Extracting knowledge patterns in a data lake for management effectiveness
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 la...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
EDP Sciences
2020-01-01
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| Series: | E3S Web of Conferences |
| Online Access: | https://www.e3s-conferences.org/articles/e3sconf/pdf/2020/74/e3sconf_ebldm2020_03045.pdf |
| Summary: | 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. |
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| ISSN: | 2267-1242 |