ScholarlyCommons

Title

Schema Mediation for Large-Scale Semantic Data Sharing

Author(s)

Alon Y. Halevy, University of Washington
Zachary G. Ives, University of PennsylvaniaFollow
Dan Suciu, University of Washington
Igor Tatarinov, University of Washington

Date of this Version

March 2005

Document Type

Journal Article

Recommended Citation

Alon Y. Halevy, Zachary G. Ives, Dan Suciu, and Igor Tatarinov, "Schema Mediation for Large-Scale Semantic Data Sharing", . March 2005.

Comments

Postprint version. Published in VLDB Journal : the International Journal on Very Large Data Bases, Volume 14, Issue 1, March 2005, pages 68-83. The original publication is available at www.springerlink.com.
Publisher URL: http://dx.doi.org/10.1007/s00778-003-0116-y

Abstract

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 common and 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 backward compatibility. As a result, many large-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 that 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. Then we describe several methods for optimizing the reformulation algorithm and an initial set of experiments studying its performance. Finally, we define and consider several global problems in managing semantic mappings in a PDMS.

Keywords

peer data management, data integration, schema mediation, web and databases