An intelligent heterogeneous autonomous database architecture for semantic heterogeneity support

Semantic heterogeneity in heterogeneous autonomous databases poses problems in instance matches, units conversion (value interpretation), contextual and structural mismatches, etc. The authors examine some of the research issues in semantic heterogeneity and propose a novel architecture for resolving such problems. The approach involves the use of artificial intelligence tools and techniques to construct domain models, that is data and knowledge representations of the constituent databases and an overall domain model of the semantic interactions among the databases. These domain models are represented as knowledge sources (KSs) in a blackboard architecture. This architecture lends itself to an opportunistic approach to query processing and goal-directed problem solving. They introduce the notion of data/knowledge packets as a means of supporting semantic heterogeneity, and show how an active and intelligent global thesaurus can be used to reformulate queries based on knowledge associated with terms and their usage in local databases