On the Feasibility and Benefits of Extensive Evaluation
Summary: Examines the feasibility and benefits of extensive evaluation in data management via incremental sampling and ANOVA-based prediction to approximate full parameter sweeps. Finds mixed predictability: some systems need few samples, others not; random sampling + ANOVA often matches full results, with guidance to improve artifact predictability and sampling strategies. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Yujie Hui (Ohio State University)
- 2. Miao Yu (Ohio State University)
- 3. Hao Qi (University of California Merced)
- 4. Yifan Gan (Ohio State University)
- 5. Tianxi Li (Ohio State University)
- 6. Yuke Li (University of California Merced)
- 7. Xueyuan Ren (Ohio State University)
- 8. Sixiang Ma (Ohio State University)
- 9. Xiaoyi Lu (University of California Merced)
- 10. Yang Wang (Ohio State University)
BibTeX Citation
@inproceedings{hui_sigmod24,
title = {{On the Feasibility and Benefits of Extensive Evaluation}},
author = {Hui, Yujie and Yu, Miao and Qi, Hao and Gan, Yifan and Li, Tianxi and Li, Yuke and Ren, Xueyuan and Ma, Sixiang and Lu, Xiaoyi and Wang, Yang},
series = {{SIGMOD} '24},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3677137},
url = {https://dl.acm.org/doi/10.1145/3677137},
year = {2024}
}
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