Expertise

Dataspace Technologies:
Designing and technologically implementing sovereign data spaces

The Fraunhofer Institute for Software and Systems Engineering focuses its research on the development of data spaces in which data providers retain control over what happens to their data even after it has been shared. Without this data sovereignty, cross-company data exchange scenarios would be inconceivable in many industries and fields of implementation. It is therefore the key to new value chains based on the fair sharing of data. Data spaces are an essential part of the European Data Strategy. They form the digital foundation for a sustainable economy in Germany and Europe.

Numerous initiatives already exist today that focus on the relevance of data spaces for business and society and aim to develop standards or implement exemplary industry solutions, for example. These include the International Data Spaces Association (IDSA), the Gaia-X AISBL, domain-specific data spaces such as the Catena-X Automotive Network, the Mobility data Space and Eona-X, as well as coordination projects such as the EU-funded Data Spaces Support Centre (DSSC). At a technological level, the Eclipse Foundation has started to set up its own working group on data spaces, which aims to develop open source software components for data spaces. Fraunhofer ISST is an important player in all initiatives and is one of the leading initiators of the data space approach.

The expertise of Fraunhofer ISST employees in the technological implementation of data spaces is correspondingly high. It includes:

• Technology:
  • Implementation of (OSS) dataspace components required for setting up a dataspace environment.
  • Provision of all necessary dataspace components (e.g. Eclipse Dataspace Components)
  • Continuous increase in the maturity level of the solutions
• Adoption:
  • Easier setup and configuration of new dataspaces by adopting existing components
  • Demonstrators and blueprints for various domain-independent scenarios
• Concepts:
  • Specification of the conceptual model of data spaces
  • Designing value creation in and with data spaces through the development of business models and use cases
  • Requirements analysis and transfer to technical implementation
  • Transfer of concepts into formal standards