The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Big Data for Modern Industry: Challenges and Trends [Point of View]
Open accessAbstract
We are living in an era of data deluge and as a result, the term ``big data'' is appearing in many contexts, from meteorology, genomics, complex physics simulations, biological and environmental research, finance and business to healthcare. As the name implies, big data literally means large collections of data sets containing abundant information. However, it has some special characteristics that distinguish it from “very large data”or “massive data”that are simply enormous collections of simple-format records, typically equivalent to enormous spreadsheets. Big data, being generally unstructured and heterogeneous, is extremely complex to deal with via traditional approaches, and requires real-time or almost real-time analysis. A short definition can therefore be that “big data” refers to data sets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.
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Cites
Business intelligence and analytics: from big data to big impact
Chen, H., Chiang, R. H. L., & Storey, V. C. (2012) · MIS Quarterly · doi:10.2307/41703503 · Cited by 6,250
Data-based techniques focused on modern industry: An overview
Yin, S., Li, X., Gao, H., & Kaynak, O. (2015) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2014.2308133 · Cited by 1,025
Self-citation
Cited by
Lei, Y., Jia, F., Lin, J., Xing, S., & Ding, S. X. (2016) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2016.2519325 · Cited by 1,191
Circular Digital Built Environment: An Emerging Framework
Çetin, S., De Wolf, C., & Bocken, N. (2021) · Sustainability · doi:10.3390/su13116348 · Cited by 367
Live Data Analytics With Collaborative Edge and Cloud Processing in Wireless IoT Networks
Sharma, S. K., & Wang, X. (2017) · IEEE Access · doi:10.1109/access.2017.2682640 · Cited by 241
“The term ‘‘Big data’’ usually refers to extremely large, heterogeneous and complex (semi-structured and unstructured) data-sets, which cannot be handled by the conventional data processing and storage tools/applications such as Relational Database Management System (RDBMS) [13].”
BackgroundInfluentialBig Data Analytics: A Review on Theoretical Contributions and Tools Used in Literature
Grover, P., & Kar, A. K. (2017) · Global Journal of Flexible Systems Management · doi:10.1007/s40171-017-0159-3 · Cited by 219
Digital twin for integration of design-manufacturing-maintenance: an overview
Fu, Y., Zhu, G., Zhu, M., & Xuan, F. (2022) · Chinese Journal of Mechanical Engineering · doi:10.1186/s10033-022-00760-x · Cited by 98
Metadata
- Authors
- Yin, S., & Kaynak, O.
- Venue
- Proceedings of the IEEE
- Published
- 2015-02
- Volume
- 103
- Issue
- 2
- Pages
- 143-146
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W1998092191
- Cited by
- 565
- References
- 2
- FWCI
- 107.42
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Yin, S., & Kaynak, O. (2015). Big Data for Modern Industry: Challenges and Trends [Point of View]. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2015.2388958
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