Cardinality Estimation for Similarity Search on High-Dimensional Data Objects: The Impact of Reference Objects
Summary: Leverages selected 'reference objects' to perform cardinality estimation for high‑dimensional similarity search, targeting data/query robustness and efficiency. Presents two reference-selection/usage strategies with dynamic DB support, achieving up to ~10× speedup and ~136× lower mean Q‑error. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Hai Lan (Royal Melbourne Institute of Technology)
- 2. Shixun Huang (University of Wollongong)
- 3. Zhifeng Bao (Royal Melbourne Institute of Technology)
- 4. Renata Borovica-Gajic (University of Melbourne)
BibTeX Citation
@article{lan_vldb25,
title = {{Cardinality Estimation for Similarity Search on High-Dimensional Data Objects: The Impact of Reference Objects}},
author = {Lan, Hai and Huang, Shixun and Bao, Zhifeng and Borovica-Gajic, Renata},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {3},
pages = {544--556},
doi = {10.14778/3712221.3712224},
url = {https://doi.org/10.14778/3712221.3712224},
year = {2025}
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,508 | Practical Parameterized Query Optimization via Efficient Plan Reuse and List-wise Ranking | 2026 | SIGMOD | 5.093636e-05 |
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