A comprehensive study of geochemical data storage performance based on different management methods
- Author
- Yinyi Cheng, Kefa Zhou, Jinlin Wang, Philippe De Maeyer (UGent) , Tim Van de Voorde (UGent) , Jining Yan and Shichao Cui
- Organization
- Abstract
- The spatial calculation of vector data is crucial for geochemical analysis in geological big data. However, large volumes of geochemical data make for inefficient management. Therefore, this study proposed a shapefile storage method based on MongoDB in GeoJSON form (SSMG) and a shapefile storage method based on PostgreSQL with open location code (OLC) geocoding (SSPOG) to solve the problem of low efficiency of electronic form management. The SSMG method consists of a JSONification tier and a cloud storage tier, while the SSPOG method consists of a geocoding tier, an extension tier, and a storage tier. Using MongoDB and PostgreSQL as databases, this study achieved two different types of high-throughput and high-efficiency methods for geochemical data storage and retrieval. Xinjiang, the largest province in China, was selected as the study area in which to test the proposed methods. Using geochemical data from shapefile as a data source, several experiments were performed to improve geochemical data storage efficiency and achieve efficient retrieval. The SSMG and SSPOG methods can be applied to improve geochemical data storage using different architectures, so as to achieve management of geochemical data organization in an efficient way, through time consumed and data compression ratio (DCR), in order to better support geological big data. The purpose of this study was to find ways to build a storage method that can improve the speed of geochemical data insertion and retrieval by using excellent big data technology to help us efficiently solve problem of geochemical data preprocessing and provide support for geochemical analysis.
- Keywords
- geochemical data, data storage, retrieval, database, BIG DATA, ANOMALIES, FOREST, MAP
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Citation
Please use this url to cite or link to this publication: http://hdl.handle.net/1854/LU-8719997
- MLA
- Cheng, Yinyi, et al. “A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods.” REMOTE SENSING, vol. 13, no. 16, 2021, doi:10.3390/rs13163208.
- APA
- Cheng, Y., Zhou, K., Wang, J., De Maeyer, P., Van de Voorde, T., Yan, J., & Cui, S. (2021). A comprehensive study of geochemical data storage performance based on different management methods. REMOTE SENSING, 13(16). https://doi.org/10.3390/rs13163208
- Chicago author-date
- Cheng, Yinyi, Kefa Zhou, Jinlin Wang, Philippe De Maeyer, Tim Van de Voorde, Jining Yan, and Shichao Cui. 2021. “A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods.” REMOTE SENSING 13 (16). https://doi.org/10.3390/rs13163208.
- Chicago author-date (all authors)
- Cheng, Yinyi, Kefa Zhou, Jinlin Wang, Philippe De Maeyer, Tim Van de Voorde, Jining Yan, and Shichao Cui. 2021. “A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods.” REMOTE SENSING 13 (16). doi:10.3390/rs13163208.
- Vancouver
- 1.Cheng Y, Zhou K, Wang J, De Maeyer P, Van de Voorde T, Yan J, et al. A comprehensive study of geochemical data storage performance based on different management methods. REMOTE SENSING. 2021;13(16).
- IEEE
- [1]Y. Cheng et al., “A comprehensive study of geochemical data storage performance based on different management methods,” REMOTE SENSING, vol. 13, no. 16, 2021.
@article{8719997,
abstract = {{The spatial calculation of vector data is crucial for geochemical analysis in geological big
data. However, large volumes of geochemical data make for inefficient management. Therefore, this
study proposed a shapefile storage method based on MongoDB in GeoJSON form (SSMG) and a
shapefile storage method based on PostgreSQL with open location code (OLC) geocoding (SSPOG)
to solve the problem of low efficiency of electronic form management. The SSMG method consists
of a JSONification tier and a cloud storage tier, while the SSPOG method consists of a geocoding
tier, an extension tier, and a storage tier. Using MongoDB and PostgreSQL as databases, this study
achieved two different types of high-throughput and high-efficiency methods for geochemical data
storage and retrieval. Xinjiang, the largest province in China, was selected as the study area in
which to test the proposed methods. Using geochemical data from shapefile as a data source, several
experiments were performed to improve geochemical data storage efficiency and achieve efficient
retrieval. The SSMG and SSPOG methods can be applied to improve geochemical data storage
using different architectures, so as to achieve management of geochemical data organization in an
efficient way, through time consumed and data compression ratio (DCR), in order to better support
geological big data. The purpose of this study was to find ways to build a storage method that can
improve the speed of geochemical data insertion and retrieval by using excellent big data technology
to help us efficiently solve problem of geochemical data preprocessing and provide support for
geochemical analysis.}},
articleno = {{3208}},
author = {{Cheng, Yinyi and Zhou, Kefa and Wang, Jinlin and De Maeyer, Philippe and Van de Voorde, Tim and Yan, Jining and Cui, Shichao}},
issn = {{2072-4292}},
journal = {{REMOTE SENSING}},
keywords = {{geochemical data,data storage,retrieval,database,BIG DATA,ANOMALIES,FOREST,MAP}},
language = {{eng}},
number = {{16}},
pages = {{15}},
title = {{A comprehensive study of geochemical data storage performance based on different management methods}},
url = {{http://doi.org/10.3390/rs13163208}},
volume = {{13}},
year = {{2021}},
}
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