A New Type of Metadata for Querying Data Integration Systems

Research on data integration has provided languages and systems able to guarantee an integrated intensional representation of a given set of data sources. A significant limitation common to most proposals is that only intensional knowledge is considered, with little or no consideration for extensional knowledge. In this paper we propose a technique to enrich the intension of an attribute with a new sort of metadata: the “relevant values”, extracted from the attribute values. Relevant values enrich schemata with domain knowledge; moreover they can be exploited by a user in the interactive process of creating/refining a query. The technique, fully implemented in a prototype, is automatic, independent of the attribute domain and it is based on data mining clustering techniques and emerging semantics from data values. It is parametrized with various metrics for similarity measures and is a viable tool for dealing with frequently changing sources.