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Matching dependencies (MDs) are recently proposed for various data quality applications such as detecting the violation of integrity constraints and duplicate object identification. In this paper, we study the problem of discovering matching dependencies for a given database instance. First, we formally define the measures, support and confidence, for evaluating the utility of MDs in the given database instance. Then, we study the discovery of MDs with certain utility requirements of support and confidence. Exact algorithms are developed, together with pruning strategies to improve the time performance. Finally, our experimental evaluation demonstrates the efficiency of the proposed methods.
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Stored only when both papers are in the corpus — 52 citations exist upstream.
Papers that cite the same references — the relational fallback while embeddings are still backfilling.
Imported from OpenAlex (CC0) — nothing here is generated.