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Desbordante data profiler enhances discovery of complex dependencies · 3 sources tracked

By PulseAugur Editorial · [4 sources] ·

Researchers have developed Desbordante, an open-source data profiler implemented in C++, to efficiently discover inclusion dependencies and graph functional dependencies (GFDs). The tool incorporates optimizations such as parallelization, data buffering, and specialized hash-table selection to significantly improve performance. Desbordante has demonstrated up to an 8x speedup for inclusion dependency discovery and a 3x improvement for GFD validation compared to existing methods, making these complex data analysis tasks more accessible on consumer hardware. AI

IMPACT Enables more efficient data analysis and pattern discovery, potentially improving downstream AI tasks that rely on clean, structured data.

RANK_REASON The cluster describes new algorithms and implementations for discovering data dependencies, published in academic venues (arXiv).

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 4 sources. How we write summaries →

Desbordante data profiler enhances discovery of complex dependencies · 3 sources tracked

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The cluster describes new algorithms and implementations for discovering data dependencies, published in academic venues (arXiv).
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COVERAGE [4]

  1. arXiv cs.LG TIER_1 English(EN) · Alexander Smirnov, Anton Chizhov, Ilya Shchuckin, Nikita Bobrov, George Chernishev ·

    Fast Discovery of Inclusion Dependencies with Desbordante

    arXiv:2608.02213v1 Announce Type: cross Abstract: Inclusion dependency is a relation between attributes of tables that indicates possible Primary Key-Foreign Key references. Automatic discovery of inclusion dependencies is a relevant problem for both academic and industrial commu…

  2. arXiv cs.LG TIER_1 English(EN) · Anton Chernikov, Yurii Litvinov, Kirill Smirnov, George Chernishev ·

    FastGFDs: Efficient Validation of Graph Functional Dependencies with Desbordante

    arXiv:2608.02321v1 Announce Type: cross Abstract: Graph functional dependencies (GFD) are a recently-developed concept aimed at capturing both topological structures in graphs and functional dependencies between attributes. The process of verifying whether a given GFD holds over …

  3. Hugging Face Daily Papers TIER_1 English(EN) ·

    FastGFDs: Efficient Validation of Graph Functional Dependencies with Desbordante

    Graph functional dependencies (GFD) are a recently-developed concept aimed at capturing both topological structures in graphs and functional dependencies between attributes. The process of verifying whether a given GFD holds over a particular graph is referred to as GFD validatio…

  4. Hugging Face Daily Papers TIER_1 English(EN) ·

    Extending Desbordante with Probabilistic Functional Dependency Discovery Support

    Data profiling aims to extract complex patterns from data for further analysis and use that data in domains such as data cleaning, data deduplication, anomaly detection, and many more. Functional dependencies (FDs) are one of the most well-known patterns. However, they are poorly…

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