Title: Semi-structured data analysis and visualisation using NoSQL

Authors: Srinidhi Hiriyannaiah; G.M. Siddesh; P. Anoop; K.G. Srinivasa

Addresses: Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, MSR Nagar, Bengaluru, India ' Department of Information Science and Engineering, M.S. Ramaiah Institute of Technology, MSR Nagar, Bengaluru, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, MSR Nagar, Bengaluru, India ' Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology, MSR Nagar, Bengaluru, India

Abstract: In the field of computing, every day huge amounts of data are created by scientific experiments, companies and users' activities. These large datasets are labelled as 'big data', presenting new challenges for computer science researchers and professionals in terms of storage, processing and analysis. Traditional relational database systems (RDBMS) supported with conventional searches cannot be effectively used to handle such multi-structured data. NoSQL databases complement to the challenges of managing RDBMS with big data and facilitate in further analysis of data. In this paper, we introduce a framework that aims at analysing semi-structured data applications using NoSQL database MongoDB. The proposed framework focuses on the key aspects needed for semi-structured data analytics in terms of data collection, data parsing and data prediction. The layers involved in the framework are request layer facilitating the queries from user, input layer that interfaces the data sources and the analytics layer; and the output layer facilitating the visualisation of the analytics performed. A performance analysis for select+fetch operations needed for analytics, of MySQL and MongoDB is carried out where NoSQL database MongoDB outperforms MySQL database. The proposed framework is applied on predicting the performance and monitoring of cluster of servers.

Keywords: analytics; semi-structured data; big data analytics; server performance monitoring; cluster analytics; MongoDB; NoSQL analytics.

DOI: 10.1504/IJBDI.2018.092657

International Journal of Big Data Intelligence, 2018 Vol.5 No.3, pp.133 - 142

Received: 22 Jun 2016
Accepted: 17 Sep 2016

Published online: 30 Oct 2017 *

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