Extensible and Robust Evaluation of Similarity Queries
Summary: Fast, a similarity-join system, introduces "reductions" that map complex domains/distance functions into simpler ones and builds a reduction graph to enumerate extensible query plans. Using runtime partitioning and sampling-based plan selection (no cost models), plus hybrid index-on-the-fly and caching, Fast robustly attains near-optimal performance across datasets and distance functions. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Daniel Schmitt (University of Salzburg)
- 2. Thomas Hütter (Software Competence Center Hagenberg)
- 3. Nikolaus Augsten (University of Salzburg)
BibTeX Citation
@article{schmitt_vldb25,
title = {{Extensible and Robust Evaluation of Similarity Queries}},
author = {Schmitt, Daniel and Hütter, Thomas and Augsten, Nikolaus},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {11},
pages = {3868--3882},
doi = {10.14778/3749646.3749660},
url = {https://doi.org/10.14778/3749646.3749660},
year = {2025}
}
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