Analytics on Non-Normalized Data Sources: More Learning, Rather Than More Cleaning

Analytics on Non-Normalized Data Sources: More Learning, Rather Than More Cleaning

Data analysis is increasingly performed over data assembled from uncontrolled sources, facing inconsistency in knowledge-representation conventions. The typical practice is to create “clean” data for analysis, matching entities and merging variants to overcome differences in kn...

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Podrobná bibliografie
Hlavní autoři: Alexis Cvetkov-Iliev, Alexandre Allauzen, Gael Varoquaux
Médium: Článek
Jazyk:English
Vydáno: IEEE 2022-01-01
Edice:IEEE Access
Témata:
Data analysis
data cleaning
data integration
embeddings
entity-matching
On-line přístup:https://ieeexplore.ieee.org/document/9758752/

Internet

https://ieeexplore.ieee.org/document/9758752/

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