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)
Incoming Non-self Citations Over Time
Authors
- 1. Doris Jung-Lin Lee (University of California Berkeley)
- 2. Dixin Tang (University of California Berkeley)
- 3. Kunal Agarwal (University of California Berkeley)
- 4. Thyne Boonmark (University of California Berkeley)
- 5. Caitlyn Chen (University of California Berkeley)
- 6. Jake Kang (University of California Berkeley)
- 7. Ujjaini Mukhopadhyay (University of California Berkeley)
- 8. Jerry Song (University of California Berkeley)
- 9. Micah Yong (University of California Berkeley)
- 10. Marti A. Hearst (University of California Berkeley)
- 11. Aditya G. Parameswaran (University of California Berkeley)
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}
}
Incoming Citations (Sorted by Pagerank)
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