Author:
Bhanage Deepali Arun,Pawar Ambika Vishal
Abstract
Purpose
The purpose of this paper is to present the bibliometric study of articles IT Infrastructure Management to Avoid Failure Conditions. As in today’s era of IT Industries, IT infrastructure management plays a crucial role. As a result, substantial research is going on to improve the reliability and availability of assets in IT infrastructure.
Design/methodology/approach
The paper analyzes and focuses the results acquired from articles accessed from Scopus for the past 15 years by examining in terms of frequently used keywords, the amount of work done in different countries and year-wise progression of the research, prolific authors, article citation frequencies, etc. Tools such as Gephi, Word Cloud, BiblioShiny, GPS visualizer, etc. are used for bibliometric analysis.
Findings
The study comes out with maximum publications of IT infrastructure management from conferences and journals. Anomaly detection, log analysis and learning system are the most frequently used keywords in the publications. Significant research has been done in the USA, followed by China under the area of Computer Science with an increase in publication since 2018.
Originality/value
This paper provides an accurate idea about the amount of work done in different countries and year-wise progression of the research. This bibliometric analysis will be useful for beginners to conduct a literature survey using appropriate literature available on the Scopus database.
Subject
Library and Information Sciences,General Computer Science
Reference20 articles.
1. Improving performances of log mining for anomaly prediction through nlp-based log parsing,2018
2. A user-friendly log viewer for storage systems;ACM Transactions on Storage,2016
3. Log analytics for dependable enterprise telephony,2012
4. Towards comprehensive dependability-driven resource use and message log-analysis for HPC systems diagnosis;Journal of Parallel and Distributed Computing,2019
5. Enabling dependability-driven resource use and message log-analysis for cluster system diagnosis,2018
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