From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies
Summary: Joint spatio-temporal repair framework that leverages spatial consistency and temporal pattern similarity to avoid mislabeling simultaneous real events as errors; formalizes constrained optimal repair and proves NP-hardness. Provides an exact decomposition with pruning and two approximation algorithms (probabilistic optimality guarantees and a greedy sliding-window for efficiency), validated on nine real-world datasets with superior empirical performance. (summarized by gpt-5-mini on Feb 11 2026)
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Authors
- 1. Weiwei Deng (Nankai University)
- 2. Yu Sun (Nankai University)
- 3. Shaoxu Song (Tsinghua University)
- 4. Xiaojie Yuan (Nankai University)
BibTeX Citation
@inproceedings{deng_sigmod26,
title = {{From Suspicious Errors to Valid Data: On Repairing Spatio-Temporal Data via Spatial and Temporal Dependencies}},
author = {Deng, Weiwei and Sun, Yu and Song, Shaoxu and Yuan, Xiaojie},
series = {{SIGMOD} '26},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3769794},
url = {https://dl.acm.org/doi/10.1145/3769794},
year = {2026}
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