GooseDB: A Database Engine that Optimally Refines Top-k Queries to Satisfy Representation Constraints
Summary: GooseDB combines DuckDB with an MILP solver to synthesize minimally modified SQL queries that enforce representation (group-count) constraints on top-k outputs given constraints and user modification preferences. Novelty: supports broad edits to selection predicates and scoring functions and is the first system to jointly optimize both (and across multiple k) for minimal-change refinements, substantially generalizing prior work. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Zixuan Chen (Northeastern University)
- 2. Jinyang Li (University of Michigan)
- 3. H. V. Jagadish (University of Michigan)
- 4. Mirek Riedewald (Northeastern University)
BibTeX Citation
@article{chen_vldb25,
title = {{GooseDB: A Database Engine that Optimally Refines Top-k Queries to Satisfy Representation Constraints}},
author = {Chen, Zixuan and Li, Jinyang and Jagadish, H. V. and Riedewald, Mirek},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5351--5354},
doi = {10.14778/3750601.3750669},
url = {https://doi.org/10.14778/3750601.3750669},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 0 of 0 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|
Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 103 | DuckDB: an Embeddable Analytical Database | 2019 | SIGMOD | 0.00034161428 |
| 1,597 | Designing Fair Ranking Schemes | 2019 | SIGMOD | 0.00010246472 |
| 5,278 | Why Not Yet: Fixing a Top-k Ranking that Is Not Fair to Individuals | 2023 | VLDB | 6.2856584e-05 |
| 6,293 | Query Refinement for Diversity Constraint Satisfaction | 2024 | VLDB | 5.9247942e-05 |
| 6,389 | Query Refinement for Diverse Top-k Selection | 2024 | SIGMOD | 5.8895166e-05 |
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 5,906 | Approximate Query Engines: Commercial Challenges and Research Opportunities | 2017 | SIGMOD |
| 2 | 103 | DuckDB: an Embeddable Analytical Database | 2019 | SIGMOD |
| 3 | 5,965 | Answering Top-k Representative Queries on Graph Databases | 2014 | SIGMOD |
| 4 | 7,685 | Beyond Quacking: Deep Integration of Language Models and RAG into DuckDB | 2025 | VLDB |
| 5 | 8,867 | Generating Flexible Workloads for Graph Databases | 2016 | VLDB |
| 6 | 9,559 | Rodeo: Making Refinements for Diverse Top-k Queries | 2024 | VLDB |
| 7 | 6,999 | DuckPGQ: Efficient Property Graph Queries in an analytical RDBMS | 2023 | CIDR |
| 8 | 10,576 | Efficient Query Repair for Aggregate Constraints | 2026 | VLDB |
| 9 | 12,338 | Demonstration of the FDB Query Engine for Factorised Databases | 2012 | VLDB |
| 10 | 6,389 | Query Refinement for Diverse Top-k Selection | 2024 | SIGMOD |