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Our implementation is integrated into Desbordante - a high-performance open-source data profiler written in C++ that exposes a Python interface, enabling CFD discovery to be invoked from any Python program.\n  Experimental results show that our enhancements speed up the algorithm by up to $318\\times$ ($118\\times$ on average) and reduce memory usage by up to $23\\times$ ($14\\times$ on average) compared with the existing","title":"Efficient Discovery of Conditional Dependencies with Desbordante","url":"https://arxiv.org/abs/2607.04030","vendor":"arxiv_cs_ai"},"summary":"arXiv:2607.04030v1 Announce Type: cross \nAbstract: Conditional functional dependencies (CFDs) are functional dependencies with a restricted scope: they specify the context in which a dependency holds and are useful for data-quality tasks, specifying complex integrity constraints, and extracting valuable insights from data.\n  We study the CFD discovery problem, which is computationally demanding. 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