End-to-end Optimization of Machine Learning Prediction Queries
Summary: Raven unifies data processing and ML inference in one IR/graph to optimize prediction queries. Data-driven runtime selection and logical-to-physical transformations span CPU/GPU and ML/DNN backends, delivering up to 13x on Spark and 330x on SQL Server. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Kwanghyun Park (Microsoft)
- 2. Karla Saur (Microsoft)
- 3. Dalitso Banda (Microsoft)
- 4. Rathijit Sen (Microsoft)
- 5. Matteo Interlandi (Microsoft)
- 6. Konstantinos Karanasos (Microsoft)
BibTeX Citation
@inproceedings{park_sigmod22,
title = {{End-to-end Optimization of Machine Learning Prediction Queries}},
author = {Park, Kwanghyun and Saur, Karla and Banda, Dalitso and Sen, Rathijit and Interlandi, Matteo and Karanasos, Konstantinos},
series = {{SIGMOD} '22},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3514221.3526141},
url = {https://dl.acm.org/doi/10.1145/3514221.3526141},
year = {2022}
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