Context Interchange: Representing and Reasoning about Data Semantics in Heterogeneous Systems

The Context INterchange (COIN) strategy [44; 48] presents a novel perspective for medi-ated data access in which semantic conflicts among heterogeneous systems are not identified a priori, but are detected and reconciled by a Context Mediator through comparison of con-texts associated with any two systems engaged in data exchange. In this paper, we present a formal characterization and reconstruction of this strategy in a COIN framework, based on a deductive object-oriented data model and language called COIN. The COIN framework pro-vides a logical formalism for representing data semantics in distinct contexts. We show that this presents a well-founded basis for reasoning about semantic disparities in heterogeneous systems. In addition, it combines the best features of loose- and tight-coupling approaches in defining an integration strategy that is scalable, extensible and accessible. These latter features are made possible by allowing complexity of the system to be harnessed in small chunks, by enabling sources and receivers to remain loosely-coupled to one another, and by sustaining an infrastructure for data integration