s climate-stricken Seewinkel region? By combining IoT, AI, and real-time environmental analysis, researchers at Forschung Burgenland are pioneering innovative solutions to optimise water management and support the energy transition, ensuring resilience in the face of climate change.

 by Christoph Klikovits (Forschung Burgenland) and Christoph Fabianek (OwnYourData)

The energy sector generates a high volume of data, but data analysts face significant barriers due to issues like security, privacy, and GDPR compliance. These challenges often hinder data sharing, analysis, and interpretation, which are essential for unlocking the added value and insights that data can provide. How can accessible governance solutions help to overcome these obstacles in the energy domain?

by Ioannis Rotskos (IPTO), Orestis Vantzos (IPTO) and Panagiotis Papadakos (ERCIM)

The transition towards sustainable energy systems is accompanied by huge data volumes generated by modern electricity grids. Within the GLACIATION EU project, Use Case 4 (UC4) demonstrates how scalable anomaly detection algorithms and the edge-cloud continuum enable efficient analytics in energy grid management. This use case highlights the deployment of advanced analytics frameworks for processing SCADA data, extracting actionable insights, and optimizing the energy utilization of renewable sources. UC4 employs the GLACIATION platform, which orchestrates distributed workloads across data centers, focusing on sustainability and cost efficiency by leveraging locally produced green energy.

by Alex Suta (Széchenyi István University), Loránd Kedves (Széchenyi István University), Árpád Tóth (Széchenyi István University)

The adoption of eXtensible Business Reporting Language (XBRL) for annual corporate disclosures is reshaping data accessibility and analytical methodologies. This technical study explores how European companies use XBRL to enhance data standardisation, which facilitates large-scale financial and sustainability analyses for practitioners and researchers.

by Michael Hubner and Jan Nausner (AIT Austrian Institute of Technology) 

In this article, we introduce our Multimodal Fusion Architecture for Sensor Applications - MuFASA, which our research group has developed at the Austrian Institute of Technology. It offers a robust fusion architecture for real-time sensor applications, providing situational awareness and precise decision support.

by Olivier Parisot (Luxembourg Institute of Science and Technology)

Capturing deep sky video streams has become accessible and inexpensive thanks to recent hardware and software advances, but the growing number of satellites in Low Earth Orbit (LEO) generates undesired light pollution. Thus, we are currently developing a resource-aware AI system for automatically detecting specific targets like satellite streaks in video streams produced with affordable observation stations.

by Jiri Bouchal (Digital Resilience Institute), Hugo Matousek (InnoConnect), Jan Ježek (University of West Bohemia)

GLayer is GPU-accelerated backend software designed for fast aggregation, filtering and visualisation of spatial data. Modelled on the OpenGL technology, GLayer is capable of performing analytical queries on large-scale datasets with millions of data points in a matter of milliseconds. At present, the tool is being tested in Aarhus as part of the BIPED project to support the city’s transition to net-zero emissions.

by José García-Nieto (ITIS, University of Málaga), Virginia García Millán (ITIS, University of Málaga), and José F. Aldana-Montes (ITIS, University of Málaga)

Researchers from ITIS Software work on projects for the generation of big data workflows for processing and analysis of earth observation, remote sensing satellite data. These handle massive amounts of data to obtain value-added applications in agroforestry, the environment, smart cities, and for society in general. As a use case, this paper provides an example of the use of Sentinel-2 satellite data for the generation of a land-cover map over a large area, the Mediterranean basin, using machine-learning algorithms and big data analysis.

by Balázs Pejó (Budapest University of Technology and Economics) and Delio Jaramillo Velez (Chalmers University of Technology)

To what extent do individual contributions enhance the overall outcome of collaborative work? This question naturally arises across scientific fields and is particularly challenging in Federated Learning. It remains largely unexplored in privacy-preserving settings where individual actions are concealed with techniques like Secure Aggregation.

by Mohammed Salah Al-Radhi and Géza Németh (Budapest University of Technology and Economics, TMIT-VIK, Budapest, Hungary) 

How can brain activity be turned into clear, intelligible speech? An ambitious research initiative in Hungary is addressing this question by developing cutting-edge methods to decode neural signals into speech, aiming to restore communication for individuals with severe speech disorders. 

by Beatrix Koltai, Gergely Ács, and András Gazdag (Budapest University of Technology and Economics)

In telemonitoring, false alarms from medical devices can overwhelm doctors and desensitize care teams to critical issues. How could we reliably detect subtle yet critical changes in a patient’s health status without false alarms or missed anomalies? By correlating data across multiple sensors, our solution improves detection accuracy and results in fewer false positives. Utilizing federated learning, our model is collaboratively trained across multiple hospitals, each with potentially limited data. This improves the model’s performance without centralising sensitive patient data.

by Stelios Zimeras (University of the Aegean)

At the University of the Aegean, we are developing advanced visualisation techniques and innovative algorithms to enhance digital epidemiology, enabling more effective disease monitoring and response through the integration of diverse big data sources.