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Chemometrics: From Data Preprocessing to Fog Computing
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Author(s): Gerard G. Dumancas (Louisiana State University, Alexandria, USA), Ghalib Bello (Icahn School of Medicine at Mount Sinai, New York, USA), Jeff Hughes (RMIT University, Melbourne, Australia), Renita Murimi (Oklahoma Baptist University, Shawnee, USA), Lakshmi Viswanath (Oklahoma Baptist University, Shawnee, USA), Casey O. Orndorff (University of the Ozarks, Clarksville, USA), Glenda Fe G. Dumancas (Louisiana State University, Alexandria, USA), Jacy O'Dell (Oklahoma Baptist University, Claremore, USA), Prakash Ghimire (Louisiana State University, Alexandria, USA)and Catherine Setijadi (Louisiana State University, Alexandria, USA)
Copyright: 2019
Volume: 2
Issue: 1
Pages: 42
Source title:
International Journal of Fog Computing (IJFC)
Editor(s)-in-Chief: Sam Goundar (Victoria University of Wellington, New Zealand)and Kashif Munir (National College of Business Administration & Economics, Pakistan)
DOI: 10.4018/IJFC.2019010101
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Abstract
The accumulation of data from various instrumental analytical instruments has paved a way for the application of chemometrics. Challenges, however, exist in processing, analyzing, visualizing, and storing these data. Chemometrics is a relatively young area of analytical chemistry that involves the use of statistics and computer applications in chemistry. This article will discuss various computational and storage tools of big data analytics within the context of analytical chemistry with examples, applications, and usage details in relation to fog computing. The future of fog computing in chemometrics will also be discussed. The article will dedicate particular emphasis to preprocessing techniques, statistical and machine learning methodology for data mining and analysis, tools for big data visualization, and state-of-the-art applications for data storage using fog computing.
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