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
- 2. Yu Sun
- 3. Shaoxu Song
- 4. Xiaojie Yuan
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