Data/knowledge packets as a means of supporting semantic heterogeneity in multidatabase systems

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Abstract
Semantic heterogeneiry in heterogeneous autonomous databases poses problems in instance matches, units conversion (value interpretation), contextual and structural mismatches, etc. In this work we examine some of the research issues in semantic heterogeniety and propose a novel architecture for resolving such problems. The approach involves the use of Artifical 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. We introduce the notion of Data/Knowledge Packets as a means of supporting both operational and structural semantic heterogeneity.

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