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LIMA: Denial Constraint Discovery in Large Dynamic Databases

Summary: LIMA continuously discovers and maintains denial constraints over large, evolving databases, rather than recomputing them on demand. Its scalable, low-overhead approach makes practical metadata inference possible for expressive rules encompassing FDs, keys, and order dependencies. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID
hb48156b05604dd47
Venue
VLDB
Year
2026
Pagerank
4.9769913e-05
Overall Rank
11,015 | 25.97%
DOI
10.14778/3827998.3828116
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BibTeX Citation

@article{martin_vldb26,
        title = {{LIMA: Denial Constraint Discovery in Large Dynamic Databases}},
        author = {Martin, Albert and de Almeida, Eduardo C. and Romero, Oscar and Queralt, Anna},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {12},
        pages = {4762--4765},
        doi = {10.14778/3827998.3828116},
        url = {https://doi.org/10.14778/3827998.3828116},
        year = {2026}
}

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Showing 6 of 6 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
350 Discovering Denial Constraints 2013 VLDB 0.00020244085
1,972 Discovery of Approximate (and Exact) Denial Constraints 2020 VLDB 9.2829634e-05
4,070 Fast Algorithms for Denial Constraint Discovery 2023 VLDB 6.8183785e-05
5,283 Fast Approximate Denial Constraint Discovery 2023 VLDB 6.1963542e-05
10,781 Discovering Approximate Denial Constraints in Large Databases 2026 VLDB 4.9769913e-05
11,327 How and Why False Denial Constraints are Discovered 2025 VLDB 4.9769913e-05
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