Advances in Big Data Analytics

Theory, Algorithms and Practices
1. Auflage 2022
ISBN: 978-981-16-3606-6
Verlag: Springer Nature Singapore

Buch, Englisch, 728 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 1267 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Shi, Yong

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik

Inhaltsverzeichnis

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.

Über Autor(innen)

Yong Shi is the Director of the Research Center on Fictitious Economy and Data Science, and Director of the Key Lab of Big Data Mining and Knowledge Management, Chinese Academy of Sciences. He has been an Isaacson Professor, Union Pacific Chair, and Charles W. and Margre H. Durham Distinguished Professor of Information Technology at the College of Information Science and Technology, University of Nebraska at Omaha, USA. He has served on the State Council of the PRC (2016), as an elected member of the International Eurasian Academy of Science (2017), and as an elected fellow of the World Academy of Sciences for the Advancement of Science in Developing Countries (2015). His research interests include big data analysis, data science, business intelligence, data mining and multiple-criteria decision making. He has published more than 20 books, over 500 papers in various journals, and numerous conferences/proceedings papers. He is the Editor-in-Chief of both the International Journal ofInformation Technology and Decision Making (SCI) and of Annals of Data Science (Springer), and serves on the Editorial Boards of numerous academic journals.