Knowledge based query processing in large scale virtual organizations

Abstract

This work concerns query processing to support data sharing in large scale Virtual Organizations(VO). Characterization of VO's data sharing contexts reflects the coexistence of factors like sources overlapping, uncertain data location, and fuzzy copies in dynamic large scale environments that hinder query processing. Existing results on distributed query evaluation are useful for VOs, but there is no appropriate solution combining high semantic level and dynamic large scale environments required by VOs. This paper proposes a characterization of VOs data sources, called Data Profile, and a query processing strategy (called QPro2e) for large scale VOs with complex data profiles. QPro2e uses an evolving distributed knowledge base describing data sources roles w.r.t shared domain concepts. It allows the identification of logical data source clusters which improve query evaluation in presence of a very large number of data sources. © 2009 Springer Berlin Heidelberg.