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)
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Authors
- 1. Mintaek Lim (Seoul National University)
- 2. Dogeun Kim (Seoul National University)
- 3. Minwoo Kim (Seoul National University)
- 4. Jaeyoung Do (Seoul National University)
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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