Data quality between promises and results

摘要

Improving the quality of data is a crucial task for business, health, and scientific data. Several data cleaning algorithms have been translated into tools to identify and repair data errors such as outlying values, duplicate records, typos, missing values, and violations of rules in general [1], [2], [3], [4].