Steering data quality with visual analytics: The complexity challenge

Shixia Liu*, Gennady Andrienko, Yingcai Wu, Nan Cao, Liu Jiang, Conglei Shi, Yu Shuen Wang, Seokhee Hong

*Corresponding author for this work

Research output: Contribution to journalReview article

4 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)191-197
Number of pages7
JournalVisual Informatics
Volume2
Issue number4
DOIs
StatePublished - Dec 2018

Keywords

  • Data cleansing
  • Data quality management
  • Visual analytics

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    Liu, S., Andrienko, G., 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), 191-197. https://doi.org/10.1016/j.visinf.2018.12.001