A Taxonomy of Dirty Data

By Won Y. Kim, B. Choi, E. Hong, Soo-Kyung Kim, Doheon Lee. 2004

317 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

BoostClean: Automated Error Detection and Repair for Machine Learning (2017, background): “Numerous data quality surveys across the database, statistics, and scientiﬁc literature highlight the prevalence, variety, and importance of this class of errors, which include missing data, incorrect data, or inconsistent representations of the same logical data value [25,29,33,39].”
A Survey on Data Quality: Classifying Poor Data (2015, background): “The terms used for data quality problems also differ [19], [20], [21], and also the mapping between each different problem to each dimension (depending on the definitions used).”
Detecting Data Errors: Where are we and what needs to be done? (2016, background): “There have been several surveys on classifying data errors [6, 17, 19, 24, 29].”
Research directions in data wrangling: Visualizations and transformations for usable and credible data (2011, unknown): “Kim et al.12 propose a taxonomy of 33 dirty data types.”
Profiler: integrated statistical analysis and visualization for data quality assessment (2012, background): “The database and statistics literature includes many taxonomies of anomalous data [5, 7, 10, 19, 24, 33].”