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A comprehensive overview of RDF for spatial and spatiotemporal data management

Published online by Cambridge University Press:  22 June 2021

Fu Zhang Open the ORCID record for Fu Zhang [Opens in a new window] ,
Qingzhe Lu ,
Zhenjun Du ,
Xu Chen  and
Chunhong Cao
Fu Zhang
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang, China; e-mails: [email protected], [email protected]
Qingzhe Lu
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang, China; e-mails: [email protected], [email protected]
Zhenjun Du
Affiliation:
SIASUN Robot & Automation CO., Ltd., Shenyang, China; e-mail: [email protected]
Xu Chen
Affiliation:
North Minzu University, Yinchuan, Ningxia, China; e-mail: [email protected]
Chunhong Cao
Affiliation:
School of Computer Science and Engineering, Northeastern University, Shenyang, China; e-mails: [email protected], [email protected] SIASUN Robot & Automation CO., Ltd., Shenyang, China; e-mail: [email protected]
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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.

Type
Review
Information
The Knowledge Engineering Review , Volume 36 , 2021 , e10
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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