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A United Framework for Large-Scale Resource Description Framework Stream Processing

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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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Authors and Affiliations

  1. College of Arts and Sciences, Shanghai Polytechnic University, Shanghai, 201209, China

    Hong Fang

  2. College of Intelligence and Computing, Tianjin University, Tianjin, 300350, China

    Bo Zhao, Xiao-Wang Zhang & Xuan-Xing Yang

  3. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, 300350, China

    Bo Zhao, Xiao-Wang Zhang & Xuan-Xing Yang

Authors
  1. Hong Fang
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  2. Bo Zhao
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  3. Xiao-Wang Zhang
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  4. Xuan-Xing Yang
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Correspondence to Xiao-Wang Zhang.

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Fang, H., Zhao, B., Zhang, XW. et al. A United Framework for Large-Scale Resource Description Framework Stream Processing. J. Comput. Sci. Technol. 34, 762–774 (2019). https://doi.org/10.1007/s11390-019-1941-9

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  • DOI: https://doi.org/10.1007/s11390-019-1941-9

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