Towards a Data Space for Interoperability of Analytic Provenance

Capturing, visualizing and analyzing provenance data to better understand and support analytic reasoning processes is a rapidly growing research feld named analytic provenance. Provenance data includes the state of a visualization within a tool as well as the user's interactions performed while interacting with the tool. Research in this feld has produced in many new approaches that generate data for specifc tools and use cases. However, since a variety of tools are used and analytic tasks are performed in real analysis use cases there is a problem in building an interoperable baseline data corpus for investigation of the transferability of different approaches. In this paper, we present a visionary data space architecture for integrating and processing analytic provenance data in a unifed way using semantic modeling. We discuss emerging challenges and research opportunities to realize such a vision using semantic models in data spaces to enable analytic provenance data interoperability.