Volume 42 Issue 2
Feb.  2017
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LI Shaojun, YANG Haijun, HUANG Yaohuan, ZHOU Qin. Geo-spatial Big Data Storage Based on NoSQL Database[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2): 163-169. DOI: 10.13203/j.whugis20140774
Citation: LI Shaojun, YANG Haijun, HUANG Yaohuan, ZHOU Qin. Geo-spatial Big Data Storage Based on NoSQL Database[J]. Geomatics and Information Science of Wuhan University, 2017, 42(2): 163-169. DOI: 10.13203/j.whugis20140774
shu

Geo-spatial Big Data Storage Based on NoSQL Database

  • 1.

    Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China

  • 2.

    Satellite Environment Center, Ministry of Environmental Protection, Beijing 100094, China

  • 3.

    Beijing Supermap GIS Technologies Company, Beijing 100015, China

Funds: 

The National Natural Science Foundation of China No. 51309210

More Information
  • Author Bio:

    LI Shaojun, PhD, specializes in geographic information software. E-mail:lishaojun@supermap.com

  • Corresponding author:

    YANG Haijun, PhD, senior engineer. E-mail:yanghj@lreis.ac.cn

  • Received Date: April 16, 2015
  • Published Date: February 04, 2017
    Graphical Abstract
    • Abstract
  • Geospatial data in databases have shifted to conform to the characteristics of big-data in tandem with the development of the Internet, mobile Internet, cloud computing, and especially, spatial data acquisition technologies. Faced with tackling spatial big data, traditional spatial database management techniques based on Relational Database Management Systems have encountered problems including the unstructured characteristics of the spatial object, the high scalability of storage capacity, and the high concurrency in big data application environment. This paper focuses on the mainstream of NoSQL databases that successfully deal with unstructured big data and are widely used in Internet applications, but lack of spatial characteristics. The data operational and query modes cannot meet the requirments of GIS applications. To resolve this problem, this paper proposes a strategy that takes a NoSQL database as a warehouse for spatial big data and a traditional spatial database as the application server. The storage system architecture and the key technology and solutions are discussed. A prototype system was developed based on MongoDB, PostgreSQL and SQLite to verify the feasibility and effectiveness of the strategy.
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    • References (15)
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