{"ID":5938050,"CreatedAt":"2026-07-07T03:14:33.014478982Z","UpdatedAt":"2026-07-07T21:13:45.234837168Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.04030","arxiv_id":"2607.04030","title":"Efficient Discovery of Conditional Dependencies with Desbordante","abstract":"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. We study the CFD discovery problem, which is computationally demanding. We build on the state-of-the-art CFDFinder algorithm and introduce a set of algorithmic and engineering improvements, including a parallelization strategy, to produce ParCFDFinder. 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. 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 Java-based implementation of Metanome. Integrating ParCFDFinder into Desbordante makes it possible, for the first time, to conveniently discover CFDs on datasets with hundreds of thousands of rows on a commodity machine within a reasonable time.","short_abstract":"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. We study the CFD discovery problem, which is c...","url_abs":"https://arxiv.org/abs/2607.04030","url_pdf":"https://arxiv.org/pdf/2607.04030v1","authors":"[\"Ivan Kozhukov\",\"Dmitry Fedoseev\",\"Maksim Emelyanov\",\"Artem Smola\",\"Pyotr Senichenkov\",\"Pavel Anosov\",\"George Chernishev\"]","published":"2026-07-04T21:15:59Z","proceeding":"cs.DB","tasks":"[\"cs.DB\",\"cs.AI\",\"cs.DC\",\"cs.LG\",\"cs.PF\"]","methods":"[]","has_code":false}