Recent Advances in Big Data Analytics

Daoji Li (), Yinfei Kong (), Zemin Zheng () and Jianxin Pan ()
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Daoji Li: California State University
Yinfei Kong: California State University
Zemin Zheng: University of Science and Technology of China
Jianxin Pan: The University of Manchester

Chapter Chapter 25 in The Palgrave Handbook of Operations Research, 2022, pp 805-834 from Springer

Abstract: Abstract Unprecedented advances in digital technology have produced a revolution that is transforming science and society. Big data have been rapidly generated in many disciplines, such as business, sciences, engineering, medicine, biology, and humanities. It is often accompanied by a large number of features and/or a large volume of observations. The value of big data lies in effective analysis using statistical inference and machine learning methods that are computationally scalable and efficient. There have seen many new statistical methods and tools to deal with big data in recent years. In this chapter, we aim to summarize some of these approaches to provide a selective overview of the recent developments of theory, methods, and implementations for big data analytics. We will focus on two types of big data: ultrahigh-dimensional data and massive data, where the former refers to the data in which the number of features may grow exponentially with the number of observations while the latter means that the number of observations is huge and much larger than the number of features.

Date: 2022
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DOI: 10.1007/978-3-030-96935-6_25

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