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Advances in big data analyticstheory, algorithms and practices /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Advances in big data analyticsby Yong Shi.
Reminder of title:	theory, algorithms and practices /
Author:	Shi, Yong.
Published:	Singapore :Springer Singapore :2022.
Description:	xiv, 728 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Big data.
Online resource:	https://doi.org/10.1007/978-981-16-3607-3
ISBN:	9789811636073$q(electronic bk.)

Advances in big data analyticstheory, algorithms and practices /
Shi, Yong.

Advances in big data analyticstheory, algorithms and practices /[electronic resource] :by Yong Shi. - Singapore :Springer Singapore :2022. - xiv, 728 p. :ill., digital ;24 cm.

Part One: Concept and Theoretical Foundation -- Chapter 1: Big Data and Big Data Analytics -- Chapter 2: Multiple Criteria Optimization Classification -- Chapter 3: Support Vector Machine Classification -- Part Two: Functional Analysis -- Chapter 4: Feature Selection -- Chapter 5: Data Stream Analysis -- Chapter 6: Learning Analysis -- Chapter 7: Sentiment Analysis -- Chapter 8: Link Analysis -- Chapter 9: Evaluation Analysis -- Part Three: Application and Future Analysis -- Chapter 10: Business and Engineering Applications -- Chapter 11: Healthcare Applications -- Chapter 12: Artificial Intelligence IQ Test -- Chapter 13: Conclusions.

Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence. Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.

ISBN: 9789811636073$q(electronic bk.)

Standard No.: 10.1007/978-981-16-3607-3doiSubjects--Topical Terms:

609582
Big data.

LC Class. No.: QA76.9.B45 / S45 2022

Dewey Class. No.: 005.7

Advances in big data analyticstheory, algorithms and practices /
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505 0 $a Part One: Concept and Theoretical Foundation -- Chapter 1: Big Data and Big Data Analytics -- Chapter 2: Multiple Criteria Optimization Classification -- Chapter 3: Support Vector Machine Classification -- Part Two: Functional Analysis -- Chapter 4: Feature Selection -- Chapter 5: Data Stream Analysis -- Chapter 6: Learning Analysis -- Chapter 7: Sentiment Analysis -- Chapter 8: Link Analysis -- Chapter 9: Evaluation Analysis -- Part Three: Application and Future Analysis -- Chapter 10: Business and Engineering Applications -- Chapter 11: Healthcare Applications -- Chapter 12: Artificial Intelligence IQ Test -- Chapter 13: Conclusions.
520 $a Today, big data affects countless aspects of our daily lives. This book provides a comprehensive and cutting-edge study on big data analytics, based on the research findings and applications developed by the author and his colleagues in related areas. It addresses the concepts of big data analytics and/or data science, multi-criteria optimization for learning, expert and rule-based data analysis, support vector machines for classification, feature selection, data stream analysis, learning analysis, sentiment analysis, link analysis, and evaluation analysis. The book also explores lessons learned in applying big data to business, engineering and healthcare. Lastly, it addresses the advanced topic of intelligence-quotient (IQ) tests for artificial intelligence. Since each aspect mentioned above concerns a specific domain of application, taken together, the algorithms, procedures, analysis and empirical studies presented here offer a general picture of big data developments. Accordingly, the book can not only serve as a textbook for graduates with a fundamental grasp of training in big data analytics, but can also show practitioners how to use the proposed techniques to deal with real-world big data problems.
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