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Steering data quality with visual analytics: The complexity challenge

Liu, S., Andrienko, G. ORCID: 0000-0002-8574-6295, Wu, Y. , Cao, N., Jiang, L., Shi, C., Wang, Y. S. & Hong, S. (2018). Steering data quality with visual analytics: The complexity challenge. Visual Informatics, 2(4), pp. 191-197. doi: 10.1016/j.visinf.2018.12.001


Data quality management, especially data cleansing, has been extensively studied for many years in the areas of data management and visual analytics. In the paper, we first review and explore the relevant work from the research areas of data management, visual analytics and human-computer interaction. Then for different types of data such as multimedia data, textual data, trajectory data, and graph data, we summarize the common methods for improving data quality by leveraging data cleansing techniques at different analysis stages. Based on a thorough analysis, we propose a general visual analytics framework for interactively cleansing data. Finally, the challenges and opportunities are analyzed and discussed in the context of data and humans.

Publication Type: Article
Additional Information: ©2018 Zhejiang University and Zhejiang University Press. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license(
Publisher Keywords: Data quality management, Visual analytics, Data cleansing
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Departments: Bayes Business School
Text - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.

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