VACUUM
VACUUMnormative guidance principles for achieving training and test dataset quality for structured datasets in data science and machine learning. The garbage-in, garbage out principle motivates a solution to the problem of data quality but does not offer a specific solution. Unlike the majority of the ad-hoc data quality assessment metrics often used by practitioners[5] VACUUM specifies qualitative principles for data quality management and serves as a basis for defining more detailed quantitative metrics of data quality.[6]
VACUUM is an acronym that stands for:
- valid
- accurate
- consistent
- uniform
- unified
- model
References
- ^ https://books.google.com/books?id=XPBbEAAAQBAJ&q=VACUUM
- ^ "VACUUM". www.enterprisedb.com. Retrieved 2021-04-27.
- ^ Jim Nasby (2015), All the Dirt on VACUUM, PGCon - PostgreSQL Conference for Users and Developers, Andrea Ross, retrieved 2021-04-27
- ^ "An Overview of VACUUM Processing in PostgreSQL". Severalnines. 2019-11-22. Retrieved 2021-04-27.
- S2CID 426050.
- .