Schema Evolution publication categorizer

In this paper, we describe an in-progress effort to augment the data integration capabilities of a service-oriented data integration product with the advanced matching capabilities of a knowledgebased schema matching workbench, thereby providing a guided, interactive, and “what”-oriented design environment for use by data architects and integration engineers.

A key aspect of any data integration endeavor is establishing a transformation that translates instances of one or more source schemata into instances of a target schema. This schema integration task must be tackled regardless of the integration architecture or mapping formalism. In this paper we provide a task model for schema integration. We use this breakdown to motivate a workbench for schema integration in which multiple tools share a common knowledge repository.

Most recent schema matching systems assemble multiple components, each employing a
particular matching technique. The domain
user must then tune the system: select the
right component to be executed and correctly
adjust their numerous \"knobs\" (e.g., thresholds, formula coefficients). Tuning is skill- and
time-intensive, but (as we show) without it the
matching accuracy is significantly inferior.
We describe eTuner, an approach to automatically tune schema matching systems. Given
a schema S, we match S against synthetic
schemas, for which the ground truth mapping