@Article{2019.100000111,
AUTHOR = {Yu Zhang, Yan-Ge Wang, Yan-Ping Bai, Yong-Zhen Li, Zhao-Yong Lv, Hong-Wei Ding},
TITLE = {A New Rockburst Experiment Data Compression Storage Algorithm Based  on Big Data Technology},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {25},
YEAR = {2019},
NUMBER = {3},
PAGES = {561--572},
URL = {http://www.techscience.com/iasc/v25n3/39685},
ISSN = {2326-005X},
ABSTRACT = {Rockburst phenomenon is a kind of phenomenon that the rock is out and ejected 
because the mineral was dug out, and the original force balance was destroyed in 
the process of mineral exploitation. From 2007, GeoLab (abbreviation of State Key 
Laboratory in China for GeoMechanics and Deep Underground Engineering) had 
made a series of important achievements in rockburst. Up to now, GeoLab’s 
rockburst experiment data is reached 800T, and these data may occupy about 
2PB hard disk space after analyzed. At this ratio, GeoLab need to buy a new hard 
disk to save all these data every 46 hours rockburst experiment. Since there is 
not enough hard disk space to save all these data, GeoLab had to slow down the 
speed of do rockburst experiment and only analyzed about 4 percent of the data. 
We call this phenomenon a dilemma for data storage. This hindered the research 
process of rockburst phenomenon. We proposed a structure to obtain data from a 
cloud platform based on big data technology. And basing on this we analyzed the 
distribution characteristics of rockburst experiment data, data frequency and data 
frequency domain. And a new rockburst experiment data compression storage 
algorithm (NDCS) based on big data technology and cloud platform was 
proposed. Then we compared NDCS with WinRAR and BDSS by occupied disk 
space, compress ratio and consuming time. Theoretical analysis and experiments 
show that NDCS has the best performance of all three algorithms. NDCS is the 
most suitable data compression storage algorithm for rockburst, and it has 
successfully solved the data storage dilemma in rockburst experiment.},
DOI = {10.31209/2019.100000111}
}