Similarity Query Processing for High-Dimensional Data
Summary: Tutorial surveying high-dimensional similarity query processing, bridging DB and ML with embeddings, auto-encoders, and pre-trained models. Reviews exact and approximate methods (cover trees, LSH, product quantization, proximity graphs), ML-driven selectivity estimation, and DB–ML synergy to spur ML-for-DB and DB-for-ML solutions. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Jianbin Qin (Shenzhen University)
- 2. Wei Wang (University of New South Wales)
- 3. Chuan Xiao (Nagoya University; Osaka University)
- 4. Ying Zhang (University of Technology Sydney)
BibTeX Citation
@article{qin_vldb20,
title = {{Similarity Query Processing for High-Dimensional Data}},
author = {Qin, Jianbin and Wang, Wei and Xiao, Chuan and Zhang, Ying},
journal = {PVLDB},
series = {{VLDB} '20},
volume = {13},
number = {12},
pages = {3437--3440},
doi = {10.14778/3415478.3415564},
url = {https://doi.org/10.14778/3415478.3415564},
year = {2020}
}
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