Sample Debiasing in the Themis Open World Database System
Summary: Themis is the first open-world DB that rebalances biased samples to approximate population-wide query results. It blends sample reweighting with Bayesian nets, using a priori population info to beat AQP and baselines with latency and robustness to gaps. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Laurel Orr (University of Washington)
- 2. Magdalena Balazinska (University of Washington)
- 3. Dan Suciu (University of Washington)
BibTeX Citation
@inproceedings{orr_sigmod20,
title = {{Sample Debiasing in the Themis Open World Database System}},
author = {Orr, Laurel and Balazinska, Magdalena and Suciu, Dan},
series = {{SIGMOD} '20},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3318464.3380606},
url = {https://dl.acm.org/doi/10.1145/3318464.3380606},
year = {2020}
}
Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 5,510 | Responsible Data Integration: Next-generation Challenges | 2022 | SIGMOD | 6.1919207e-05 |
| 6,262 | Mosaic: A Sample-Based Database System for Open World Query Processing | 2020 | CIDR | 5.9361728e-05 |
| 7,556 | ReStore - Neural Data Completion for Relational Databases | 2021 | SIGMOD | 5.5997742e-05 |
| 7,838 | Consistent Range Approximation for Fair Predictive Modeling | 2023 | VLDB | 5.5343411e-05 |
| 8,851 | Memory-Efficient and Flexible Detection of Heavy Hitters in High-Speed Networks | 2023 | SIGMOD | 5.357707e-05 |
| 10,831 | Holistic query Approximation via RL Modeling | 2025 | VLDB | 5.093636e-05 |
| 11,063 | New Trends in Data Forgetting for Sustainable Data Management | 2025 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 17 of 17 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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