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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)

Paper ID
7390
Venue
SIGMOD
Year
2026
Pagerank
4.1945683e-05
Overall Rank
10,081 | 29.87%
DOI
10.1145/3769794

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