Supervised by: Ministry of Culture of PRC

Sponsored by:National Library of China
  Library Society of China

ISSN 1001-8867    CN 11-2746/G2

gst-store: Querying Large Spatiotemporal RDF Graphs

Abstract

The Simple Protocol and RDF Query Language (SPARQL) query language allows users to issue a structural query over a resource description framework (RDF) graph. However, the lack of a spatiotemporal query language limits the usage of RDF data in spatiotemporal-oriented applications. As the spatiotemporal information continuously increases in RDF data, it is necessary to design an effective and efficient spatiotemporal RDF data management system. In this paper, we formally define the spatiotemporal information-integrated RDF data, introduce a spatiotemporal query language that extends the SPARQL language with spatiotemporal assertions to query spatiotemporal information-integrated RDF data, and design a novel index and the corresponding query algorithm. The experimental results on a large, real RDF graph integrating spatial and temporal information (> 180 million triples) confirm the superiority of our approach. In contrast to its competitors, gst-store outperforms by more than 20%-30% in most cases.

Keywords: spatiotemporal query;RDF graph;tree index

DOI:https://doi.org/10.1515/dim-2017-0008

Received October 19, 2017; accepted November 19, 2017

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