Factorized and Vectorized Execution: Optimizing Analytical and Semantic Queries over Relations
Summary: FFX combines arbitrary factorization with fully vectorized, pipelined execution via packed factorized vectors, avoiding join-result explosion without sacrificing cache locality. It also compacts factorized intermediates into LLM prompts, reducing token and inference costs. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Sunny Yasser (Polytechnique Montréal)
- 2. Anas Dorbani (Polytechnique Montréal)
- 3. Amine Mhedhbi (Polytechnique Montréal)
BibTeX Citation
@inproceedings{yasser_sigmod26,
title = {{Factorized and Vectorized Execution: Optimizing Analytical and Semantic Queries over Relations}},
author = {Yasser, Sunny and Dorbani, Anas and Mhedhbi, Amine},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802055},
url = {https://dl.acm.org/doi/10.1145/3802055},
year = {2026}
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