Processing and Analysis of Large-Scale Seismic Signal in Hadoop Platform
Journal Title: Advances in Robotics & Mechanical Engineering - Year 2018, Vol 1, Issue 1
Abstract
Through the usage of fifteen noteworthy ventures by the International Seismological Bureau, world has fabricated a seismic observing system, which makes all local and global seismic information that can be observed to published on a week after week for the client download. Given the immense measure of data on this information, Hadoop stage has possessed the capacity to oversee and capacity productively, and to break down more significant data. It has received appropriated storage to enhance the literacy rate and grow the capacity limit, also it has utilized MapReduce to coordinate the information in the HDFS (Hadoop Distributed File System) to guarantee that they are broke down and prepared rapidly. In the interim, it likewise has utilized excess information stockpiling to guarantee information security, in this way making it an instrument for taking care of extensive information. Seismic data are the data extracted from the digital readings of seismic waves. Seismic waves are similar to the recorded echoes what we make on the top of rigged cliff. The only difference is that these seismic waves propagate downwards. In our modern society, information increases in high speed and a large amount of data resides on cloud platform. Over 1/3rd of total digital data are produced yearly which needs to be processed and analyzed. Hugelive digital data like seismic data, where even a small amount of information impacts greatly to human life has to be analyzed and processed to obtain more valuable information [1]. Thus, Hadoop ecological system comes into picture, which is easy to develop & process applications of mass data, has high fault tolerance nature, being developed on java platform and an open source, and ensures deployment of system [2].
Authors and Affiliations
Shristi Bharti, Priyanka S, Chinmay Bhushan, Shahrukh Javed
Keywords
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- EP ID EP578758
- DOI 10.32474/ARME.2018.01.000103
- Views 100
- Downloads 0
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