IDEAS home Printed from https://ideas.repec.org/a/taf/amstat/v74y2020i4p329-344.html
   My bibliography  Save this article

Big Data? Statistical Process Control Can Help!

Author

Listed:
  • Peihua Qiu

Abstract

“Big data” is a buzzword these days due to an enormous amount of data-rich applications in different industries and research projects. In practice, big data often take the form of data streams in the sense that new batches of data keep being collected over time. One fundamental research problem when analyzing big data in a given application is to monitor the underlying sequential process of the observed data to see whether it is longitudinally stable, or how its distribution changes over time. To monitor a sequential process, one major statistical tool is the statistical process control (SPC) charts, which have been developed and used mainly for monitoring production lines in the manufacturing industries during the past several decades. With many new and versatile SPC methods developed in the recent research, it is our belief that SPC can become a powerful tool for handling many big data applications that are beyond the production line monitoring. In this article, we introduce some recent SPC methods, and discuss their potential to solve some big data problems. Certain challenges in the interface between the current SPC research and some big data applications are also discussed.

Suggested Citation

  • Peihua Qiu, 2020. "Big Data? Statistical Process Control Can Help!," The American Statistician, Taylor & Francis Journals, vol. 74(4), pages 329-344, October.
    • Handle: RePEc:taf:amstat:v:74:y:2020:i:4:p:329-344
    DOI: 10.1080/00031305.2019.1700163
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00031305.2019.1700163
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00031305.2019.1700163?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zeqing Yang & Mingxuan Zhang & Yingshu Chen & Ning Hu & Lingxiao Gao & Libing Liu & Enxu Ping & Jung Il Song, 2024. "Surface defect detection method for air rudder based on positive samples," Journal of Intelligent Manufacturing, Springer, vol. 35(1), pages 95-113, January.
    2. Wilson Rojas-Preciado & Mauricio Rojas-Campuzano & Purificación Galindo-Villardón & Omar Ruiz-Barzola, 2023. "Control Chart T2Qv for Statistical Control of Multivariate Processes with Qualitative Variables," Mathematics, MDPI, vol. 11(12), pages 1-32, June.

    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:taf:amstat:v:74:y:2020:i:4:p:329-344. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UTAS20 .

    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.