A Machine Learning Approach to SPARQL Query Performance Prediction
| Autor: | Rakebul Hasan, Fabien Gandon |
|---|---|
| Přispěvatelé: | Web-Instrumented Man-Machine Interactions, Communities and Semantics (WIMMICS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), ANR-10-CORD-0021,Kolflow,Collaboration homme-machine dans des processus continus de construction de connaissances(2010), Université Nice Sophia Antipolis (1965 - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (1965 - 2019) (UNS) |
| Jazyk: | angličtina |
| Rok vydání: | 2014 |
| Předmět: |
Computer science
Feature vector InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL 02 engineering and technology computer.software_genre Machine learning Named graph 020204 information systems 0202 electrical engineering electronic engineering information engineering SPARQL RDF computer.programming_language Information retrieval business.industry [INFO.INFO-WB]Computer Science [cs]/Web InformationSystems_DATABASEMANAGEMENT computer.file_format Linked data Support vector machine Kernel (statistics) 020201 artificial intelligence & image processing Data mining Artificial intelligence business computer RDF query language |
| Zdroj: | The 2014 IEEE/WIC/ACM International Conference on Web Intelligence The 2014 IEEE/WIC/ACM International Conference on Web Intelligence, Aug 2014, Warsaw, Poland WI-IAT (2) HAL |
| Popis: | International audience; In this paper we address the problem of predicting SPARQL query performance. We use machine learning techniques to learn SPARQL query performance from previously executed queries. Traditional approaches for estimating SPARQL query cost are based on statistics about the underlying data. However, in many use-cases involving querying Linked Data, statistics about the underlying data are often missing. Our approach does not require any statistics about the underlying RDF data, which makes it ideal for the Linked Data scenario. 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 predict SPARQL query execution time. |
| Databáze: | OpenAIRE |
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