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Poster Communications Year : 2014

Predicting SPARQL Query Performance

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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Dates and versions

hal-01075489 , version 1 (23-10-2014)

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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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