HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries
Summary: HypDB is the first end-to-end system for detecting, explaining, and resolving bias in OLAP queries, including Simpson’s paradox. It identifies root causes, exposes domain/data-collection effects, and rewrites queries to produce less biased decision-support insights. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Babak Salimi (University of Washington)
- 2. Corey Cole (University of Washington)
- 3. Peter Li (University of Washington)
- 4. Johannes Gehrke (Microsoft)
- 5. Dan Suciu (University of Washington)
BibTeX Citation
@article{salimi_vldb18,
title = {{HypDB: A Demonstration of Detecting, Explaining and Resolving Bias in OLAP queries}},
author = {Salimi, Babak and Cole, Corey and Li, Peter and Gehrke, Johannes and Suciu, Dan},
journal = {PVLDB},
series = {{VLDB} '18},
volume = {11},
number = {12},
pages = {2062--2065},
doi = {10.14778/3229863.3236260},
url = {https://doi.org/10.14778/3229863.3236260},
year = {2018}
}Incoming Citations (Sorted by Pagerank)
Showing 7 of 7 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,584 | Complaint-driven Training Data Debugging for Query 2.0 | 2020 | SIGMOD | 8.3783546e-05 |
| 3,000 | Explaining Black-Box Algorithms Using Probabilistic Contrastive Counterfactuals | 2021 | SIGMOD | 7.8677069e-05 |
| 3,004 | SCODED: Statistical Constraint Oriented Data Error Detection | 2020 | SIGMOD | 7.8608629e-05 |
| 5,054 | Explainable AI: Foundations, Applications, Opportunities for Data Management Research | 2022 | SIGMOD | 6.3843089e-05 |
| 6,105 | Optimizing In-memory Database Engine for AI-powered On-line Decision Augmentation Using Persistent Memory | 2021 | VLDB | 5.9743349e-05 |
| 10,979 | Finding Convincing Views to Endorse a Claim | 2025 | VLDB | 5.093636e-05 |
| 11,170 | Counterfactual Explanation at Will, with Zero Privacy Leakage | 2024 | SIGMOD | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 3 of 3 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 2,546 | Bias in OLAP Queries: Detection, Explanation, and Removal (Or Think Twice About Your AVG-Query) | 2018 | SIGMOD | 8.4340413e-05 |
| 2,840 | Towards Sustainable Insights or why polygamy is bad for you | 2017 | CIDR | 8.0650295e-05 |
| 8,415 | ZaliQL: Causal Inference from Observational Data at Scale | 2017 | VLDB | 5.4301705e-05 |
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