Quick Compression and Transmission of Meteorological Big Data in Complicated Visualization Systems

He-Ping Yang, Ying-Rui Sun, Nan Chen, Xiao-Wei Jiang, Jing-Hua Chen, Ming Yang, Qi Wang, Zi-Mo Huo, Ming-Nong Feng and Ning Cai

Complexity, 2022, vol. 2022, 1-9

Abstract: The sizes of individual data files have steadily increased along with rising demand for customized services, leading to issues such as low efficiency of web-based geographical information system (WebGIS)-based data compression, transmission, and rendering for rich Internet applications (RIAs) in complicated visualization systems. In this article, a WebGIS-based technical solution for the efficient transmission and visualization of meteorological big data is proposed. Based on open-source technology such as HTML5 and Mapbox GL, the proposed scheme considers distributed data compression and transmission on the server side as well as distributed requests and page rendering on the browser side. A high-low 8-bit compression method is developed for compressing a 100 megabyte (MB) file into a megabyte-scale file, with a compression ratio of approximately 90%, and the recovered data are accurate to two decimal places. Another part of the scheme combines pyramid tile cutting, concurrent domain name request processing, and texture rendering. Experimental results indicate that with this scheme, grid files of up to 100 MB can be transferred and displayed in milliseconds, and multiterminal service applications can be supported by building a grid data visualization mode for big data and technology centers, which may serve as a reference for other industries.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:hin:complx:6860915

DOI: 10.1155/2022/6860915

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