212
Views
1
CrossRef citations to date
0
Altmetric
Discussion

Discussion on “Experiences with big data: Accounts from a data scientist’s perspective”

ORCID Icon
Pages 553-555 | Published online: 06 Mar 2020
 

Abstract

I congratulate the authors for their interesting and insightful discussion on big data in production environments. Although problems with big data are trendy research topics, there are many practical opportunities and challenges for its use by quality practitioners for data-driven activities such as assurance, diagnosis, monitoring, and control. I appreciate the opportunity to discuss and provide my insights on the topic. In the following, I confine my assessments to data acquisition and preprocessing, and statistical methods for quality engineering.

This article responds to:
Experiences with big data: Accounts from a data scientist’s perspective

Additional information

Notes on contributors

Murat Caner Testik

Murat Caner Testik is a professor and department chair in the Industrial Engineering Department at Hacettepe University. He has a PhD degree in Industrial Engineering from Arizona State University with a major in quality engineering. His research interests and publications are mainly in the area of quality engineering and data mining for quality and process improvement. He is currently the president of the European Network for Business and Industrial Statistics. He is a past editor-in-chief of Quality Engineering.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 694.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.