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Volume 34 Issue 4
July  2019
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Hong Fang, Bo Zhao, Xiao-Wang Zhang, Xuan-Xing Yang. A United Framework for Large-Scale Resource Description Framework Stream Processing[J]. Journal of Computer Science and Technology, 2019, 34(4): 762-774. DOI: 10.1007/s11390-019-1941-9
Citation: Hong Fang, Bo Zhao, Xiao-Wang Zhang, Xuan-Xing Yang. A United Framework for Large-Scale Resource Description Framework Stream Processing[J]. Journal of Computer Science and Technology, 2019, 34(4): 762-774. DOI: 10.1007/s11390-019-1941-9
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A United Framework for Large-Scale Resource Description Framework Stream Processing

  • 1 College of Arts and Sciences, Shanghai Polytechnic University, Shanghai 201209, China;
    2 College of Intelligence and Computing, Tianjin University, Tianjin 300350, China;
    3 Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin 300350, China

Funds: This paper is supported by the National Key Research and Development Program of China under Grant No. 2017YFC0908401, and the National Natural Science Foundation of China under Grant No. 61672377. Xiao-Wang Zhang is supported by the program of Peiyang Young Scholars of China under Grant No. 2019XRX-0032.
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  • Corresponding author:

    Xiao-Wang Zhang E-mail: xiaowangzhang@tju.edu.cn

  • Received Date: January 14, 2019
  • Revised Date: May 08, 2019
  • Published Date: July 10, 2019
    Abstract
  • Resource description framework (RDF) stream is useful to model spatio-temporal data. In this paper, we propose a framework for large-scale RDF stream processing, LRSP, to process general continuous queries over large-scale RDF streams. Firstly, we propose a formalization (named CT-SPARQL) to represent the general continuous queries in a unified, unambiguous way. Secondly, based on our formalization we propose LRSP to process continuous queries in a common white-box way by separating RDF stream processing, query parsing, and query execution. Finally, we implement and evaluate LRSP with those popular continuous query engines on some benchmark datasets and real-world datasets. Due to the architecture of LRSP, many efficient query engines (including centralized and distributed engines) for RDF can be directly employed to process continuous queries. The experimental results show that LRSP has a higher performance, specially, in processing large-scale real-world data.
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