Framework for parallelisation on big data

Rahim, L.A. and Kudiri, K.M. and Bahattacharjee, S. (2019) Framework for parallelisation on big data. PLoS ONE, 14 (5). ISSN 19326203

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Abstract

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). © 2019 Rahim et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Item Type: Article
Additional Information: cited By 6
Uncontrolled Keywords: article; big data; stochastic model; algorithm; biology; data mining; procedures; software, Algorithms; Big Data; Computational Biology; Data Mining; Software
Depositing User: Mr Ahmad Suhairi UTP
Date Deposited: 10 Nov 2023 03:26
Last Modified: 10 Nov 2023 03:26
URI: https://khub.utp.edu.my/scholars/id/eprint/11606

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