- Title
- Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies
- Creator
- Dai, Hong-Ning; Wang, Hao; Xu, Guangquan; Wan, Jiafu; Imran, Muhammad
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/184123
- Identifier
- vital:16438
- Identifier
-
https://doi.org/10.1080/17517575.2019.1633689
- Identifier
- ISBN:1751-7575 (ISSN)
- Abstract
- Data analytics in massive manufacturing data can extract huge business values while can also result in research challenges due to the heterogeneous data types, enormous volume and real-time velocity of manufacturing data. This paper provides an overview on big data analytics in manufacturing Internet of Things (MIoT). This paper first starts with a discussion on necessities and challenges of big data analytics in manufacturing data of MIoT. Then, the enabling technologies of big data analytics of manufacturing data are surveyed and discussed. Moreover, this paper also outlines the future directions in this promising area. © 2019 Informa UK Limited, trading as Taylor & Francis Group.
- Publisher
- Taylor and Francis Ltd.
- Relation
- Enterprise Information Systems Vol. 14, no. 9-10 (2020), p. 1279-1303
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright © 2019 Informa UK Limited, trading as Taylor & Francis Group
- Rights
- Open Access
- Subject
- 3503 Business Systems in Context; Data analytics; Data mining; Internet of things; Smart manufacturing
- Reviewed
- Funder
- The research of Hong-Ning Dai and Hao Wang is supported by Macao Science and Technology Development Fund under Grant No. 0026/2018/A1, National Natural Science Foundation of China (NFSC) under Grant No. 61672170, NSFC-Guangdong Joint Fund under Grant No. U1401251, and Science and Technology Program of Guangzhou under Grant No. 201807010058. Guang quan Xu’s work is supported by the State Key Development Program of China (No. 2017YFE0111900), National Science Foundation of China (No. 61572355, U1736115). Jiafu Wan’s work is supported by Science and Technology Program of Guangzhou (No. 201802030005), Guangdong Province Key Areas R & D Program (No. 2019B090919002). Muhammad Imran’s work is supported by the Deanship of Scientific Research, King Saud University through research group number RG-1435-051.
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