Learning-based SPARQL query performance modeling and prediction

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

Research output: Contribution to journalArticle

6 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)1015-1035
Number of pages21
JournalWorld Wide Web
Volume21
Issue number4
Early online date24 Oct 2017
DOIs
Publication statusPublished - Jul 2018

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