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 contributionpeer-review

    4 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 - 17th International Conference, Proceedings
    EditorsWojciech Cellary, Jianmin Wang, Mohamed F. Mokbel, Hua Wang, Rui Zhou, Yanchun Zhang
    PublisherSpringer Verlag
    Number of pages15
    ISBN (Print)9783319487397
    Publication statusPublished - 2016
    Event17th International Conference on Web Information Systems Engineering, WISE 2016 - Shanghai, China
    Duration: 8 Nov 201610 Nov 2016

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume10041 LNCS
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349


    Conference17th International Conference on Web Information Systems Engineering, WISE 2016


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