14 citations · 33 across the 19 of their papers we have counts for
7 papers · 1 filter
FastGFDs: Efficient Validation of Graph Functional Dependencies with Desbordante
Anton Chernikov, Yurii Litvinov, Kirill Smirnov +1
Graph functional dependencies (GFD) are a recently-developed concept aimed at capturing both topological structures in graphs and functional dependencies between attributes. The pr…
Fast Discovery of Inclusion Dependencies with Desbordante
Alexander Smirnov, Anton Chizhov, Ilya Shchuckin +2
Inclusion dependency is a relation between attributes of tables that indicates possible Primary Key-Foreign Key references. Automatic discovery of inclusion dependencies is a relev…
Extending Desbordante with Probabilistic Functional Dependency Discovery Support
Ilia Barutkin, Maxim Fofanov, Sergey Belokonny +2
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 m…
Order in Desbordante: Techniques for Efficient Implementation of Order Dependency Discovery Algorithms
Yakov Kuzin, Dmitriy Shcheka, Michael Polyntsov +3
Science-intensive data profiling focuses on discovery and validation of various patterns in datasets. This study considers discovery of one such pattern - order dependency (OD). Si…
Lightning Fast Matching Dependency Discovery with Desbordante
Alexey Shlyonskikh, Michael Sinelnikov, Daniil Nikolaev +2
Matching dependency is a generalization of the functional dependency concept, which allows users to apply custom similarity functions for matching individual attributes. Matching d…
Efficient Discovery of Conditional Dependencies with Desbordante
Ivan Kozhukov, Dmitry Fedoseev, Maksim Emelyanov +4
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…
Scalable Maximal Frequent Episode Mining with Desbordante
Maxim Ivanov, Matvei Smirnov, Alisa Strazdina +1
Episode mining aims to extract subsequences of events that possess certain distinctive properties and constitute facts valuable to the user. Maximal frequent episode mining concent…