Over the last few years, the European Union (EU) has placed significant emphasis on the interoperability of critical infrastructures (CIs). One of the main CI transportation infrastructures are ports. The control systems managing such infrastructures are constantly evolving and handle diverse sets of people, data, and processes. Additionally, interdependencies among different infrastructures can lead to discrepancies in data models that propagate and intensify across interconnected systems. This article introduces “BigDaM”, a Big Data Management framework for critical infrastructures. It is a cutting-edge data model that adheres to the latest technological standards and aims to consolidate APIs and services within highly complex CI infrastructures. Our approach takes a bottom-up perspective, treating each service interconnection as an autonomous entity that must align with the proposed common vocabulary and data model. By injecting strict guidelines into the service/component development’s lifecycle, we explicitly promote interoperability among the services within critical infrastructure ecosystems. This approach facilitates the exchange and reuse of data from a shared repository among developers, small and medium-sized enterprises (SMEs), and large vendors. Business challenges have also been taken into account, in order to link the generated data assets of CIs with the business world. The complete framework has been tested in the main EU ports, part of the transportation sector of CIs. Performance evaluation and the aforementioned testing is also being analyzed, highlighting the capabilities of the proposed approach.

The current data landscape presents several challenges in data governance and integration. This paper presents the integration of the Data Fabric implemented in the Horizon Europe project aerOS, which integrates and unifies data available in the IoT-Edge-Cloud continuum, with the Data Space implemented in the RE4DY project in order to extend the capabilities of the Data Fabric by enabling the definition fine-grained data access policies and data monetization. This integration would promote collaboration with other participants in the Data Space based on the reuse of data assets from the Data Fabric by sharing them in a trusted environment and the integration of new data or services with the Data Fabric, thus contributing to the creation and integration of new data ecosystems.