The task of model based data integration becomes more complicated when the data sources to be integrated are distributed, heterogeneous, and high in number. One recent solution to the issues of distribution and scale is to perform data integration using peer-to-peer (P2P) networks. Current P2P data integration architectures have mostly been flat, only specifying mappings directly between peers. Some do form the schemas into hierarchies, but none provide any abstraction of the schemas. This paper describes a set of general purpose P2P meta-data and data exchange primitives provided by an extended version of the AutoMed toolkit, and uses the primitives to implement a new architecture called iXPeer. iXPeer deals with integration on several levels of abstraction, where the lower levels define precise mappings between data source schemas, but the higher levels are loser associations based on keywords.
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