Schema Evolution publication categorizer

Most data integration applications require a matching
between the schemas of the respective data sets. We show
how the existence of duplicates within these data sets can be
exploited to automatically identify matching attributes. We
describe an algorithm that first discovers duplicates among
data sets with unaligned schemas and then uses these duplicates
to perform schema matching between schemas with
opaque column names.
Discovering duplicates among data sets with unaligned
schemas is more difficult than in the usual setting, because

Mediators – used in federated information systems – provide a homogeneous read-only
access to a set of autonomous information sources. To achieve semantic integration of the
heterogeneous data, correspondences between the autonomous schemas are specified
manually by experts. Considering the continuous evolution of those typically long-living
information systems, schema evolution is an important aspect. New concepts are necessary to adapt correspondences consistently to evolving schemas.
In this paper we propose a formalized schema evolution mechanism for federated information