InferF: Declarative Factorization of AI/ML Inferences over Joins
Summary: Declarative factorized inference over multi-way joins: push partial ML subcomputations to join-tree nodes to cut redundant inference and join cost. InferF formalizes plan selection for arbitrary analyzable inference expressions and uses greedy/genetic search; up to 11.3x speedup on Velox. (summarized by gpt-5-mini on Apr 11 2026)
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Authors
- 1. Kanchan Chowdhury (Arizona State University; Marquette University)
- 2. Lixi Zhou (Arizona State University)
- 3. Lulu Xie (Arizona State University)
- 4. Xinwei Fu (Amazon)
- 5. Jia Zou (Arizona State University)
BibTeX Citation
@inproceedings{chowdhury_sigmod26,
title = {{InferF: Declarative Factorization of AI/ML Inferences over Joins}},
author = {Chowdhury, Kanchan and Zhou, Lixi and Xie, Lulu and Fu, Xinwei and Zou, Jia},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3786662},
url = {https://dl.acm.org/doi/10.1145/3786662},
year = {2026}
}
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