Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/120110
Citations
Scopus Web of Science® Altmetric
?
?
Type: Journal article
Title: Learning-based SPARQL query performance modeling and prediction
Author: Zhang, W.
Sheng, Q.
Qin, Y.
Taylor, K.
Yao, L.
Citation: World Wide Web, 2018; 21(4):1015-1035
Publisher: Springer Nature
Issue Date: 2018
ISSN: 1386-145X
1573-1413
Statement of
Responsibility: 
Wei Emma Zhang, Quan Z. Sheng, Yongrui Qin, Kerry Taylor, Lina Yao
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.
Keywords: SPARQL; feature modelling; prediction; query performance
Rights: © Springer Science+Business Media, LLC 2017
RMID: 0030119269
DOI: 10.1007/s11280-017-0498-1
Grant ID: http://purl.org/au-research/grants/arc/DP140100104
Appears in Collections:Computer Science publications

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.