Steerable Self-Driving Data Visualization

Yuyu Luo; Xuedi Qin; Chengliang Chai; Nan Tang; Guoliang Li; Wenbo Li

doi:10.1109/TKDE.2020.2981464

Steerable Self-Driving Data Visualization

Yuyu Luo, Xuedi Qin, Chengliang Chai^*, Nan Tang, Guoliang Li^*, Wenbo Li

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

26 Citations (Scopus)

Abstract

In this work, we present a self-driving data visualization system, called DeepEye, that automatically generates and recommends visualizations based on the idea of visualization by examples. We propose effective visualization recognition techniques to decide which visualizations are meaningful and visualization ranking techniques to rank the good visualizations. Furthermore, a main challenge of automatic visualization system is that the users may be misled by blindly suggesting visualizations without knowing the user's intent. To this end, we extend DeepEye to be easily steerable by allowing the user to use keyword search and providing click-based faceted navigation. Empirical results, using real-life data and use cases, verify the power of our proposed system.

Original language	English
Pages (from-to)	475-490
Number of pages	16
Journal	IEEE Transactions on Knowledge and Data Engineering
Volume	34
Issue number	1
DOIs	https://doi.org/10.1109/TKDE.2020.2981464
Publication status	Published - 1 Jan 2022
Externally published	Yes

Keywords

Data exploration
Data visualization
Faceted navigation
Keyword search
Visualization recommendation

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10.1109/TKDE.2020.2981464

Cite this

Luo, Y., Qin, X., Chai, C., Tang, N., Li, G., & Li, W. (2022). Steerable Self-Driving Data Visualization. IEEE Transactions on Knowledge and Data Engineering, 34(1), 475-490. https://doi.org/10.1109/TKDE.2020.2981464

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title = "Steerable Self-Driving Data Visualization",

abstract = "In this work, we present a self-driving data visualization system, called DeepEye, that automatically generates and recommends visualizations based on the idea of visualization by examples. We propose effective visualization recognition techniques to decide which visualizations are meaningful and visualization ranking techniques to rank the good visualizations. Furthermore, a main challenge of automatic visualization system is that the users may be misled by blindly suggesting visualizations without knowing the user's intent. To this end, we extend DeepEye to be easily steerable by allowing the user to use keyword search and providing click-based faceted navigation. Empirical results, using real-life data and use cases, verify the power of our proposed system.",

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AU - Li, Wenbo

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AB - In this work, we present a self-driving data visualization system, called DeepEye, that automatically generates and recommends visualizations based on the idea of visualization by examples. We propose effective visualization recognition techniques to decide which visualizations are meaningful and visualization ranking techniques to rank the good visualizations. Furthermore, a main challenge of automatic visualization system is that the users may be misled by blindly suggesting visualizations without knowing the user's intent. To this end, we extend DeepEye to be easily steerable by allowing the user to use keyword search and providing click-based faceted navigation. Empirical results, using real-life data and use cases, verify the power of our proposed system.

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Steerable Self-Driving Data Visualization

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Keywords

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