Many issues still exist in data cleaning despite extensive studies on this method. With visual interface and visualization

visual data cleaning has become one of the most important data cleaning methods. This study describes existing data quality problems and visual data cleaning processes

reviews state-of-the-art visual data cleaning methods (including sources

categories of data quality issues

and visual data cleaning methods)

and summarizes the challenges and opportunities associated with visual data cleaning problems. Data cleaning techniques are related to specific data quality issues. Thus

this study examines different data quality problems to summarize and review previous works on visual data cleaning. Based on data quality issues

visual cleaning methods are summarized as direct visual cleaning

visual missing data

visual uncertainty data

visual data transformation

and data cleaning resource sharing. Challenges and further research directions are surveyed according to different data quality issues. We introduce and provide an overview of visual data cleaning problems

as well as highlight research directions of visual data cleaning.