Considerations and recommendations for data availability for data analytics for manufacturing

Don Libes, Seungjun Shin, Jungyub Woo

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Scopus citations

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.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015
EditorsFeng Luo, Kemafor Ogan, Mohammed J. Zaki, Laura Haas, Beng Chin Ooi, Vipin Kumar, Sudarsan Rachuri, Saumyadipta Pyne, Howard Ho, Xiaohua Hu, Shipeng Yu, Morris Hui-I Hsiao, Jian Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages68-75
Number of pages8
ISBN (Electronic)9781479999255
DOIs
StatePublished - 2015 Dec 22
Externally publishedYes
Event3rd IEEE International Conference on Big Data, IEEE Big Data 2015 - Santa Clara, United States
Duration: 2015 Oct 292015 Nov 1

Publication series

NameProceedings - 2015 IEEE International Conference on Big Data, IEEE Big Data 2015

Other

Other3rd IEEE International Conference on Big Data, IEEE Big Data 2015
Country/TerritoryUnited States
CitySanta Clara
Period15/10/2915/11/1

Keywords

  • big data
  • challenge problems
  • data analytics
  • data quality
  • requirements
  • smart manufacturing

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