Efficient Stratified Sampling Graphing Method for Mass Data

Authors

  • Jianjun Wang Lanzhou Geophysical National Observation and Research Station, Lanzhou; Gansu Provincial Earthquake Administration, Lanzhou https://orcid.org/0000-0002-5644-7747
  • Yingang Zhao Anqiu Earthquake Station, Weifang
  • Jun Chen Anhui Earthquake Agency, Hefei
  • Suqing Zhang Institute of Geophysics, China Earthquake Administration, Beijing
  • Xudong Zhao Institute of Geophysics, China Earthquake Administration, Beijing
  • Yufei He Institute of Geophysics, China Earthquake Administration, Beijing

DOI:

https://doi.org/10.5334/dsj-2019-056

Keywords:

mass data, polyline graphing, sampling graphing, stratified sampling, image similarity

Abstract

Sequentially linking data during polyline graphing of mass data (millions of points or more) generally results in poor graphing efficiency. Numerous curves are buried behind each pixel and cannot be displayed due to resolution limits of the width of the X-axis. Herein, a new efficient stratified sampling graphing method is proposed. The test results demonstrated that: (1) The full dataset is divided into 2X subsets, where X is the width of the X-axis in pixels, and the maximum and minimum values of the data in each subset are respectively calculated and linked in order of appearance. This method yields 4X sampled data graphs that are highly consistent with the full dataset graphs. (2) When the dataset is divided into 2X, 4X, 6X, 8X, or more subsets (progressively increasing by even multiples), the similarity gradually increases. The average similarities can reach approximately 99.24% and 99.93% in the 2X and 50X subsets, respectively. We think that 2X is the optimal subset allocation, which can achieve a high similarity, but also achieve the fastest sampling speed. (3) Compared with the speed of full dataset graphing, the overall speed of the “single-thread sampling + graphing” is increased by approximately 70 times, and that of the “threadPool sampling + graphing” was enhanced by approximately 200 times. The method employs the minimum amount of sampled data to obtain the full dataset graph that users expect to see, thereby significantly improving graphing speeds of mass data.

Author Biography

Jianjun Wang, Lanzhou Geophysical National Observation and Research Station, Lanzhou; Gansu Provincial Earthquake Administration, Lanzhou

Jianjun Wang, male, master, senior engineer, born in December 1975, Master's supervisor. Graduated from Lanzhou Institute of Seismology(LIS) in 2008, received a Master's degree in solid Geophysics, He is now in the West Strong Earthquake Prediction Laboratory of LIS. He has been a Master tutor since 2017. The research direction is seismic electromagnetic observation and research, database software development, mainly engaged in geomagnetic daily variation, geomagnetic K-index, seismic precursory database software research and development and so on. He presided over 1 Spark Program of China Earthquake Administration(CEA), 1 item of basic Scientific Research Project of LIS, 2 special items of monitoring and prediction of CEA, 1 item of participating in the National Natural Science Foundation of China; More than 10 SCI and core papers was published in China and abroad. He has won the second prize of excellent achievements in CEA and 4 awards of excellent achievements in LIS.

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Published

2019-11-13

Issue

Vol. 18 (2019)

Section

Research Papers

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