IDEAS home Printed from https://ideas.repec.org/h/spr/sprchp/978-3-030-96935-6_25.html
   My bibliography  Save this book chapter

Recent Advances in Big Data Analytics

In: The Palgrave Handbook of Operations Research

Author

Listed:
  • 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)

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.

Suggested Citation

  • Daoji Li & Yinfei Kong & Zemin Zheng & Jianxin Pan, 2022. "Recent Advances in Big Data Analytics," Springer Books, in: Saïd Salhi & John Boylan (ed.), The Palgrave Handbook of Operations Research, chapter 0, pages 805-834, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-96935-6_25
    DOI: 10.1007/978-3-030-96935-6_25
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a search for a similarly titled item that would be available.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:sprchp:978-3-030-96935-6_25. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.