A Comprehensive Study of Geochemical Data Storage Performance Based on Different Management Methods

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.

NoSQL Database Modeling and Management: A Systematic Literature Review

“…Another way is to follow a method to support the design, as shown in three studies. In [62], a storage method to improve the management of geochemical data is proposed; it consists of two levels, JSONification, where the data is converted using an existing library, and storage in the cloud. In [51], the authors developed a model whose architecture is made up of seven layers, which generates the scheme according to the user and system requirements, with the aim of managing data asymmetry.…”

“…Regarding the document model, five studies addressed data characteristics. In [62], the authors deal with geochemical data, which amounts increase rapidly with the improvement of test methods and precision. These are in a format called a shape file, which consists of data vectors.…”

“…Twelve studies have identified characteristics of the tools with the document model, eleven of these focused on MongoDB for various reasons. [62] Point out that it can process large amounts of data efficiently. It supports embedded document objects and arrays, has an automatic chunking mechanism, and has remarkable performance for storing structured data.…”