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Poster communications
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.
Cited literature [9 references]
https://hal.inria.fr/hal-01075489
Contributor : Rakebul Hasan Connect in order to contact the contributor
Submitted on : Thursday, October 23, 2014 - 1:25:08 PM
Last modification on : Tuesday, December 7, 2021 - 4:10:17 PM
Long-term archiving on: : Saturday, January 24, 2015 - 10:06:04 AM
Contributor : Rakebul Hasan Connect in order to contact the contributor
Submitted on : Thursday, October 23, 2014 - 1:25:08 PM
Last modification on : Tuesday, December 7, 2021 - 4:10:17 PM
Long-term archiving on: : Saturday, January 24, 2015 - 10:06:04 AM
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eswc2014-poster.pdf
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