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Learning-based SPARQL Query Performance Prediction

Zhang, Wei Emma, Sheng, Quan Z., Taylor, Kerry, Qin, Yongrui and Yao, Lina (2016) Learning-based SPARQL Query Performance Prediction. In: The 17th International conference on Web Information Systems Engineering (WISE), November 7 - 10, 2016, Shanghai, China.

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Abstract

According to the predictive results of query performance, queries can be rewritten to reduce time cost or rescheduled to the time when the resource is not in contention. As more large RDF datasets appear on the Web recently, predicting performance of SPARQL query processing is one major challenge in managing a large RDF dataset efficiently. In this paper, we focus on representing SPARQL queries with feature vectors and using these feature vectors to train predictive models that are used to predict the performance of SPARQL queries. The evaluations performed on real world SPARQL queries demonstrate that the proposed approach can effectively predict SPARQL query performance and outperforms state-of-the-art approaches.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
School of Computing and Engineering > High-Performance Intelligent Computing > Planning, Autonomy and Representation of Knowledge
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Depositing User: Yongrui Qin
Date Deposited: 08 Nov 2016 10:18
Last Modified: 20 Jul 2017 21:07
URI: http://eprints.hud.ac.uk/id/eprint/29767

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