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Optimizing Parallelism of Big Data Analytics at Distributed Computing System

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@article{IJASEIT2676,
   author = {Rohyoung Myung and Heonchang Yu and Daewon Lee},
   title = {Optimizing Parallelism of Big Data Analytics at Distributed Computing System},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {5},
   year = {2017},
   pages = {1716--1721},
   keywords = {big data analytics; distributed computing system; distributed processing framework; parallel programming model},
   abstract = {Since advent of information revolution, there have been a lot of interest at big data analytics as well as big data. In the big data analytics, it is essential that not only extracting valuable information from the big data but also processing the data rapidly. Therefore, the distributed computing systems which process the analytics concurrently with parallel programming model based distributed processing framework as well as provide data analytics related libraries get attention of researchers. Several big data analytics programming models are studied that implemented for processing and generating huge data sets. However, developing the big data analytics in the distributed computing systems with utilizing parallel processing framework needs expertise in each area. In this paper, we demonstrate there is huge gap among usages of processing units if the big data analytics are naively executed at the distributed system. And we also prove that applying proper parallelism of those methods results in 1.5 to 3.3 times improvement of execution time compared to default parallelism.},
   issn = {2088-5334},
   publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development},
   url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2676},
   doi = {10.18517/ijaseit.7.5.2676}
}

EndNote

%A Myung, Rohyoung
%A Yu, Heonchang
%A Lee, Daewon
%D 2017
%T Optimizing Parallelism of Big Data Analytics at Distributed Computing System
%B 2017
%9 big data analytics; distributed computing system; distributed processing framework; parallel programming model
%! Optimizing Parallelism of Big Data Analytics at Distributed Computing System
%K big data analytics; distributed computing system; distributed processing framework; parallel programming model
%X Since advent of information revolution, there have been a lot of interest at big data analytics as well as big data. In the big data analytics, it is essential that not only extracting valuable information from the big data but also processing the data rapidly. Therefore, the distributed computing systems which process the analytics concurrently with parallel programming model based distributed processing framework as well as provide data analytics related libraries get attention of researchers. Several big data analytics programming models are studied that implemented for processing and generating huge data sets. However, developing the big data analytics in the distributed computing systems with utilizing parallel processing framework needs expertise in each area. In this paper, we demonstrate there is huge gap among usages of processing units if the big data analytics are naively executed at the distributed system. And we also prove that applying proper parallelism of those methods results in 1.5 to 3.3 times improvement of execution time compared to default parallelism.
%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2676
%R doi:10.18517/ijaseit.7.5.2676
%J International Journal on Advanced Science, Engineering and Information Technology
%V 7
%N 5
%@ 2088-5334

IEEE

Rohyoung Myung,Heonchang Yu and Daewon Lee,"Optimizing Parallelism of Big Data Analytics at Distributed Computing System," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1716-1721, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.5.2676.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Myung, Rohyoung
AU  - Yu, Heonchang
AU  - Lee, Daewon
PY  - 2017
TI  - Optimizing Parallelism of Big Data Analytics at Distributed Computing System
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 5
Y2  - 2017
SP  - 1716
EP  - 1721
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - big data analytics; distributed computing system; distributed processing framework; parallel programming model
N2  - Since advent of information revolution, there have been a lot of interest at big data analytics as well as big data. In the big data analytics, it is essential that not only extracting valuable information from the big data but also processing the data rapidly. Therefore, the distributed computing systems which process the analytics concurrently with parallel programming model based distributed processing framework as well as provide data analytics related libraries get attention of researchers. Several big data analytics programming models are studied that implemented for processing and generating huge data sets. However, developing the big data analytics in the distributed computing systems with utilizing parallel processing framework needs expertise in each area. In this paper, we demonstrate there is huge gap among usages of processing units if the big data analytics are naively executed at the distributed system. And we also prove that applying proper parallelism of those methods results in 1.5 to 3.3 times improvement of execution time compared to default parallelism.
UR  - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2676
DO  - 10.18517/ijaseit.7.5.2676

RefWorks

RT Journal Article
ID 2676
A1 Myung, Rohyoung
A1 Yu, Heonchang
A1 Lee, Daewon
T1 Optimizing Parallelism of Big Data Analytics at Distributed Computing System
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 5
YR 2017
SP 1716
OP 1721
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 big data analytics; distributed computing system; distributed processing framework; parallel programming model
AB Since advent of information revolution, there have been a lot of interest at big data analytics as well as big data. In the big data analytics, it is essential that not only extracting valuable information from the big data but also processing the data rapidly. Therefore, the distributed computing systems which process the analytics concurrently with parallel programming model based distributed processing framework as well as provide data analytics related libraries get attention of researchers. Several big data analytics programming models are studied that implemented for processing and generating huge data sets. However, developing the big data analytics in the distributed computing systems with utilizing parallel processing framework needs expertise in each area. In this paper, we demonstrate there is huge gap among usages of processing units if the big data analytics are naively executed at the distributed system. And we also prove that applying proper parallelism of those methods results in 1.5 to 3.3 times improvement of execution time compared to default parallelism.
LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2676
DO  - 10.18517/ijaseit.7.5.2676