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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)

Paper ID
7029
Venue
SIGMOD
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,195 | 23.20%
DOI
10.1145/3677137

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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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