Automated Data Processing and Integration of Large Multiple Data Sources in Geohazards Monitoring

Authors

  • Chaoyang He College of Environment and Civil Engineering, Chengdu University of Technology, 1 Third Road East, Erxianqiao, Chengdu, Sichuan
  • Nengpan Ju College of Environment and Civil Engineering, Chengdu University of Technology, 1 Third Road East, Erxianqiao, Chengdu, Sichuan
  • Qiang Xu College of Environment and Civil Engineering, Chengdu University of Technology, 1 Third Road East, Erxianqiao, Chengdu, Sichuan
  • Jian Huang College of Environment and Civil Engineering, Chengdu University of Technology, 1 Third Road East, Erxianqiao, Chengdu, Sichuan

DOI:

https://doi.org/10.15273/ijge.2017.01.003

Keywords:

geohazard monitoring, monitoring and early warning, big data, data integration, data mining, data analyzing

Abstract

The development of geohazard information management system has greatly promoted the wireless automation monitoring technology for geohazards. More monitoring instruments are increasingly used in geohazard monitoring. Consequently, the types of monitoring data become more and more complicated, and massive amount of monitoring data are collected, which raises new demands in data storage and retrieval. In order to meet the requirements of data processing in geohazard monitoring, this paper presents a method of geohazard monitoring data processing, realizing the heterogeneous data integration, data access optimization, and abnormal data processing. Having analyzed the wireless automation monitoring process and the features of geohazard monitoring data, we defined the data integration standards of multiple data sources. Based on this, we developed a Geohazard Monitoring Data Integration System, with optimization in both hardware and software. This system allows automatic integration of large monitoring data from multiple sources. It has important significance for geohazard monitoring and early warning. A Geohazard Monitoring Data Analyzing System based on the monitoring data integrated by this system and data mining technology is developed to fully explore the hidden values of Big Data. Through field tests in Guizhou province with 92 sets of monitoring equipment and 5 types of databases, this method is proven to meet the system requirements with satisfactory performance.

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Issue

Vol. 3 No. 1-2 (2017)

Section

Articles

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