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Efficiently Answering Durability Prediction Queries

Summary: Proposes MLSS for durability queries, enabling efficient stepwise simulation on complex/black-box models. Uses importance splitting to bias toward promising prefixes, automates splitter design, preserving unbiased MC estimates with guarantees. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
6257
Venue
SIGMOD
Year
2021
Pagerank
5.7822533e-05
Overall Rank
6,761 | 53.62%
DOI
10.1145/3448016.3457305

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@inproceedings{gao_sigmod21,
        title = {{Efficiently Answering Durability Prediction Queries}},
        author = {Gao, Junyang and Xu, Yifan and Agarwal, Pankaj K. and Yang, Jun},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457305},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457305},
        year = {2021}
}

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