Widely spread breast cancer takes patients to an early grave. Early
detection and ability to predict the effectiveness of treatments are among the
means to fight this malignant disease. Data federation from dozens of data sources
is needed for data analytics. The granularity, internality, structure and all other
characteristics differ in federated data. We discuss alternative approaches to data
federation and their theoretical basis, especially the ontology and governance of
data. We developed an artifact in our on-going research. The artifact is used to
support the federation of cancer data at a university hospital. We detected that our
federative approach and the artifact improved the interoperability of data in the
case. We suggest that our approach is capable to that also in other contexts.