Efficient Discovery of Conditional Dependencies with Desbordante
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...
| Authors | Ivan Kozhukov, Dmitry Fedoseev, Maksim Emelyanov et al. |
|---|---|
| Primary category | cs.DB |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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