Eleven Papers Map the Frontier of Data Spaces and Data Governance

Big data and data science have moved a long way since their core concepts were introduced more than a decade ago. Novel paradigms such as cyber-physical systems, the computing continuum, and distributed analytics have been proposed and backed by technical advances in storage and processing speed. With recent developments in machine learning and artificial intelligence layered on top, the importance of big data governance and data science has grown dramatically, and policymakers now recognize data as a cornerstone of the digital society. Yet the same data revolution that creates opportunity also opens new gaps and challenges, which is precisely the territory a new special issue of the journal Data Science and Engineering sets out to explore.

The special issue, titled “Towards the Next Frontier in Data Management: Data Spaces and Data Governance,” collects the most recent advances in big data and data science, with particular focus on the creation of virtual data spaces and data-based innovative processes and applications. It was organized with the support of the CINI National Lab on Data Science and drew 24 papers through an open call for submissions, including best papers from the 3rd Italian Conference on Big Data and Data Science, ITADATA2024. After what the editors describe as a rigorous peer-review process, 11 papers were selected, spanning theoretical aspects, innovative applications in different domains, and surveys. The editorial introducing the collection was published on 15 June 2026 and is open access.

The accepted papers open with a statistical deep dive. In “Evaluation of Estimation Methods for Simultaneous Equations Models Across Varying Levels of Data Variability,” Pérez-Sánchez, B. and colleagues examine Simultaneous Equations Models, regression equations widely used in complex systems modeling such as economics and health sciences.

Engineering the plumbing of cloud systems comes next. Rucco, C. and co-authors, in “Enhancing Data Ingestion Efficiency in Cloud-Based Systems: A Design Pattern Approach,” propose a novel design pattern supporting data ingestion in cloud-based systems, addressing the challenges of high-volume data processing. Complementing this systems-level view, Jehangiri, A. I. and colleagues present “Data Platforms for Multi-Organizational Settings: Systematic Literature Review, Use Cases and Reference Architecture,” a literature review on platforms that enable data sharing and processing among multiple organizations. Building on their findings, the paper also proposes a reference architecture for such collaborative platforms.

The first, “A Security Analysis of European Data Space Architectures” by Singh, A. K. and colleagues, investigates the main European data space architectures, namely IDS-RAM, Gaia-X, FIWARE, and IHAN, with particular reference to their security aspects.

Health data governance receives dedicated attention in “Evaluating Regulatory Compliance in Maturity Models for Patient-Centred Health Data Sharing: A Literature Review,” a survey in which Breidenbach, M. and Geisler, S. analyze the literature on EU-based health data ecosystems with a focus on maturity models. In a related governance direction, Hmimou, A. and colleagues, in “Operationalizing and Automating Data Validation in Data Spaces,” present a novel approach based on knowledge graphs to automate data validation according to data policies in data spaces.

Two further data space papers extend the concept into simulation and access control. Coppolino, L. and co-authors examine the possible integration of data spaces with simulation services in “Simulation as a Service in Data Spaces: A Digital Twin-based Approach,” proposing a framework that leverages digital twins to expose simulation services within data spaces. On the security side, Fotiou, N. and colleagues propose, in “Relationship-Based Access Control for Data Spaces,” a novel access control approach tailored for data spaces that natively models the relationships among entities in a data space and the organizational structure of data consumers.

The issue then pivots from data spaces to applications. Frazzetto, P. and colleagues, in “Graph Neural Networks for Candidate-Job Matching: An Inductive Learning Approach,” propose and validate on a real-world dataset a novel approach for candidate-job matching using Graph Neural Networks.

Industrial data streams pose different challenges, and Colombi, L. and colleagues tackle them in “Embedding Models for Multivariate Time Series Anomaly Detection in Industry 5.0.” They introduce an anomaly detection framework for multivariate time series based on two embedding models, and report that it is more robust to noise than existing approaches.

The collection closes with a resource for the medical artificial intelligence community. Siragusa, I. and colleagues present, in “MedPix20: A Comprehensive Survey of Medical Image Analysis,” a multimodal medical dataset called MedPix 2.0, showing its potential in LLM-based applications. Taken together, the eleven papers trace an arc from estimation theory and ingestion patterns, through the security, validation, and access control of shared data environments, to graph learning, industrial anomaly detection, and medical datasets. The editors close by thanking the authors, the reviewers whose comments helped improve the quality and selection of the papers, the editorial team of Data Science and Engineering, and the Editors-in-Chiefs, Prof. Bin Cui and Prof. Elena Ferrari, for the opportunity to serve as guest editors and for their guidance throughout the process.

Subject of Research: A peer-reviewed special issue on data spaces and data governance spanning security architectures, automated data validation, graph neural networks, and industrial anomaly detection.

Article Title: Special Issue Editorial on “Data Spaces and Data Governance”

Article References: Bena, N., Distefano, S., Romano, L., & Tzouganatou, A. (2026). Special Issue Editorial on “Data Spaces and Data Governance”. Data Science and Engineering, 11(2), 275-276. https://doi.org/10.1007/s41019-026-00354-w

Image Credits: AI Generated

DOI: 10.1007/s41019-026-00354-w

Keywords: data spaces, data governance, big data, data science, cloud computing, digital twins, graph neural networks, access control, health data sharing, anomaly detection, machine learning, peer review