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 (Fudan University)
- 2. Zhikang Wang (Fudan University)
- 3. Zijing Tan (Fudan University)
- 4. Shuai Ma (Beihang University)
BibTeX Citation
@article{su_vldb25,
title = {{Discovering Approximate Inclusion Dependencies}},
author = {Su, Qingdong and Wang, Zhikang and Tan, Zijing and Ma, Shuai},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {4},
pages = {1210--1222},
doi = {10.14778/3717755.3717777},
url = {https://doi.org/10.14778/3717755.3717777},
year = {2025}
}
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