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Making data visualization more efficient and effective: a survey

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Abstract

Data visualization is crucial in today’s data-driven business world, which has been widely used for helping decision making that is closely related to major revenues of many industrial companies. However, due to the high demand of data processing w.r.t. the volume, velocity, and veracity of data, there is an emerging need for database experts to help for efficient and effective data visualization. In response to this demand, this article surveys techniques that make data visualization more efficient and effective. (1) Visualization specifications define how the users can specify their requirements for generating visualizations. (2) Efficient approaches for data visualization process the data and a given visualization specification, which then produce visualizations with the primary target to be efficient and scalable at an interactive speed. (3) Data visualization recommendation is to auto-complete an incomplete specification, or to discover more interesting visualizations based on a reference visualization.

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Notes

  1. Note that, our pipeline and terminologies used in this paper are slightly different than those used in the visualization community. Please refer to https://infovis-wiki.net/wiki/Visualization_Pipeline for more details.

  2. https://courses.cs.washington.edu/courses/cse442/17au/lectures/CSE442-Tools.pdf.

  3. https://www.tableau.com/about/blog/2018/11/ask-data-simplifying-analytics-natural-language-98655.

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Acknowledgements

Funding was provided by 973 Program of China (Grant No. 2015CB358700) and National Natural Science Foundation of China (Grant Nos. 61632016, 61521002, 61661166012).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xuedi Qin, Yuyu Luo & Guoliang Li

  2. Qatar Computing Research Institute, HBKU, Doha, Qatar

    Nan Tang

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  1. Xuedi Qin
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  2. Yuyu Luo
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  3. Nan Tang
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  4. Guoliang Li
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Correspondence to Guoliang Li.

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Qin, X., Luo, Y., Tang, N. et al. Making data visualization more efficient and effective: a survey. The VLDB Journal 29, 93–117 (2020). https://doi.org/10.1007/s00778-019-00588-3

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