In this paper, we present an architecture for mobility data spaces enabling trustworthy and reliable data operations along with its main constituent parts. The architecture makes use of a data lake for scalable storage of diverse mobility data sets, on top of which separate computing and storage layers are implemented to allow independent scaling with a data operations toolbox providing all data operations. Furthermore, to cater for mobility analytics, machine learning and artificial intelligence support, an edge analytics suite is provided that encompasses distributed algorithms for mobility analytics and federated learning, thereby exploiting edge computing technologies. In turn, this is supported by a resource allocator that monitors the energy consumption of data-intensive operations and provides this information to the platform for intelligent task placement in edge devices, aiming at energy-efficient operations. As a result, an end-to-end platform is proposed that combines data services and infrastructure services towards supporting mobility application domains, such as urban and maritime.

This article contributes to the policy dialogue about how to govern healthcare data in the AI era and how to incentivize patients to share their data. Existing approaches to data-sharing restrict the flow of data. Yet, as healthcare AI technologies rely on data in enhancing their scope, such lack of data hinders the creation of future applications and diminishes the need for data to furnish them. We shift attention to a GDPR based policy that does not restrict data flows and argue that the existing experience in monetizing digitalized copyright material such as music can offer a practical and well tested solution.