Dynamic Pruning for Recursive Joins
Summary: Dynamic pruning for recursive joins generates runtime filters per step, enabling deeper joins with fewer scanned tuples. Prototype evaluations show up to 135.6x speedups vs baseline and 3.99x vs state-of-the-art pruning, with negligible per-step overhead. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Norifumi Nishikawa (Hitachi)
- 2. Akira Shimizu (Hitachi)
- 3. Akira Ito (Hitachi)
- 4. Shinji Fujiwara (Hitachi)
- 5. Yuto Hayamizu (University of Tokyo)
- 6. Masaru Kitsuregawa (University of Tokyo)
- 7. Kazuo Goda (Hitachi)
BibTeX Citation
@inproceedings{nishikawa_sigmod25,
title = {{Dynamic Pruning for Recursive Joins}},
author = {Nishikawa, Norifumi and Shimizu, Akira and Ito, Akira and Fujiwara, Shinji and Hayamizu, Yuto and Kitsuregawa, Masaru and Goda, Kazuo},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3722212.3724434},
url = {https://dl.acm.org/doi/10.1145/3722212.3724434},
year = {2025}
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