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Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows

Summary: Lux embeds always-on visualization recommendations into pandas notebooks, surfacing dataframe patterns and promising analyses as users work. Its high-level visualization language supports rapid experimentation with under two seconds’ overhead for 98% of UCI datasets. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
13133
Venue
VLDB
Year
2022
Pagerank
7.7873864e-05
Overall Rank
3,072 | 78.93%
DOI
10.14778/3494124.3494151

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{lee_vldb22,
        title = {{Lux: Always-on Visualization Recommendations for Exploratory Dataframe Workflows}},
        author = {Lee, Doris Jung-Lin and Tang, Dixin and Agarwal, Kunal and Boonmark, Thyne and Chen, Caitlyn and Kang, Jake and Mukhopadhyay, Ujjaini and Song, Jerry and Yong, Micah and Hearst, Marti A. and Parameswaran, Aditya G.},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {3},
        pages = {727--738},
        doi = {10.14778/3494124.3494151},
        url = {https://doi.org/10.14778/3494124.3494151},
        year = {2022}
}

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