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GUN: An Efficient Execution Strategy for Querying the Web of Data
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BibTeX
@MISC{Montoya_gun:an,
author = {Gabriela Montoya and Hala Skaf-molli and Pascal Molli and Maria-esther Vidal},
title = {GUN: An Efficient Execution Strategy for Querying the Web of Data},
year = {}
}
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Abstract
Abstract. Local-As-View (LAV) mediators provide a uniform interface to a federation of heterogeneous data sources to attempt the execution of queries against the federation. LAV mediators rely on query rewriters to translate mediator queries into equivalent queries on the federated data sources. The query rewriting problem in LAV mediators has shown to be NP-complete, and there may be an exponential number of rewritings, making unfeasible the execution or even generation of all the rewritings for some queries. The complexity of this problem can be particularly impacted when queries and data sources are described using SPARQL conjunctive queries, for which millions of rewritings could be generated. We aim at providing an efficient solution to the problem of executing LAV SPARQL query rewritings while the gathered answer is as complete as possible. We formulate the Result-Maximal k-Execution problem (Re-MakE) as the problem of maximizing the query results obtained from the execution of only k rewritings. Additionally, a novel query execution strategy called GUN is proposed to solve the ReMakE problem. Our ex-perimental evaluation demonstrates that GUN outperforms traditional techniques in terms of answer completeness and execution time. 1
Keyphrases
efficient execution strategy data source lav mediator result-maximal k-execution problem mediator query query result ex-perimental evaluation sparql conjunctive query uniform interface equivalent query traditional technique execution time heterogeneous data source exponential number gathered answer remake problem query rewriter efficient solution lav sparql query rewriting answer completeness novel query execution strategy
