Mitigating the Impedance Mismatch between Prediction Query Execution and Database Engine
Summary: Prediction-aware operator with inference-context reuse and batched invocation to reduce ML UDF impedance in DB engines. IMBridge on OceanBase enables one-off inference context setup and batched invocation, gives 71.4x speedup for prediction queries. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Chenyang Zhang (East China Normal University)
- 2. Junxiong Peng (East China Normal University)
- 3. Chen Xu (East China Normal University)
- 4. Quanqing Xu (Ant Financial)
- 5. Chuanhui Yang (Ant Financial)
BibTeX Citation
@inproceedings{zhang_sigmod25,
title = {{Mitigating the Impedance Mismatch between Prediction Query Execution and Database Engine}},
author = {Zhang, Chenyang and Peng, Junxiong and Xu, Chen and Xu, Quanqing and Yang, Chuanhui},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3725326},
url = {https://dl.acm.org/doi/10.1145/3725326},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,230 | Eliminating Redundant Feature Tests in Decision Tree and Random Forest Inference on SQL Predicates | 2026 | SIGMOD | 5.093636e-05 |
| 10,232 | EncoderForge: Generating Efficient SQL for Encoders in Machine Learning Inference Pipelines | 2026 | SIGMOD | 5.093636e-05 |
| 10,386 | NeurStore: Efficient In-database Deep Learning Model Management System | 2026 | SIGMOD | 5.093636e-05 |
| 10,466 | InferF: Declarative Factorization of AI/ML Inferences over Joins | 2026 | SIGMOD | 5.093636e-05 |
| 10,531 | TPCx-AI under the Microscope: A Benchmarking Debt Analysis | 2026 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 43 of 43 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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