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Learning-based SPARQL query performance modeling and prediction

Zhang, Wei Emma, Sheng, Quan Z., Qin, Yongrui, Taylor, Kerry and Yao, Lina (2017) Learning-based SPARQL query performance modeling and prediction. World Wide Web Journal. ISSN 1386-145X

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Abstract

One of the challenges of managing an RDF database is predicting performance of SPARQL queries before they are executed. Performance characteristics, such as the execution time and memory usage, can help data consumers identify unexpected long-running queries before they start and estimate the system workload for query scheduling. Extensive works address such performance prediction problem in traditional SQL queries but they are not directly applicable to SPARQL queries. In this paper, we adopt machine learning techniques to predict the performance of SPARQL queries. Our work focuses on modeling features of a SPARQL query to a vector representation. Our feature modeling method does not depend on the knowledge of underlying systems and the structure of the underlying data, but only on the nature of SPARQL queries. Then we use these features to train prediction models. We propose a two-step prediction process and consider performances in both cold and warm stages. Evaluations are performed on real world SPRAQL queries, whose execution time ranges from milliseconds to hours. The results demonstrate that the proposed approach can effectively predict SPARQL query performance and outperforms state-of-the-art approaches.

Item Type: Article
Additional Information: This is a post-peer-review, pre-copyedit version of an article published in World Wide Web. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11280-017-0498-1
Uncontrolled Keywords: SPARQL, Feature modeling, Prediction, Query performance
Subjects: Q Science > QA Mathematics > QA76 Computer software
Schools: School of Computing and Engineering
Related URLs:
Depositing User: Sally Hughes
Date Deposited: 10 Jan 2018 16:27
Last Modified: 26 Mar 2018 15:30
URI: http://eprints.hud.ac.uk/id/eprint/34182

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