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Network science models for data analytics automationtheories and applications /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Network science models for data analytics automationby Xin W. Chen.
Reminder of title:	theories and applications /
Author:	Chen, Xin W.
Published:	Cham :Springer International Publishing :2022.
Description:	vi, 122 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Data mining.
Online resource:	https://doi.org/10.1007/978-3-030-96470-2
ISBN:	9783030964702$q(electronic bk.)

Network science models for data analytics automationtheories and applications /
Chen, Xin W.

Network science models for data analytics automationtheories and applications /[electronic resource] :by Xin W. Chen. - Cham :Springer International Publishing :2022. - vi, 122 p. :ill., digital ;24 cm. - Automation, collaboration, & e-services,v. 92193-4738 ;. - Automation, collaboration, & e-services ;v.2..

Network Science Models -- Interdependent Critical Infrastructures -- Public Health -- Smart and Autonomous Power Grid -- Water Distribution Systems -- Transportation Systems.

This book explains network science and its applications in data analytics for critical infrastructures, engineered systems, and knowledge acquisition. Each chapter describes step-by-step processes of how network science enables and automates data analytics through examples. The book not only dissects modeling techniques and analytical results but also explores the intrinsic development of these models and analyses. This unique approach bridges the gap between theory and practice and channels' managerial and problem-solving skills. Engineers, researchers, and managers would benefit from the extensive theoretical background and practical examples discussed in this book. Advanced undergraduate students and graduate students in mathematics, statistics, engineering, business, public health, and social science may use this book as a one-semester textbook or a reference book. Readers who are more interested in applications may skip Chapter 1 and peruse through the rest of the book with ease.

ISBN: 9783030964702$q(electronic bk.)

Standard No.: 10.1007/978-3-030-96470-2doiSubjects--Topical Terms:

184440
Data mining.

LC Class. No.: QA76.9.D343 / C44 2022

Dewey Class. No.: 006.312

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