LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes
Summary: LakeBench benchmarks joinable/unionable table discovery in messy data lakes with 16M real tables, >10K labeled queries, and diverse ground truth. It is 1,600× larger and 100× larger in storage than prior datasets, enabling effectiveness, efficiency, and scalability evaluation. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Yuhao Deng (Beijing Institute of Technology)
- 2. Chengliang Chai (Beijing Institute of Technology)
- 3. Lei Cao (Massachusetts Institute of Technology; University of Arizona)
- 4. Qin Yuan (Beijing Institute of Technology)
- 5. Siyuan Chen (Beijing Institute of Technology)
- 6. Yanrui Yu (Beijing Institute of Technology)
- 7. Zhaoze Sun (Beijing Institute of Technology)
- 8. Junyi Wang (Beijing Institute of Technology)
- 9. Jiajun Li (Beijing Institute of Technology)
- 10. Ziqi Cao (Beijing Institute of Technology)
- 11. Kaisen Jin (Beijing Institute of Technology)
- 12. Chi Zhang (Beijing Institute of Technology)
- 13. Yuqing Jiang (Beijing Institute of Technology)
- 14. Yuanfang Zhang (Beijing Institute of Technology)
- 15. Yuping Wang (Beijing Institute of Technology)
- 16. Ye Yuan (Beijing Institute of Technology)
- 17. Guoren Wang (Beijing Institute of Technology)
- 18. Nan Tang (Hong Kong University of Science and Technology)
BibTeX Citation
@article{deng_vldb24,
title = {{LakeBench: A Benchmark for Discovering Joinable and Unionable Tables in Data Lakes}},
author = {Deng, Yuhao and Chai, Chengliang and Cao, Lei and Yuan, Qin and Chen, Siyuan and Yu, Yanrui and Sun, Zhaoze and Wang, Junyi and Li, Jiajun and Cao, Ziqi and Jin, Kaisen and Zhang, Chi and Jiang, Yuqing and Zhang, Yuanfang and Wang, Yuping and Yuan, Ye and Wang, Guoren and Tang, Nan},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {8},
pages = {1925--1938},
doi = {10.14778/3659437.3659448},
url = {https://doi.org/10.14778/3659437.3659448},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 7,613 | LakeCompass: An End-to-End System for Data Maintenance, Search and Analysis in Data Lakes | 2024 | VLDB | 5.5817761e-05 |
| 10,399 | Retrieve-and-Verify: A Table Context Selection Framework for Accurate Column Annotations | 2026 | SIGMOD | 5.093636e-05 |
| 10,486 | Qualitative Join Discovery in Data Lakes using Examples | 2026 | SIGMOD | 5.093636e-05 |
| 10,850 | Birdie: Natural Language-Driven Table Discovery Using Differentiable Search Index | 2025 | VLDB | 5.093636e-05 |
| 10,933 | LakeVisage: Towards Scalable, Flexible and Interactive Visualization Recommendation for Data Discovery over Data Lakes | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 24 of 24 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 10,933 | LakeVisage: Towards Scalable, Flexible and Interactive Visualization Recommendation for Data Discovery over Data Lakes | 2025 | VLDB |
| 2 | 2,315 | SANTOS: Relationship-based Semantic Table Union Search | 2023 | SIGMOD |
| 3 | 1,303 | Finding Related Tables in Data Lakes for Interactive Data Science | 2020 | SIGMOD |
| 4 | 3,073 | DeepJoin: Joinable Table Discovery with Pre-trained Language Models | 2023 | VLDB |
| 5 | 2,192 | Semantics-aware Dataset Discovery from Data Lakes with Contextualized Column-based Representation Learning | 2023 | VLDB |
| 6 | 878 | Table Union Search on Open Data | 2018 | VLDB |
| 7 | 11,270 | Searching Data Lakes for Nested and Joined Data | 2024 | VLDB |
| 8 | 4,123 | Integrating Data Lake Tables | 2023 | VLDB |
| 9 | 10,486 | Qualitative Join Discovery in Data Lakes using Examples | 2026 | SIGMOD |
| 10 | 7,613 | LakeCompass: An End-to-End System for Data Maintenance, Search and Analysis in Data Lakes | 2024 | VLDB |