Predicting SPARQL Query Performance

Rakebul Hasan 1 Fabien Gandon 1
1 WIMMICS - Web-Instrumented Man-Machine Interactions, Communities and Semantics
CRISAM - Inria Sophia Antipolis - Méditerranée , Laboratoire I3S - SPARKS - Scalable and Pervasive softwARe and Knowledge Systems
Abstract : We address the problem of predicting SPARQL query performance. We use machine learning techniques to learn SPARQL query performance from previously executed queries. We show how to model SPARQL queries as feature vectors, and use k -nearest neighbors regression and Support Vector Machine with the nu-SVR kernel to accurately (R^2 value of 0.98526) predict SPARQL query execution time.
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Poster communications
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https://hal.inria.fr/hal-01075489
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Rakebul Hasan, Fabien Gandon. Predicting SPARQL Query Performance. 11th Extended Semantic Web Conference (ESWC2014), May 2014, Crete, Greece. pp.222 - 225, 2014, ⟨10.1007/978-3-319-11955-7_23⟩. ⟨hal-01075489⟩

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