Title :
Considerations and recommendations for data availability for data analytics for manufacturing
Author :
Don Libes;Seungjun Shin;Jungyub Woo
Author_Institution :
Engineering Laboratory, National Institute of Standards and Technology, Gaithersburg, Maryland, 20899 USA
Abstract :
Data analytics is increasingly becoming recognized as a valuable set of tools and techniques for improving performance in the manufacturing enterprise. However, data analytics requires data and a lack of useful and usable data has become an impediment to research in data analytics. In this paper, we describe issues that would help aid data availability including data quality, reliability, efficiency, and formats specific to data analytics in manufacturing. To encourage data availability, we present recommendations and requirements to guide future data contributions. We also describe the need for data for challenge problems in data analytics. A better understanding of these needs, recommendations, and requirements may improve the ability of researchers and other practitioners to improve research and more rapidly deploy data analytics in manufacturing.
Keywords :
"Data analysis","Manufacturing","Sensors","Encryption","NIST","Synchronization"
Conference_Titel :
Big Data (Big Data), 2015 IEEE International Conference on
DOI :
10.1109/BigData.2015.7363743