Framework for parallelisation on big data

The parallelisation of big data is emerging as an important framework for large-scale parallel data applications such as seismic data processing. The field of seismic data is so large or complex that traditional data processing software is incapable of dealing with it. For example, the implementatio...

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Bibliographic Details
Published in:PloS one 2019-05, Vol.14 (5), p.e0214044-e0214044
Main Authors: Rahim, Lukman Ab, Kudiri, Krishna Mohan, Bahattacharjee, Shiladitya
Format: Article
Language:English
Subjects:
Algorithms
Big Data
Biology and Life Sciences
Cloud computing
Computational Biology - methods
Computer and Information Sciences
Connectionism
Data management
Data Mining - methods
Data processing
Engineering and Technology
Geophysics
International conferences
Methods
Parallel processing
Physical Sciences
Research and Analysis Methods
Seismic activity
Seismic studies
Seismic surveys
Seismological data
Social Sciences
Software
Stochasticity
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Summary:The parallelisation of big data is emerging as an important framework for large-scale parallel data applications such as seismic data processing. The field of seismic data is so large or complex that traditional data processing software is incapable of dealing with it. For example, the implementation of parallel processing in seismic applications to improve the processing speed is complex in nature. To overcome this issue, a simple technique which that helps provide parallel processing for big data applications such as seismic algorithms is needed. In our framework, we used the Apache Hadoop with its MapReduce function. All experiments were conducted on the RedHat CentOS platform. Finally, we studied the bottlenecks and improved the overall performance of the system for seismic algorithms (stochastic inversion).
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0214044