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High Precision ≠ High Cost: Temporal Data Fusion for Multiple Low-Precision Sensors

Summary: Temporal fusion from multiple cheap, low-precision sensors via per-timestamp observation selection, not weighted averaging. ML-guided local trend model; NP-hard optimal fusion, DP exact algorithms, plus approximation with guarantees, robust to outliers. (summarized by gpt-5.4-mini on May 24 2026)

Paper ID
6970
Venue
SIGMOD
Year
2024
Pagerank
5.093636e-05
Overall Rank
11,179 | 23.31%
DOI
10.1145/3654946

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BibTeX Citation

@inproceedings{zhu_sigmod24,
        title = {{High Precision ≠ High Cost: Temporal Data Fusion for Multiple Low-Precision Sensors}},
        author = {Zhu, Jingyu and Sun, Yu and Song, Shaoxu and Yuan, Xiaojie},
        series = {{SIGMOD} '24},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3654946},
        url = {https://dl.acm.org/doi/10.1145/3654946},
        year = {2024}
}

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