LakeVisage: Towards Scalable, Flexible and Interactive Visualization Recommendation for Data Discovery over Data Lakes
Summary: Introduces visualization recommendation for data discovery over data lakes: automatically surfaces visualizations that reveal salient trends in query-result tables. LakeVisage constructs candidate plans from discovery-engine outputs and prunes uninteresting ones, yielding ~10x speedups and improved usability. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Yihao Hu (Duke University)
- 2. Jin Wang (Megagon Labs)
- 3. Sajjadur Rahman (Adobe)
BibTeX Citation
@article{hu_vldb25,
title = {{LakeVisage: Towards Scalable, Flexible and Interactive Visualization Recommendation for Data Discovery over Data Lakes}},
author = {Hu, Yihao and Wang, Jin and Rahman, Sajjadur},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {10},
pages = {3545--3558},
doi = {10.14778/3748191.3748214},
url = {https://doi.org/10.14778/3748191.3748214},
year = {2025}
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