Uses inner products to measure the similarity between a signal and an analyzing function.
Published in Chapter:
Visualization Tools for Big Data Analytics in Quantitative Chemical Analysis: A Tutorial in Chemometrics
Gerard G. Dumancas (Louisiana State University – Alexandria, USA), Ghalib A. Bello (Icahn School of Medicine at Mount Sinai, USA), Jeff Hughes (RMIT University, Australia), Renita Murimi (Oklahoma Baptist University, USA), Lakshmi Chockalingam Kasi Viswanath (Oklahoma Baptist University, USA), Casey O'Neal Orndorff (Louisiana State University – Alexandria, USA), Glenda Fe Dumancas (Louisiana State University – Alexandria, USA), and Jacy D. O'Dell (Oklahoma Baptist University, USA)
Copyright: © 2018
|Pages: 45
DOI: 10.4018/978-1-5225-3142-5.ch030
Abstract
Modern instruments have the capacity to generate and store enormous volumes of data and the challenges involved in processing, analyzing and visualizing this data are well recognized. The field of Chemometrics (a subspecialty of Analytical Chemistry) grew out of efforts to develop a toolbox of statistical and computer applications for data processing and analysis. This chapter will discuss key concepts of Big Data Analytics within the context of Analytical Chemistry. The chapter will devote particular emphasis on 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. Various statistical techniques used for the analysis of Big Data in Chemometrics are introduced. This chapter also gives an overview of computational tools for Big Data Analytics for Analytical Chemistry. The chapter concludes with the discussion of latest platforms and programming tools for Big Data storage like Hadoop, Apache Hive, Spark, Google Bigtable, and more.