Learning-Based SPARQL Query Performance Prediction

Wei Emma Zhang; Quan Z. Sheng; Kerry Taylor; Yongrui Qin; Lina Yao

doi:10.1007/978-3-319-48740-3_23

Learning-Based SPARQL Query Performance Prediction

Wei Emma Zhang, Quan Z. Sheng, Kerry Taylor, Yongrui Qin, Lina Yao

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

4 Citations (Scopus)

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.

Original language	English
Title of host publication	Web Information Systems Engineering – WISE 2016
Subtitle of host publication	17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I
Editors	Wojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang
Publisher	Springer Verlag
Pages	313-327
Number of pages	15
Volume	10041 LNCS
ISBN (Electronic)	9783319487403
ISBN (Print)	9783319487397, 3319487396
DOIs	https://doi.org/10.1007/978-3-319-48740-3_23
Publication status	Published - 2 Nov 2016
Event	17th International Conference on Web Information Systems Engineering - Shanghai, China Duration: 8 Nov 2016 → 10 Nov 2016 http://www.wise-conferences.org/2016/ (Link to Conference Website )

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10041 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Web Information Systems Engineering
Abbreviated title	WISE 2016
Country/Territory	China
City	Shanghai
Period	8/11/16 → 10/11/16
Internet address	http://www.wise-conferences.org/2016/ (Link to Conference Website )

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10.1007/978-3-319-48740-3_23Licence: Other

http://eprints.hud.ac.uk/id/eprint/29767/Licence: Unspecified

Cite this

Zhang, W. E., Sheng, Q. Z., Taylor, K., Qin, Y., & Yao, L. (2016). Learning-Based SPARQL Query Performance Prediction. In W. Cellary, M. F. Mokbel, J. Wang, H. Wang, R. Zhou, & Y. Zhang (Eds.), Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I (Vol. 10041 LNCS, pp. 313-327). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10041 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-48740-3_23

Zhang, Wei Emma ; Sheng, Quan Z. ; Taylor, Kerry et al. / Learning-Based SPARQL Query Performance Prediction. Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I . editor / Wojciech Cellary ; Mohamed F. Mokbel ; Jianmin Wang ; Hua Wang ; Rui Zhou ; Yanchun Zhang. Vol. 10041 LNCS Springer Verlag, 2016. pp. 313-327 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{42180814d3f64c30bf093431251044bc,

title = "Learning-Based SPARQL Query Performance Prediction",

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 = "Feature modeling, Prediction, SPARQL",

author = "Zhang, {Wei Emma} and Sheng, {Quan Z.} and Kerry Taylor and Yongrui Qin and Lina Yao",

year = "2016",

month = nov,

day = "2",

doi = "10.1007/978-3-319-48740-3_23",

language = "English",

isbn = "9783319487397",

volume = "10041 LNCS",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Verlag",

pages = "313--327",

editor = "Wojciech Cellary and Mokbel, {Mohamed F.} and Jianmin Wang and Hua Wang and Rui Zhou and Yanchun Zhang",

booktitle = "Web Information Systems Engineering – WISE 2016",

address = "Germany",

note = "17th International Conference on Web Information Systems Engineering, WISE 2016 ; Conference date: 08-11-2016 Through 10-11-2016",

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}

Zhang, WE, Sheng, QZ, Taylor, K, Qin, Y & Yao, L 2016, Learning-Based SPARQL Query Performance Prediction. in W Cellary, MF Mokbel, J Wang, H Wang, R Zhou & Y Zhang (eds), Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I . vol. 10041 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10041 LNCS, Springer Verlag, pp. 313-327, 17th International Conference on Web Information Systems Engineering, Shanghai, China, 8/11/16. https://doi.org/10.1007/978-3-319-48740-3_23

Learning-Based SPARQL Query Performance Prediction. / Zhang, Wei Emma; Sheng, Quan Z.; Taylor, Kerry et al.

Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I . ed. / Wojciech Cellary; Mohamed F. Mokbel; Jianmin Wang; Hua Wang; Rui Zhou; Yanchun Zhang. Vol. 10041 LNCS Springer Verlag, 2016. p. 313-327 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10041 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Learning-Based SPARQL Query Performance Prediction

AU - Zhang, Wei Emma

AU - Sheng, Quan Z.

AU - Taylor, Kerry

AU - Qin, Yongrui

AU - Yao, Lina

PY - 2016/11/2

Y1 - 2016/11/2

N2 - 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.

AB - 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.

KW - Feature modeling

KW - Prediction

KW - SPARQL

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DO - 10.1007/978-3-319-48740-3_23

M3 - Conference contribution

AN - SCOPUS:84996590384

SN - 9783319487397

SN - 3319487396

VL - 10041 LNCS

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 313

EP - 327

BT - Web Information Systems Engineering – WISE 2016

A2 - Cellary, Wojciech

A2 - Mokbel, Mohamed F.

A2 - Wang, Jianmin

A2 - Wang, Hua

A2 - Zhou, Rui

A2 - Zhang, Yanchun

PB - Springer Verlag

T2 - 17th International Conference on Web Information Systems Engineering

Y2 - 8 November 2016 through 10 November 2016

ER -

Zhang WE, Sheng QZ, Taylor K, Qin Y, Yao L. Learning-Based SPARQL Query Performance Prediction. In Cellary W, Mokbel MF, Wang J, Wang H, Zhou R, Zhang Y, editors, Web Information Systems Engineering – WISE 2016: 17th International Conference, Shanghai, China, November 8-10, 2016, Proceedings, Part I . Vol. 10041 LNCS. Springer Verlag. 2016. p. 313-327. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-48740-3_23

Learning-Based SPARQL Query Performance Prediction

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