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 (R2 value of 0.98526) predict SPARQL query execution time.
Cite
CITATION STYLE
Hasan, R., & Gandon, F. (2014). Predicting SPARQL query performance. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8798, pp. 222–225). Springer Verlag. https://doi.org/10.1007/978-3-319-11955-7_23
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