| Issue |
E3S Web Conf.
Volume 214, 2020
2020 International Conference on Energy Big Data and Low-carbon Development Management (EBLDM 2020)
|
|
|---|---|---|
| Article Number | 03045 | |
| Number of page(s) | 5 | |
| Section | Digital Development and Environmental Management of Energy Supply Chain | |
| DOI | https://doi.org/10.1051/e3sconf/202021403045 | |
| Published online | 07 December 2020 | |
Extracting knowledge patterns in a data lake for management effectiveness
1 International Business School, Shaanxi Normal University, Xi’an, China
2 International Business School, Shaanxi Normal University, Xi’an, China
a czy@snnu.edu.cn
b 1219769143@qq.com
c 2585941175@qq.com
With the correlation collision between different types of data becomes more and more intense, a meaningful and far-reaching data revolution has arrived. Enterprises urgently require a hybrid data platform that can effectively break data silos, and unify data aggregation and sharing. Once the data lake was born, it has been a promising method for enterprises to profoundly improve their Business Intelligence. In this paper, we combine principle component analysis (PCA) with a network-based approach to extract a visual knowledge pattern from data sources in data lake, so as to improve management effectiveness.
© The Authors, published by EDP Sciences, 2020
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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