Uncertainty in Data Integration and Dataspace Support Platforms

Abstract

Data integration has been an important area of research for several years. However, such systems suffer from one of the main drawbacks of database systems: the need to invest significant modeling effort upfront. Dataspace support platforms (DSSP) envision a system that offers useful services on its data without any setup effort and that improves with time in a pay-as-you-go fashion. We argue that to support DSSPs, the system needs to model uncertainty at its core. We describe the concepts of probabilistic mediated schemas and probabilistic mappings as enabling concepts for DSSPs.