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Advancing Fact Attribution for Query Answering: Aggregate Queries and Novel Algorithms

Summary: First practical method to compute Banzhaf and Shapley contributions for aggregate queries (beyond SPJUU). Two optimizations—grouping equal-contribution tuples and using lineage gradients to amortize cost—yield up to 1000× speedups and scale to million-instance tests. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID
14207
Venue
VLDB
Year
2025
Pagerank
5.3847009e-05
Overall Rank
8,688 | 40.40%
DOI
10.14778/3749646.3749670

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{abramovich_vldb25,
        title = {{Advancing Fact Attribution for Query Answering: Aggregate Queries and Novel Algorithms}},
        author = {Abramovich, Omer and Deutch, Daniel and Frost, Nave and Kara, Ahmet and Olteanu, Dan},
        journal = {PVLDB},
        series = {{VLDB} '25},
        volume = {18},
        number = {11},
        pages = {3996--4008},
        doi = {10.14778/3749646.3749670},
        url = {https://doi.org/10.14778/3749646.3749670},
        year = {2025}
}

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank Citing Paper Year Venue Pagerank
10,183 Tractability Frontiers of the Shapley Value for Aggregate Conjunctive Queries 2026 PODS 5.093636e-05
10,429 Analyzing Deviations from Monotonic Trends through Database Repair 2026 SIGMOD 5.093636e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 11 of 11 cited papers.

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

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