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Evaluating Top-k Queries with Inconsistency Degrees

Summary: Proposes inconsistency-aware top-k evaluation under denial constraints, with two inconsistency measures (single/multi) quantified by why-provenance and provenance polynomials. Presents a top-k algorithm for monotone/non-monotone scoring, with gains over full ranking. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12293
Venue
VLDB
Year
2020
Pagerank
5.4813895e-05
Overall Rank
8,133 | 44.21%
DOI
10.14778/3407790.3407815

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{issa_vldb20,
        title = {{Evaluating Top-k Queries with Inconsistency Degrees}},
        author = {Issa, Ousmane and Bonifati, Angela and Toumani, Farouk},
        journal = {PVLDB},
        series = {{VLDB} '20},
        volume = {13},
        number = {11},
        pages = {2146--2158},
        doi = {10.14778/3407790.3407815},
        url = {https://doi.org/10.14778/3407790.3407815},
        year = {2020}
}

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank Citing Paper Year Venue Pagerank
5,611 Fast Algorithms for Denial Constraint Discovery 2023 VLDB 6.1504635e-05
7,396 Fast Detection of Denial Constraint Violations 2022 VLDB 5.6257228e-05
10,818 Evaluating Continuous Queries with Inconsistency Annotations 2025 VLDB 5.093636e-05
11,660 INCA: Inconsistency-Aware Data Profiling and Querying 2021 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

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

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