Schema Evolution publication categorizer

This paper presents XLearner, a novel tool that helps
the rapid development of XML mapping queries written
in XQuery. XLearner is novel in that it learns XQuery
queries consistent with given examples (fragments) of intended
query results. XLearner combines known learning
techniques, incorporates mechanisms to cope with issues
specific to the XQuery learning context, and provides a systematic
way for the semi-automatic development of queries.
This paper describes the XLearner system. It presents algorithms
for learning various classes of XQuery, shows that

We are developing a software tool to support schema
matching for data transformations. Schema matching
is the process of finding relationships between components
of two given database schemas. The tool is
unique in that it first extracts conceptual schemas from
the two database schemas and allows the user to use
the extracted conceptual schemas as clues to perform
schema matching. This paper overviews the tool and
explains its implementation.

We focus on the problem of data transformations, i.e., how to
transform data to another structure to adapt it to new application
requirements or given environments. Here, we define data transformation
as the process of taking as input two schemas A and B
and an instance of A, and producing an instance of B. Today, data
transformations are required in many situations: to integrate multiple
information sources, to construct and receive data for Web
services, and to migrate data from legacy systems to new systems,
from local databases to data warehouses. This demonstration focuses