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)
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Authors
- 1. Jingyu Zhu (Nankai University)
- 2. Yu Sun (Nankai University)
- 3. Shaoxu Song (Tsinghua University)
- 4. Xiaojie Yuan (Nankai University)
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}
}
Incoming Citations (Sorted by Pagerank)
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| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,372 | From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies | 2026 | SIGMOD | 5.093636e-05 |
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