In the current rapidly evolving technological landscape, Data Spaces and Digital Twins represent two pivotal and transformative data-driven elements whose integration discloses novel opportunities for multiple stakeholders. Data Spaces (DS) are virtual and secure ecosystems designed for managing and aggregating heterogeneous real-time and historical data sources, with the aim of enabling more efficient data sovereignty policies and more interoperable data exchange. Digital Twins (DT) are virtual replicas that allow simulating and analysing the behaviour of a complex physical asset or system under different conditions, both in real time and off-line.
DT have proven to be especially helpful in supporting the decision-making process of cities, industries, and corporations by enabling operators to identify potential issues and optimise system performance without disrupting the services provided to the public. This is particularly relevant in: a) Critical Entities (CE), which are resources and infrastructures providing essential services for maintaining vital societal functions and economic activities (e.g., transportation, energy, or water infrastructures), and b) Smart Urban Communities, which leverage smart technologies to benefit stakeholders and citizens of an interconnected city. In both these cases, the role of data is pivotal, since the accuracy and usefulness of DT strongly depend on data availability and data quality in order to achieve a true digital resilience of critical infrastructures.
The relevance of a proper integration between DT and DS is also confirmed by a series of recent initiatives aimed at proposing dedicated architectures (e.g., International Data Spaces, IDS), collaborative data sharing environments (e.g., European data strategy), and architectural standards (e.g., Gaia-X) in order to enable trusted data exchange in accordance with European data protection guidelines.
The enabling role of DS is rapidly gaining momentum in contexts where centralised cloud-based data storage solutions have to be integrated with highly-distributed edge data processing. Furthermore, data sovereignty requirements in DS for DT for critical entities constitute another engaging challenge, as it is crucial to ensure that data are secured and protected from unauthorised access, misuse, or deletion, particularly when the right of a country or an organisation to keep the ownership and control over its own data is at stake. This aspect is even more relevant in specific geographical regions, as the recent Critical Entity Resilience Directive (CER) of the European Union asks EU Member States to define national resilience harmonisation strategies and risk assessment frameworks, which would greatly benefit from the adoption of data-driven approaches. Moreover, the data generated by a DT for a CE usually needs to be shared with other stakeholders, such as national and international regulatory authorities or maintenance contracting companies, and data sovereignty must be ensured without hindering data interoperability, sharing, and usage. Similarly, the European Data Space for Smart Communities (DS4SSCC-DEP) initiative promotes a federated deployment pattern for large-scale DSs aligned with Europe’s Digital Decade objectives [6]. Consequently, high-quality DS are needed to integrate and manage extremely variegated data sources, to allow data-interoperability among multiple systems and organisations, and to support data intensive processing and analytics, specifically focusing on scalability, privacy, and security requirements.
This workshop, now in its third year at the IEEE Big Data flagship conference in the big data sector, aims to bring together researchers and practitioners from computer science and data science domain and from the industry sector and from governmental bodies. The workshop represents a fruitful opportunity for discussing the latest research developments and the ongoing challenges in the field, as well as for fostering collaborations and networking.