Conference paper
Big Data Analytics for Industrial Process Control
Today, in modern factories, each step in manufacturing produces a bulk of valuable as well as highly precise information. This provides a great opportunity for understanding the hidden statistical dependencies in the process. Systematic analysis and utilization of advanced analytical methods can lead towards more informed decisions.
In this article we discuss some of the challenges related to big data analysis in manufacturing and relevant solutions to some of these challenges.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2017 |
| Pages: | 1-8 |
| Proceedings: | 2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation |
| Series: | Emerging Technologies and Factory Automation (etfa), International Conference on |
| ISBN: | 1509065059 , 1509065067 , 9781509065059 , 9781509065066 , 1509065040 and 9781509065042 |
| ISSN: | 19460759 and 19460740 |
| Types: | Conference paper |
| DOI: | 10.1109/ETFA.2017.8247658 |
| ORCIDs: | Kulahci, Murat |
Big Data Big Data analytics Cloud computing Data mining Genetic algorithms KEYWORDS Manufacturing Prediction algorithms Sparks advanced analytical methods control engineering computing data analysis hidden statistical dependencies industrial process control manufacturing manufacturing processes modern factories process control production engineering computing systematic analysis
