NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments & Analysis]
Summary: NeurBench benchmarks learned database components under controllable, measurable data and workload drift via a unified drift factor. Its drift-aware generators preserve correlations, enabling realistic cross-scenario robustness analysis beyond fixed drift settings. (summarized by gpt-5.6-luna on Jul 26 2026)
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Authors
- 1. Zhanhao Zhao (National University of Singapore)
- 2. Haotian Gao (National University of Singapore)
- 3. Naili Xing (National University of Singapore)
- 4. Lingze Zeng (National University of Singapore)
- 5. Meihui Zhang (Beijing Institute of Technology)
- 6. Gang Chen (Zhejiang University)
- 7. Manuel Rigger (National University of Singapore)
- 8. Beng Chin Ooi (Zhejiang University)
BibTeX Citation
@inproceedings{zhao_sigmod26,
title = {{NeurBench: A Benchmark Suite for Learned Database Components with Drift Modeling: [Experiments \& Analysis]}},
author = {Zhao, Zhanhao and Gao, Haotian and Xing, Naili and Zeng, Lingze and Zhang, Meihui and Chen, Gang and Rigger, Manuel and Ooi, Beng Chin},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3802091},
url = {https://dl.acm.org/doi/10.1145/3802091},
year = {2026}
}
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