Preserving mapping consistency under schema changes

Preserving mapping consistency under schema changes In dynamic environments like the Web, data sources may change not only their data but also their schemas, their semantics, and their query capabilities. When a mapping is left inconsistent by a schema change, it has to be detected and updated. We present a novel framework and a tool (ToMAS) for automatically adapting (rewriting) mappings as schemas evolve. Our approach considers not only local changes to a schema but also changes that may affect and transform many components of a schema. Our algorithm detects mappings affected by structural or constraint changes and generates all the rewritings that are consistent with the semantics of the changed schemas. Our approach explicitly models mapping choices made by a user and maintains these choices, whenever possible, as the schemas and mappings evolve. When there is more than one candidate rewriting, the algorithm may rank them based on how close they are to the semantics of the existing mappings. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The VLDB Journal Springer Journals

Preserving mapping consistency under schema changes

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Publisher
Springer-Verlag
Copyright
Copyright © 2004 by Springer-Verlag
Subject
ComputerScience
ISSN
1066-8888
eISSN
0949-877X
D.O.I.
10.1007/s00778-004-0136-2
Publisher site
See Article on Publisher Site

Abstract

In dynamic environments like the Web, data sources may change not only their data but also their schemas, their semantics, and their query capabilities. When a mapping is left inconsistent by a schema change, it has to be detected and updated. We present a novel framework and a tool (ToMAS) for automatically adapting (rewriting) mappings as schemas evolve. Our approach considers not only local changes to a schema but also changes that may affect and transform many components of a schema. Our algorithm detects mappings affected by structural or constraint changes and generates all the rewritings that are consistent with the semantics of the changed schemas. Our approach explicitly models mapping choices made by a user and maintains these choices, whenever possible, as the schemas and mappings evolve. When there is more than one candidate rewriting, the algorithm may rank them based on how close they are to the semantics of the existing mappings.

Journal

The VLDB JournalSpringer Journals

Published: Sep 1, 2004

References

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