Schema Evolution publication categorizer

Technologies for overcoming heterogeneities between
autonomous data sources are key in the emerging
networked world. Our doctoral research investigates technologies
for alleviating structural heterogeneity between
relational data sources. At the heart of structural heterogeneity
is the data mapping problem. The data mapping
problem is to discover effective mappings between structured
data sources. These mappings are the basic “glue”
for facilitating large-scale ad-hoc information sharing
between autonomous peers in a dynamic environment.

The data mapping problem is to discover effective mappings
between structured representations of data. These
mappings are the basic ‘glue’ for facilitating large-scale
ad-hoc information sharing between autonomous peers in a
dynamic environment. Automating their discovery is one of
the fundamental unsolved challenges for information integration
and sharing on the Web. We outline a general approach
to automating the discovery of mappings between
relational data sources which leverages new perspectives
on the data mapping problem and report on a prototype implementation.

We demonstrate a prototype of the relational data mapping module
of MIQIS, a formal framework for investigating information flow
in peer-to-peer database management systems. Data maps constitute
effective mappings between structured data sources. These
mappings are the ‘glue’ for facilitating large scale ad-hoc information
sharing between autonomous peers, and automating their
discovery is one of the fundamental unsolved challenges for information
interoperability and sharing. Our approach to automating
data map discovery utilizes heuristic search within a space delineated