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2021 Volume 36
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A comprehensive overview of RDF for spatial and spatiotemporal data management

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  • Abstract: Currently, a large amount of spatial and spatiotemporal RDF data has been shared and exchanged on the Internet and various applications. Resource Description Framework (RDF) is widely accepted for representing and processing data in different (including spatiotemporal) application domains. The effective management of spatial and spatiotemporal RDF data are becoming more and more important. A lot of work has been done to study how to represent, query, store, and manage spatial and spatiotemporal RDF data. In order to grasp and learn the main ideas and research results of spatial and spatiotemporal RDF data, in this paper, we provide a comprehensive overview of RDF for spatial and spatiotemporal data management. We summarize spatial and spatiotemporal RDF data management from several essential aspects such as representation, querying, storage, performance assessment, datasets, and management tools. In addition, the direction of future research and some comparisons and analysis are also discussed in depth.
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  • Cite this article

    Fu Zhang, Qingzhe Lu, Zhenjun Du, Xu Chen, Chunhong Cao. 2021. A comprehensive overview of RDF for spatial and spatiotemporal data management. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000084
    Fu Zhang, Qingzhe Lu, Zhenjun Du, Xu Chen, Chunhong Cao. 2021. A comprehensive overview of RDF for spatial and spatiotemporal data management. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000084
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A comprehensive overview of RDF for spatial and spatiotemporal data management

  • Received: 27 December 2020
  • Revised: 16 May 2021
  • Accepted: 17 May 2021
  • Published online: 22 June 2021
The Knowledge Engineering Review  36,  2021, 36(1)  |  Cite this article

Abstract: Abstract: Currently, a large amount of spatial and spatiotemporal RDF data has been shared and exchanged on the Internet and various applications. Resource Description Framework (RDF) is widely accepted for representing and processing data in different (including spatiotemporal) application domains. The effective management of spatial and spatiotemporal RDF data are becoming more and more important. A lot of work has been done to study how to represent, query, store, and manage spatial and spatiotemporal RDF data. In order to grasp and learn the main ideas and research results of spatial and spatiotemporal RDF data, in this paper, we provide a comprehensive overview of RDF for spatial and spatiotemporal data management. We summarize spatial and spatiotemporal RDF data management from several essential aspects such as representation, querying, storage, performance assessment, datasets, and management tools. In addition, the direction of future research and some comparisons and analysis are also discussed in depth.

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    • The authors would like to really appreciate the hard work and the time the reviewers took for reviewing this paper. The work is supported by the National Natural Science Foundation of China (61672139), the Natural Science Foundation of Ningxia Province (No. 2020AAC03212), and Major scientific and technological innovation projects of Shandong Province (2019JZZY010128).

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    Fu Zhang, Qingzhe Lu, Zhenjun Du, Xu Chen, Chunhong Cao. 2021. A comprehensive overview of RDF for spatial and spatiotemporal data management. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000084
    Fu Zhang, Qingzhe Lu, Zhenjun Du, Xu Chen, Chunhong Cao. 2021. A comprehensive overview of RDF for spatial and spatiotemporal data management. The Knowledge Engineering Review 36(1), doi: 10.1017/S0269888921000084
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