KNOWPIA
WELCOME TO KNOWPIA
VACUUM 
Summary
VACUUM[1][2][3][4] is a set of normative 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 edit
- ^ 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.
- ^ Pipino, Leo L.; Lee, Yang W.; Wang, Richard Y. (2002-04-01). "Data quality assessment". Communications of the ACM. 45 (4): 211–218. doi:10.1145/505248.506010. ISSN 0001-0782. S2CID 426050.
- ^ Wang, R.Y.; Storey, V.C.; Firth, C.P. (August 1995). "A framework for analysis of data quality research". IEEE Transactions on Knowledge and Data Engineering. 7 (4): 623–640. doi:10.1109/69.404034.