SILKMOTH: An Efficient Method for Finding Related Sets with Maximum Matching Constraints
Summary: SILKMOTH efficiently discovers set pairs related by similarity-weighted maximum matching, while preserving exact brute-force results. Signature-based pruning, novel filters, and triangle-inequality acceleration yield an order-of-magnitude speedup across broad similarity metrics. (summarized by gpt-5.6-luna on Jul 24 2026)
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Authors
- 1. Dong Deng (Massachusetts Institute of Technology)
- 2. Albert Kim (Massachusetts Institute of Technology)
- 3. Samuel Madden (Massachusetts Institute of Technology)
- 4. Michael Stonebraker (Massachusetts Institute of Technology)
BibTeX Citation
@article{deng_vldb17,
title = {{SILKMOTH: An Efficient Method for Finding Related Sets with Maximum Matching Constraints}},
author = {Deng, Dong and Kim, Albert and Madden, Samuel and Stonebraker, Michael},
journal = {PVLDB},
series = {{VLDB} '17},
volume = {10},
number = {10},
pages = {1082--1093},
doi = {10.14778/3115404.3115413},
url = {https://doi.org/10.14778/3115404.3115413},
year = {2017}
}
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