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.
arXiv:2608. 02213v1 Announce Type: cross Abstract: Inclusion dependency is a relation between attributes of tables that indicates possible Primary Key-Foreign Key references.
By Alexander Smirnov, Anton Chizhov, Ilya Shchuckin, Nikita Bobrov, George Chernishev
arXiv:2607. 23632v1 Announce Type: cross Abstract: Science-intensive data profiling focuses on discovery and validation of various patterns in datasets.
By Yakov Kuzin, Dmitriy Shcheka, Michael Polyntsov, Kirill Stupakov, Mikhail Firsov, George Chernishev
arXiv:2607. 23636v1 Announce Type: cross Abstract: 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.
By Ilia Barutkin, Maxim Fofanov, Sergey Belokonny, Vladislav Makeev, George Chernishev
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.
arXiv:2607. 10771v1 Announce Type: cross Abstract: Matching dependency is a generalization of the functional dependency concept, which allows users to apply custom similarity functions for matching individual attributes.
By Alexey Shlyonskikh, Michael Sinelnikov, Daniil Nikolaev, Yurii Litvinov, George Chernishev