Advances in Big Data Analytics : Theory, Algorithms and Practices / by Yong Shi.

Format:

Book

Author/Creator:

Shi, Yong, Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Artificial intelligence-Data processing.

Big data.

Data mining.

Computer science.

Data Science.

Big Data.

Data Mining and Knowledge Discovery.

Models of Computation.

Local Subjects:

Data Science.

Big Data.

Data Mining and Knowledge Discovery.

Models of Computation.

Physical Description:

1 online resource (XIV, 728 pages) : 1 illustrations

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-981-16-3607-3

9789811636073

Access Restriction:

Restricted for use by site license.