A comprehensive overview of RDF for spatial and spatiotemporal data management

被引：3
|
作者：
Zhang, Fu [1 ]
Lu, Qingzhe [1 ]
Du, Zhenjun [2 ]
Chen, Xu [3 ]
Cao, Chunhong [1 ,2 ]
机构：
[1] Northeastern Univ, Sch Comp Sci & Engn, Shenyang, Peoples R China
[2] SIASUN Robot & Automat CO Ltd, Shenyang, Peoples R China
[3] North Minzu Univ, Yinchuan, Ningxia, Peoples R China
来源：
KNOWLEDGE ENGINEERING REVIEW | 2021年 / 36卷
基金：
中国国家自然科学基金;
关键词：
NEXT-GENERATION; KNOWLEDGE-BASE; SEMANTIC WEB; TIME;
D O I：
10.1017/S0269888921000084
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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.
引用
收藏
页数：41
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