Distributed Systems
Work description
The growing adoption of data space architectures in industrial and scientific contexts raises new challenges in terms of interoperability and efficient access to data distributed across multiple autonomous participants. Domains such as healthcare, energy, and manufacturing generate increasing volumes of heterogeneous data, whose controlled sharing between organizations is essential to enable federated analytics, process optimization, and informed decision-making. Initiatives such as International Data Spaces (IDS) and GAIA-X have been establishing reference frameworks for this sovereign data sharing, defining connector models, access control, and metadata management. In this context, the use of SQL as a unified query language over heterogeneous data sources emerges as a promising approach to simplify federated access to information, while maintaining the sovereignty and compliance guarantees required by these architectures. The planned activities include: - Study and prototyping of abstraction layers for the execution of federated SQL queries over heterogeneous data sources interconnected by IDS connectors. - Analysis of distributed query execution capabilities, considering the data sovereignty requirements and access control models defined by the IDS and GAIA-X frameworks. - Exploration of query optimization strategies in contexts where data resides in autonomous nodes with distinct sharing policies, minimizing unnecessary data transfer and maximizing local processing (query pushdown). - Investigation of metadata registration and publication mechanisms based on the IDS Information Model and GAIA-X catalogues, within the scope of the data discoveryproblem. - Development of techniques for automatic schema discovery in heterogeneous data representations. - Study of approaches for inferring the query capabilities available at each data space participant, supporting the dynamic formulation of distributed queries. - Implementation of a functional prototype integrating a federated SQL query execution engine with data discovery capabilities using IDS and/or GAIA-X connectors. - Evaluation of the prototype's performance and scalability in representative scenarios of industrial and scientific data infrastructures. - Dissemination of results through publications in leading conferences and journals in the areas of distributed databases, data architectures, and federated computing. - Writing a doctoral thesis in the context of the developed work. - Writing an activity report regarding the grant.
Academic Qualifications
- Enrollment in a PhD program in Computer Science or a related field.
Minimum profile required
- Knowledgeable in Distributed Systems;- Comprehensive knowledge of IDS and GAIA-X ecosystems, namely of connectors, demonstrated through academic or professional projects;- At least 1 article published in peer-reviewed conferences or journals.
Preference factors
- Comprehensive knowledge in permission management mechanisms, such as XACML; - Comprehensive knowledge of permission management in distributed settings; - Prior experience with federated query processing frameworks or engines.
Application Period
Since 21 May 2026 to 03 Jun 2026
Centre
High-Assurance Software