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Analysis of Devops Tools using the Traditional Data Mining Techniques

by R. Vaasanthi, V. Prasanna Kumari, S. Philip Kingston
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 161 - Number 11
Year of Publication: 2017
Authors: R. Vaasanthi, V. Prasanna Kumari, S. Philip Kingston
10.5120/ijca2017913319

R. Vaasanthi, V. Prasanna Kumari, S. Philip Kingston . Analysis of Devops Tools using the Traditional Data Mining Techniques. International Journal of Computer Applications. 161, 11 ( Mar 2017), 47-49. DOI=10.5120/ijca2017913319

@article{ 10.5120/ijca2017913319,
author = { R. Vaasanthi, V. Prasanna Kumari, S. Philip Kingston },
title = { Analysis of Devops Tools using the Traditional Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { Mar 2017 },
volume = { 161 },
number = { 11 },
month = { Mar },
year = { 2017 },
issn = { 0975-8887 },
pages = { 47-49 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume161/number11/27195-2017913319/ },
doi = { 10.5120/ijca2017913319 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:07:14.546024+05:30
%A R. Vaasanthi
%A V. Prasanna Kumari
%A S. Philip Kingston
%T Analysis of Devops Tools using the Traditional Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 161
%N 11
%P 47-49
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

DevOps - a portmanteau of Development and Operations is a set of practices focused on using latest generation tools to automate the configuration process for system resources and application components [2]. Process efficiency improves from hours and days to seconds and minutes. IT performance strongly correlates with well-known DevOps practices such as use of Version Control and Continuous Delivery. The longer an organization has implemented — and continues to improve upon — DevOps practices, the better it performs [7]. And better IT performance correlates to higher performance for the entire organization. Today, enormous amount of tools are present in DevOps space.

References
  1. CSI Communications, Knowledge Digest for IT community ISSN 0970-647X, Volume No: 38, August 2014.
  2. Devops for Big data @ Supaket, Solution Architect at Enersys.co.th, August 2016
  3. Dell Software, “Devops for the cloud”, achieving agility throughout the application life cycle, Dec 2014.
  4. Han J. and Kamber M., Data Mining: Concepts and Techniques, 2nd ed., San Francisco, Morgan Kauffmann Publishers,2001
  5. Shahrukh Teli and Prashasti Kanikar., A Survey on Decision Tree Based Approaches in Data Mining., Volume 5, Issue 4, 2015., Volume 5, Issue 4, 2015.
  6. Li, Linna, and Xuemin Zhang. "Study of data mining algorithm based on decision tree." In Computer Design and Applications (ICCDA), 2010 International Conference on, vol. 1, pp. V1-155. IEEE, 2010
  7. CA technologies, Research paper: DevOps: the worst-kept secret to winning in the application economy., October 2014
  8. Floris Erich, Chintan Amrit and Maya Daneva., Research Gate, “Report: DevOps Literature Review”, October 2014
  9. Saroj, Tripti Chaudhary., “Study on Various Clustering Techniques” International Journal of Computer Science and Information Technologies, Vol. 6 (3) , 2015, 3031-3033
  10. Shameem Fathima,D. Manimegalai and Nisar Hundewale, “ A Review of Data Mining Classification Techniques Applied for Diagnosis and Prognosis of the Arbovirus- Dengue” IJCSI International Journal of Computer science issues, Vol 8 Issue 6m No 3 , 2011.
  11. https://en.wikipedia.org/wiki/Systems_development_life_cycle
  12. S. Anupama Kumar and M.N. Vijayalakshmi “Relevance of Data mining techniques in efficient sector” International Journal of Machine Learning and Computing. Vol 3, No.1, 2013.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining DevOps and Classification

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