Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/110030
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Type: Conference paper
Title: Learning-based SPARQL query performance prediction
Author: Zhang, W.
Sheng, Q.
Taylor, K.
Qin, Y.
Yao, L.
Citation: Web Information Systems Engineering, 2016 / Cellary, W., Mokbel, M., Wang, J., Wang, H., Zhou, R., Zhang, Y. (ed./s), vol.10041 LNCS, pp.313-327
Publisher: Springer
Issue Date: 2016
Series/Report no.: Lecture Notes in Computer Science
ISBN: 9783319487397
ISSN: 0302-9743
1611-3349
Conference Name: 17th International Conference on Web Information Systems Engineering (WISE) (08 Nov 2016 - 10 Nov 2016 : Shanghai, Peoples R China)
Statement of
Responsibility: 
Wei Emma Zhang, B, Quan Z. Sheng, Kerry Taylor, Yongrui Qin, and Lina Yao
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.
Keywords: SPARQL; Feature modeling; Prediction
Rights: © Springer International Publishing AG 2016
RMID: 0030059273
DOI: 10.1007/978-3-319-48740-3_23
Appears in Collections:Computer Science publications

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