Similarity Joins of Sparse Features
Summary: Introduces FastScalableSparseJoiner (FSSJ) for sparse-feature similarity joins. Uses quasi-prefix filtering that exploits element-skew to prune candidates and avoid broadcasting, enabling load-balanced, shared-nothing execution. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Ahmed Metwally (Uber Technologies, Inc.)
- 2. Michael Shum (Massachusetts Institute of Technology)
BibTeX Citation
@inproceedings{metwally_sigmod24,
title = {{Similarity Joins of Sparse Features}},
author = {Metwally, Ahmed and Shum, Michael},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3626246.3653370},
url = {https://dl.acm.org/doi/10.1145/3626246.3653370},
year = {2024}
}
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