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Revisiting Filtered ANN Benchmarks: A Hardness-Controlled Benchmark Generator for Realistic Evaluation

Summary: Introduces α-Hardness, an execution-driven FANN query metric based on overfetching that predicts performance more reliably than selectivity or attribute correlation. HCBGen controls hardness to generate realistic, privacy-preserving workloads and expose gaps hidden by easy benchmarks. (summarized by gpt-5.6-luna on Aug 28 2026)

Paper ID
h7bfe7ef3804c09d2
Venue
VLDB
Year
2026
Pagerank
4.9793485e-05
Overall Rank
10,823 | 27.24%
DOI
10.14778/3828612.3828630

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BibTeX Citation

@article{lim_vldb26,
        title = {{Revisiting Filtered ANN Benchmarks: A Hardness-Controlled Benchmark Generator for Realistic Evaluation}},
        author = {Lim, Mintaek and Kim, Dogeun and Kim, Minwoo and Do, Jaeyoung},
        journal = {PVLDB},
        series = {{VLDB} '26},
        volume = {19},
        number = {10},
        pages = {2763--2776},
        doi = {10.14778/3828612.3828630},
        url = {https://doi.org/10.14778/3828612.3828630},
        year = {2026}
}

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