Machine Learning Tools and Toolkits in the Exploration of Big Data

Authors

  • Khan A Department of Computer Science, Aligarh Muslim University, Aligarh, India
  • Zubair S Department of Computer Science, Aligarh Muslim University, Aligarh, India

DOI:

https://doi.org/10.26438/ijcse/v6i12.570575

Keywords:

Big data, Application Programming Interface (API), Command Line Interface (CLI), Graphic User Interface (GUI), Machine learning, Tool, Toolkits, Platform, Library, Interface

Abstract

Machine learning (ML) is the best way to make progress towards human level artificial intelligence, which allows software applications to become more accurate in predicting results. It is the most promising technique that has profound realization in reorganizing practices pertaining to various fields viz. healthcare, education world industry, retail and manufacturing sectors, traffic and urban planning etc. The compilation and storage followed by specific training of the stored data are some of the salient features of the machine learning process that has tremendous scope in discovering novel output in various relevant fields. There are plenty of tools in ML that may help in the training of data without being explicitly programmed. Tools are categorized into- framework, platform, library, and interface. For the successful development and effective execution of ML, one can categorically manipulate various related tools. Working through such tools advances the process as applied to the various applications. In the present study, we intend to exploit recommendation engines for the development of tools that can handle the huge quantity of data. The usage of the overwhelming quantity of multimodal data and streamlining the same for its personalized usage are some of the unique features of the study. We also focus on the evaluation of a toolkit with loads of data and furthering several ML tools along with their features and use for the desired application in the relevant field.

References

[1] https://www.simplilearn.com/what-is-machine-learning-and-why-it-matters-article

[2] https://dzone.com/articles/5-open-source-machine-learning-frameworks-and-tool

[3] https://www.forbes.com/sites/ciocentral/2018/02/28/gartner-magic-quadrant-whos-winning-in-the-data-machine-learning-space/

[4] J. V. N. Lakshmi and A. Sheshasaayee, “A Big Data Analytical Approach for Analyzing Temperature Dataset using Machine Learning Techniques,” Int. J. Sci. Res. Comput. Sci. Eng., vol. 5, no. 3, pp. 92–97, 2017.

[5] C. E. Sapp, “Preparing and Architecting for Machine Learning,” 2017.

[6] https://www.gartner.com/it-glossary/big-data/

[7] https://www.quora.com/How-are-big-data-and-machine-learning-related

[8] Rakesh. S.Shirsath, VaibhavA.Desale, Amol. D.Potgantwar, "Big Data Analytical Architecture for Real-Time Applications", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.4, pp.1-8, 2017

[9]https://www.forbes.com/sites/ciocentral/2018/02/28/gartner-magic-quadrant-whos-winning-in-the-data-machine-learning-space/#3995d9407dab

[10] https://www.sas.com/en_us/insights/analytics/machine-learning.html

[11] https://towardsdatascience.com/gui-fying-the-machine-learning-workflow-towards-rapid-discovery-of-viable-pipelines-cab2552c909f

[12] https://machinelearningmastery.com/machine-learning-tools/

[13] https://knowm.org/machine-learning-tools-an-overview/

[14] https://blogs.opentext.com/choosing-the-right-programming-language-for-machine-learning-algorithms-with-apache-spark/amp/

[15] https://medium.com/@UdacityINDIA/machine-learning-programming-languages-why-is-the-best-and-why-56f9f370cb99

[16] https://www.analyticsindiamag.com/machine-learning-framework-10-need-know/

[17] https://searchenterpriseai.techtarget.com/feature/How-to-make-a-wise-machine-learning-platforms-comparison

[18] V. Vinothina, “MACHINE LEARNING TOOLS-AN OVERVIEW,” in International Conference on Recent Trends in Engineering Science, Humanities and Management, 2017, pp. 629–637.

[19] https://www.oreilly.com/ideas/square-off-machine-learning-libraries

[20] https://machinelearningmastery.com/tour-weka-machine-learning-workbench/

[21] https://bookdown.org/rdpeng/rprogdatascience/history-and-overview-of-r.html

[22] https://en.wikipedia.org/wiki/SciPy

[23] https://github.com/scikit-learn/scikit-learn

[24] https://github.com/EdwardRaff/JSAT

[25] http://accord-framework.net/intro.html

[26] Pylearn2 Documentation Release dev, LISA lab, University of Montreal, 2015.

[27] https://www.csie.ntu.edu.tw/~cjlin/libsvm/

[28] Thomas A. Henzinger, Anmol V. Singh, Vasu Singh, Thomas Wies,DamienZufferey, “Static Scheduling in clouds”

[29] Mike Gashler, “Waffles: A Machine Learning Toolkit”, Journal of Machine Learning Research, 12 (2011), 2383-2387.

[30] G.Holmes, A.Donkin, I.H Witten, “WEKA: a machine learning workbench”, Proceedings of Second Australian and New Zealand conferences on Intelligent Information System, 1994.

[31] https://www.predictiveanalyticstoday.com/knime/

[32] https://rapidminer.com/products/studio/feature-list/

[33] https://orange.biolab.si/#Orange-Features

[34] http://126kr.com/article/yucgkiovd

[35] S¨orenSonnenburg et.al, “The SHOGUN Machine Learning Toolbox”, Journal of Machine Learning Research 11 (2010) , 1799-1802.

[36] https://mahout.apache.org/docs/latest/index.html

[37] http://cloudacademy.com/blog/aws-machine-learning/

[38] https://www.microsoft.com/en-us/research/blog/microsoft-open-sources-distributed-machine-learning-toolkit-for-more-efficient-big-data-research/

[39] https://www.predictiveanalyticstoday.com/microsoft-azure-machine-learning/

[40] https://spark.apache.org/mllib/

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Published

2018-12-31
CITATION
DOI: 10.26438/ijcse/v6i12.570575
Published: 2018-12-31

How to Cite

[1]
A. Khan and S. Zubair, “Machine Learning Tools and Toolkits in the Exploration of Big Data”, Int. J. Comp. Sci. Eng., vol. 6, no. 12, pp. 570–575, Dec. 2018.

Issue

Vol. 6 No. 12 (2018): IJCSE December Editor

Section

Research Article

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