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Scalable query rewriting: a graph-based approach

Scalable query rewriting: a graph-based approach
Scalable query rewriting: a graph-based approach
In this paper we consider the problem of answering queries using views, which is important for data integration, query optimization, and data warehouses. We consider its simplest form, conjunctive queries and views, which already is NP-complete. Our context is data integration, so we search for maximally-contained rewritings. By looking at the problem from a graph perspective we are able to gain a better insight and develop an algorithm which compactly represents common patterns in the source descriptions, and (optionally) pushes some computation offline. This together with other optimizations result in an experimental performance about two orders of magnitude faster than current state-of-the-art algorithms, rewriting queries using over 10000 views within seconds.
97-108
Konstantinidis, George
f174fb99-8434-4485-a7e4-bee0fef39b42
Ambite, Jose Luis
0a7ecac4-d15d-47c9-ac49-c06d8525f9d7
Konstantinidis, George
f174fb99-8434-4485-a7e4-bee0fef39b42
Ambite, Jose Luis
0a7ecac4-d15d-47c9-ac49-c06d8525f9d7

Konstantinidis, George and Ambite, Jose Luis (2011) Scalable query rewriting: a graph-based approach. In Proceedings of the 2011 international conference on Management of data - SIGMOD '11. pp. 97-108 . (doi:10.1145/1989323.1989335).

Record type: Conference or Workshop Item (Paper)

Abstract

In this paper we consider the problem of answering queries using views, which is important for data integration, query optimization, and data warehouses. We consider its simplest form, conjunctive queries and views, which already is NP-complete. Our context is data integration, so we search for maximally-contained rewritings. By looking at the problem from a graph perspective we are able to gain a better insight and develop an algorithm which compactly represents common patterns in the source descriptions, and (optionally) pushes some computation offline. This together with other optimizations result in an experimental performance about two orders of magnitude faster than current state-of-the-art algorithms, rewriting queries using over 10000 views within seconds.

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Published date: 12 June 2011

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Local EPrints ID: 504279
URI: http://eprints.soton.ac.uk/id/eprint/504279
PURE UUID: f087f492-b228-4119-bed7-0d84f53c9baf

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Date deposited: 02 Sep 2025 17:08
Last modified: 02 Sep 2025 17:08

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Author: George Konstantinidis
Author: Jose Luis Ambite

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