TOP: A Framework for Enabling Algorithmic Optimizations for Distance-Related Problems
Summary: TOP unifies distance-related algorithms under a generic abstraction and derives seven principles for sound triangle-inequality pruning. Its compiler-like framework automatically generates optimized implementations, matching or surpassing hand-tuned methods, with up to 237× (2.5× average) speedups. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Yufei Ding (North Carolina State University)
- 2. Xipeng Shen (North Carolina State University)
- 3. Madanlal Musuvathi (Microsoft)
- 4. Todd Mytkowicz (Microsoft)
BibTeX Citation
@article{ding_vldb15,
title = {{TOP: A Framework for Enabling Algorithmic Optimizations for Distance-Related Problems}},
author = {Ding, Yufei and Shen, Xipeng and Musuvathi, Madanlal and Mytkowicz, Todd},
journal = {PVLDB},
series = {{VLDB} '15},
volume = {8},
number = {10},
pages = {1046--1057},
doi = {10.14778/2794367.2794371},
url = {https://doi.org/10.14778/2794367.2794371},
year = {2015}
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|---|---|---|---|---|
| 2,137 | Efficient Processing of k Nearest Neighbor Joins using MapReduce | 2012 | VLDB | 9.110238e-05 |
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