Discovering Approximate Inclusion Dependencies
Summary: First comprehensive study and algorithm for approximate inclusion dependencies (AINDs), introducing deletion semantics complementary to prior insertion semantics. Partition-aware violation quantification plus a novel three-layer filtering and pruning pipeline enable out-of-core AIND discovery that often avoids full scans and outperforms prior methods on dirty data. (summarized by gpt-5-mini on Feb 09 2026)
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Authors
- 1. Qingdong Su
- 2. Zhikang Wang
- 3. Zijing Tan
- 4. Shuai Ma
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