Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/110030
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Type: | Conference paper |
Title: | Learning-based SPARQL query performance prediction |
Author: | Zhang, W. Sheng, Q. Taylor, K. Qin, Y. Yao, L. |
Citation: | Lecture Notes in Artificial Intelligence, 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) (8 Nov 2016 - 10 Nov 2016 : Shanghai, Peoples R China) |
Editor: | Cellary, W. Mokbel, M. Wang, J. Wang, H. Zhou, R. Zhang, Y. |
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 |
DOI: | 10.1007/978-3-319-48740-3_23 |
Published version: | http://dx.doi.org/10.1007/978-3-319-48740-3_23 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
RA_hdl_110030.pdf | Restricted Access | 432.57 kB | Adobe PDF | View/Open |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.