Learning-based SPARQL query performance prediction

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

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)


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 languageEnglish
Title of host publicationWeb information systems engineering – WISE 2016
Subtitle of host publication17th International Conference, Shanghai, China, November 8–10, 2016, Proceedings, Part I
EditorsWojciech Cellary, Mohamed F. Mokbel, Jianmin Wang, Hua Wang, Rui Zhou, Yanchun Zhang
Place of PublicationCham
PublisherSpringer, Springer Nature
Number of pages15
ISBN (Electronic)9783319487403
ISBN (Print)9783319487397
Publication statusPublished - 2016
Externally publishedYes
Event17th International Conference on Web Information Systems Engineering, WISE 2016 - Shanghai, China, Shanghai
Duration: 8 Nov 201610 Nov 2016

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference17th International Conference on Web Information Systems Engineering, WISE 2016


  • Feature modeling
  • Prediction

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