Frontiers of Data and Computing ›› 2020, Vol. 2 ›› Issue (2): 20-30.doi: 10.11871/jfdc.issn.2096-742X.2020.02.002

• Special Issue: Data Analysis Technology & Application • Previous Articles     Next Articles

Big Data Driven Data Technology Analysis Frontier and Application in Resource Discipline

Wang Juanle1,*(),Cheng Kai1,2,Han Xuehua1,2,Zhang Min1,2   

  1. 1. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
    2. University of Chinese Academy of Sciences, Beijing 100049, China
  • Received:2020-01-15 Online:2020-04-20 Published:2020-06-03
  • Contact: Juanle Wang E-mail:wangjl@igsnrr.ac.cn

Abstract:

[Objective] Driven by big data and supported by information technology, it makes it possible to break through and solve the soul problem of the comprehensive study of resource science, promoting the new development and innovative application of resource science, and the innovative application of resource science. [Methods] Based on the domain demand of the resource discipline, this paper expounds the frontier of data analysis technology in the resource discipline, including remote sensing monitoring, resource surveying, resource network mining and resource comprehensive analysis, and takes the “The Big Data Driven Resource Discipline Innovation Platform” supported by 13th Five-year Informatization Plan of Chinese Academy of Sciences as an example to demonstrate its typical application architecture. [Results] Based on application cases, three big data-driven scenarios in the typical application of scientific research activities in resource discipline are presented, including ecological risk prevention of transportation and pipeline control in China-Mongolia-Russia economic corridor, assessment of the carrying capacity of resources and environment in Beijing-Tianjin-Hebei region, and assessment of the beautiful China driven by big data. [Conclusions] The data analysis technologies driven by big data in the field of resource discipline have great potential and some of them have been applied in reality. However, more new methods and models adapted to the development of resource discipline are needed to promote its paradigm shift to comprehensive scientific research.

Key words: resource discipline, big data, data driven, demonstration platform