Schema Mediation in Peer Data Management Systems
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distributed databases
query formulation
query languages
query processing
data integration tool
data management tool
peer data management system
query answering
complexity
query optimisation
query reformulation
schema design
schema mediation
semantic information
semantic mapping
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Intuitively, data management and data integration tools should be well-suited for exchanging information in a semantically meaningful way. Unfortunately, they suffer from two significant problems: they typically require a comprehensive schema design before they can be used to store or share information, and they are difficult to extend because schema evolution is heavyweight and may break backwards compatibility. As a result, many small-scale data sharing tasks are more easily facilitated by non-database-oriented tools that have little support for semantics. The goal of the peer data management system (PDMS) is to address this need: we propose the use of a decentralized, easily extensible data management architecture in which any user can contribute new data, schema information, or even mappings between other peers’ schemas. PDMSs represent a natural step beyond data integration systems, replacing their single logical schema with an interlinked collection of semantic mappings between peers’ individual schemas. This paper considers the problem of schema mediation in a PDMS. Our first contribution is a flexible language for mediating between peer schemas, which extends known data integration formalisms to our more complex architecture. We precisely characterize the complexity of query answering for our language. Next, we describe a reformulation algorithm for our language that generalizes both global-as-view and local-as-view query answering algorithms. Finally, we describe several methods for optimizing the reformulation algorithm, and an initial set of experiments studying its performance.
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Copyright 2003 IEEE. Reprinted from Proceedings of the 19th International Conference on Data Engineering 2003 (ICDE 2003), pages 505-516. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, author Zachary Ives was affiliated with the University of Washington. Currently (April 2005), he is a faculty member in the Department of Computer and Information Science at the University of Pennsylvania.
Copyright 2003 IEEE. Reprinted from Proceedings of the 19th International Conference on Data Engineering 2003 (ICDE 2003), pages 505-516. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of the University of Pennsylvania's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. NOTE: At the time of publication, author Zachary Ives was affiliated with the University of Washington. Currently (April 2005), he is a faculty member in the Department of Computer and Information Science at the University of Pennsylvania.