Context Interchange: Overcoming the Challenges of Large-Scale Interoperable Database Systems in a Dynamic Environment
Cheng Hian Goh, Stuart Madnick, Michael D. Siegel · DSpace@MIT (Massachusetts Institute of Technology) · 2011
Research in database interoperability has primarily focused on circumventing schematic and semantic incompatibility arising from autonomy of the underlying databases.We argue that, while existing integration strategies might provide satisfactory support for small or static systems, their inadequacies rapidly become evident in large-scale interoperable database systems operating in a dynamic environment.The frequent entry and exit of heterogeneous interoperating agents renders "frozen" interfaces (e.g., shared schemes) im- practical and places an ever increasing burden on the system to accord more flexibility to heterogeneous users.User heterogeneity mandates that disparate users' conceptual models and preferences must be accommodated, and the emergence of large-scale networks sug- gests that the integration strategy must be scalable and capable of dealing with evolving semantics.As an alternative to the integration approaches presented in the literature, we propose a strategy based on the notion of context interchange.In the context interchange framework, assumptions underlying the interpretations attributed to data are explicitly represented in the form of data contexts with respect to a shared ontology.Data exchange in this framework is accompanied by context mediation whereby data originating from multiple source contexts is automatically transformed to comply with the receiver context.The focus on data contexts giving rise to data heterogeneity (as opposed to focusing on data conflicts exclusively) has a number of advantages over classical integration approaches, providing interoperating agents with greater flexibility as well as a framework for graceful evolution and efficient implementation of large-scale interoperable database systems.