Schema Evolution publication categorizer

Most virtual database systems are suitable for environments
in which the set of member information sources is small and stable.
Consequently, present virtual database systems do not scale up very
well. The main reason is the complexity and cost of incorporating new
information sources into the virtual database. In this paper we describe
a system, called Autoplex, which uses machine learning techniques for
automating the discovery of new content for virtual database systems.
Autoplex assumes that several information sources have already been
incorporated mapped" into the virtual database system by human
experts as done in standard virtual database systems . Autoplex learns
the
features
of these examples. It then applies this knowledge to new
candidate sources, trying to infer views that resemble" the examples.
In this paper we report initial results from the Autoplex project.