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Performance Comparison of CRUD Operations in IoT based Big Data Computing

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@article{IJASEIT2674,
   author = {Jung Yeon Seo and Dae Won Lee and Hwa Min Lee},
   title = {Performance Comparison of CRUD Operations in IoT based Big Data Computing},
   journal = {International Journal on Advanced Science, Engineering and Information Technology},
   volume = {7},
   number = {5},
   year = {2017},
   pages = {1765--1770},
   keywords = {NoSQL; RDBMS, MongoDB; MySQL; big data; performance calculation.},
   abstract = {

Nowadays, due to the development of mobile devices, the kinds of data that are generated are becoming diverse, and the amount is becoming huge. The vast amount of data generated in this way is called big data. Big data must be processed in a different way than existing data processing methods. Representative methods of big data processing are RDBMS (Relational Database System) and NoSQL method. We compare NoSQL and RDBMS, which are representative database systems. In this paper, we use MySQL query and MongoDB query to compare RDBMS and NoSQL. We gradually compare the performance of CRUD operations in MySQL and MongoDB by increasing the amount of data. MongoDB sets index and compares it again.  Through result of these operations is to choose a database system that fits the situation.  This makes it possible to design and analyse big data more efficiently. 

},    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=2674},    doi = {10.18517/ijaseit.7.5.2674} }

EndNote

%A Seo, Jung Yeon
%A Lee, Dae Won
%A Lee, Hwa Min
%D 2017
%T Performance Comparison of CRUD Operations in IoT based Big Data Computing
%B 2017
%9 NoSQL; RDBMS, MongoDB; MySQL; big data; performance calculation.
%! Performance Comparison of CRUD Operations in IoT based Big Data Computing
%K NoSQL; RDBMS, MongoDB; MySQL; big data; performance calculation.
%X 

Nowadays, due to the development of mobile devices, the kinds of data that are generated are becoming diverse, and the amount is becoming huge. The vast amount of data generated in this way is called big data. Big data must be processed in a different way than existing data processing methods. Representative methods of big data processing are RDBMS (Relational Database System) and NoSQL method. We compare NoSQL and RDBMS, which are representative database systems. In this paper, we use MySQL query and MongoDB query to compare RDBMS and NoSQL. We gradually compare the performance of CRUD operations in MySQL and MongoDB by increasing the amount of data. MongoDB sets index and compares it again.  Through result of these operations is to choose a database system that fits the situation.  This makes it possible to design and analyse big data more efficiently. 

%U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2674 %R doi:10.18517/ijaseit.7.5.2674 %J International Journal on Advanced Science, Engineering and Information Technology %V 7 %N 5 %@ 2088-5334

IEEE

Jung Yeon Seo,Dae Won Lee and Hwa Min Lee,"Performance Comparison of CRUD Operations in IoT based Big Data Computing," International Journal on Advanced Science, Engineering and Information Technology, vol. 7, no. 5, pp. 1765-1770, 2017. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.7.5.2674.

RefMan/ProCite (RIS)

TY  - JOUR
AU  - Seo, Jung Yeon
AU  - Lee, Dae Won
AU  - Lee, Hwa Min
PY  - 2017
TI  - Performance Comparison of CRUD Operations in IoT based Big Data Computing
JF  - International Journal on Advanced Science, Engineering and Information Technology; Vol. 7 (2017) No. 5
Y2  - 2017
SP  - 1765
EP  - 1770
SN  - 2088-5334
PB  - INSIGHT - Indonesian Society for Knowledge and Human Development
KW  - NoSQL; RDBMS, MongoDB; MySQL; big data; performance calculation.
N2  - 

Nowadays, due to the development of mobile devices, the kinds of data that are generated are becoming diverse, and the amount is becoming huge. The vast amount of data generated in this way is called big data. Big data must be processed in a different way than existing data processing methods. Representative methods of big data processing are RDBMS (Relational Database System) and NoSQL method. We compare NoSQL and RDBMS, which are representative database systems. In this paper, we use MySQL query and MongoDB query to compare RDBMS and NoSQL. We gradually compare the performance of CRUD operations in MySQL and MongoDB by increasing the amount of data. MongoDB sets index and compares it again.  Through result of these operations is to choose a database system that fits the situation.  This makes it possible to design and analyse big data more efficiently. 

UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2674 DO - 10.18517/ijaseit.7.5.2674

RefWorks

RT Journal Article
ID 2674
A1 Seo, Jung Yeon
A1 Lee, Dae Won
A1 Lee, Hwa Min
T1 Performance Comparison of CRUD Operations in IoT based Big Data Computing
JF International Journal on Advanced Science, Engineering and Information Technology
VO 7
IS 5
YR 2017
SP 1765
OP 1770
SN 2088-5334
PB INSIGHT - Indonesian Society for Knowledge and Human Development
K1 NoSQL; RDBMS, MongoDB; MySQL; big data; performance calculation.
AB 

Nowadays, due to the development of mobile devices, the kinds of data that are generated are becoming diverse, and the amount is becoming huge. The vast amount of data generated in this way is called big data. Big data must be processed in a different way than existing data processing methods. Representative methods of big data processing are RDBMS (Relational Database System) and NoSQL method. We compare NoSQL and RDBMS, which are representative database systems. In this paper, we use MySQL query and MongoDB query to compare RDBMS and NoSQL. We gradually compare the performance of CRUD operations in MySQL and MongoDB by increasing the amount of data. MongoDB sets index and compares it again.  Through result of these operations is to choose a database system that fits the situation.  This makes it possible to design and analyse big data more efficiently. 

LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=2674 DO - 10.18517/ijaseit.7.5.2674