Data Mining and Knowledge Engineering

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Data Performance Evaluation Using RDBMS and non-RDBMS


Affiliations
1 Department of CSE, AMC Engineering College, Bengaluru., India
2 Department of CSE, AMC Engineering College, Bengaluru., India
3 Department of CSE, AMC Engineering College, Bengaluru, India
     

   Subscribe/Renew Journal


The Internet of Things (IoT) initiates a challenge for the Database Management System (DBMS) in estimating how to store and handle very excess amount of heterogeneous data. DBMS is classified as two types: Relational DBMS and Non-Relational DBMS. This paper seeks to provide an estimation of two open-source DBMSs: MySQL as one of the Relational DBMS and MongoDB as one of the Non-Relational DBMS. The contrast is depended on estimating the performance of inserting and restoring the excess amount of data and assessing the performance of both types of databases with different stipulations in the cloud computing. This paper introduces two different models which are used for prediction and finds the difference between them to assess the latency in which that data responds efficiently and the database size. Thus the models of prediction help to choose the suitable database to store and maintain the data. The result shows that the MongoDB is more and more efficient than that of the MySQL database. In which MongoDB is able to save or store resources and data better and efficient than that of the MySQL.

Keywords

IoT, DBMS, SQL, MySQL, NoSQL, MongoDB, AWS, Cloud, Multiple Non-Linear Regressions
User
Subscription Login to verify subscription
Notifications
Font Size

  • C. Gyorödi, R. Gyorödi, and R. Sotoc, „„A comparative study of relational and non-relational database models in a Web-based application,‟‟ Int.J. Adv. Comput. Sci. Appl., vol. 6, no. 11, pp. 78–83, 2015.

  • C. Li and J. Gu, „„An integration approach of hybrid databases based on SQL in cloud computing environment,‟‟ Softw., Pract. Exper., vol. 49, no. 3, pp. 401–422, Mar. 2019

  • L. Kumar, S. Rajawat, and K. Joshi, „„Comparative analysis of NoSQL (MongoDB) with MySQL database,‟‟ Int. J. Modern Trends Eng. Res., vol. 2, no. 5, pp. 120–127, May 2015.

  • Z. Bicevska and I. Oditis, „„Towards NoSQL-based data warehouse solu- tions,‟‟ Procedia Comput. Sci., vol. 104, pp. 104–111, Jan. 2017.

  • Y. Rasheed, M. Qutqut, and F. Almasalha, „„Overview of the current status of NoSQL database,‟‟ Int. J. Comput. Sci. Netw. Secur., vol. 19, no. 4, pp. 47–53, Apr. 2019.

  • C. Asiminidis, G. Kokkonis, and S. Kontogiannis, „„Database systems performance evaluation for IoT applications,‟‟ Int. J. Database Manage. Syst., vol. 10, no. 06, pp. 1–14, Dec. 2018.

  • D. Laksono, „„Testing spatial data deliverance in SQL and NoSQL database using NodeJS fullstack Web app,‟‟ in Proc. 4th Int. Conf. Sci. Technol. (ICST), Yogyakarta, Indonesia, Aug. 2018

  • Haleemunnisa fathima and Kumud Wasnik . “comparison of SQL, nosql and newSQL databases for internet of things”Published in: 2016 IEEE Bombay Section Symposium (IBSS)Date of Conference: 21-22 Dec. 2016

  • J. Fjällid, „„A comparative study of databases for storing sensor data,‟‟ M.S. thesis, Dept. Comp. Sci., Tech. Univ., Stockholm, Sweden, 2019.

  • J. Moon, S. Kum, and S. Lee, „„A heterogeneous IoT data analysis frame- work with collaboration of edge-cloud computing: Focusing on indoor PM10 and PM2.5 status prediction,‟‟ Sensors, vol. 19, no. 14, p. 3038, Jul. 2019.

  • Y. Liu, K. Akram Hassan, M. Karlsson, Z. Pang, and S. Gong, „„A datacentric Internet of Things framework based on azure cloud,‟‟ IEEE Access, vol. 7, pp. 53839–53858, 2019.

  • Y. Rasheed, M. Qutqut, and F. Almasalha, „„Overview of the current status of NoSQL database,‟‟ Int. J. Comput. Sci. Netw. Secur., vol. 19, no. 4, pp. 47–53, Apr. 2019.


Abstract Views: 158

PDF Views: 0




  • Data Performance Evaluation Using RDBMS and non-RDBMS

Abstract Views: 158  |  PDF Views: 0

Authors

D Salangai Nayagi
Department of CSE, AMC Engineering College, Bengaluru., India
J Anju
Department of CSE, AMC Engineering College, Bengaluru., India
A Ashwini
Department of CSE, AMC Engineering College, Bengaluru., India
Divya Krishnan
Department of CSE, AMC Engineering College, Bengaluru, India
K Tejaswini
Department of CSE, AMC Engineering College, Bengaluru., India

Abstract


The Internet of Things (IoT) initiates a challenge for the Database Management System (DBMS) in estimating how to store and handle very excess amount of heterogeneous data. DBMS is classified as two types: Relational DBMS and Non-Relational DBMS. This paper seeks to provide an estimation of two open-source DBMSs: MySQL as one of the Relational DBMS and MongoDB as one of the Non-Relational DBMS. The contrast is depended on estimating the performance of inserting and restoring the excess amount of data and assessing the performance of both types of databases with different stipulations in the cloud computing. This paper introduces two different models which are used for prediction and finds the difference between them to assess the latency in which that data responds efficiently and the database size. Thus the models of prediction help to choose the suitable database to store and maintain the data. The result shows that the MongoDB is more and more efficient than that of the MySQL database. In which MongoDB is able to save or store resources and data better and efficient than that of the MySQL.

Keywords


IoT, DBMS, SQL, MySQL, NoSQL, MongoDB, AWS, Cloud, Multiple Non-Linear Regressions

References