Rapidash: Efficient Detection of Constraint Violations
Summary: Reduces denial-constraint (DC) violation detection to orthogonal range search and introduces Rapidash, achieving near-linear time and space (vs prior quadratic worst-case). Validations on large open-source and production datasets show up to 84× speedups and markedly better scalability. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zifan Liu (University of Wisconsin)
- 2. Shaleen Deep (Microsoft)
- 3. Anna Fariha (University of Utah)
- 4. Fotis Psallidas (Microsoft)
- 5. Ashish Tiwari (Microsoft)
- 6. Avrilia Floratou (Microsoft)
BibTeX Citation
@article{liu_vldb24,
title = {{Rapidash: Efficient Detection of Constraint Violations}},
author = {Liu, Zifan and Deep, Shaleen and Fariha, Anna and Psallidas, Fotis and Tiwari, Ashish and Floratou, Avrilia},
journal = {PVLDB},
series = {{VLDB} '24},
volume = {17},
number = {8},
pages = {2009--2021},
doi = {10.14778/3659437.3659454},
url = {https://doi.org/10.14778/3659437.3659454},
year = {2024}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,623 | Incremental Detection of Denial Constraint Violations | 2025 | VLDB | 5.2434488e-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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