Data sovereignity

You want to be sure that you can determine who is allowed to do what with your data in a dataspace, who is not, and under what conditions. Data sovereignty is understood to mean the greatest possible control and dominion over one's (own) data. In business and politics, the term is often used synonymously with the term digital sovereignty. However, an official definition is still lacking.

Data Traceability.

You also want transparency about what actually happens to your data when you share it. The identification of personal data and tracking and identifying the respective processing operations is considered a supporting process, therefore there is no precise provision in the GDPR that specifies the procedure. Nevertheless, it is an indispensable prerequisite to properly apply the requirements of the GDPR.

Trust anchor.

You also want to be able to trust that whoever claims to be an actor in such a data space, or in an ecosystem based on it, is really who they claim to be.

Data spaces.

1. Data spaces aim at a virtual integration of physically distributed data.
Essentially, the data should be left where it is generated and managed.

2. A common database pattern is not intended, so no pattern integration is attempted. The data spaces concept, as a data integration concept, provides integration at the semantic level. For this reason, it is important to have vocabularies in place in order to ensure semantic interoperability between data from distributed databases.

3. The characteristic of dataspaces is that we don't necessarily have a single source of truth, not least because dataspaces are very distributed architectures.

4. Dataspaces können in sich selbst verschachtelt sein. Man kann sich vorstellen, dass man ineinander verschachtelte Räume aus Dataspaces bilden kann, die überlappend sein können.