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- W2024443169 abstract "The discovery of unknown functional dependencies in a dataset is of great importance for database redesign, anomaly detection and data cleansing applications. However, as the nature of the problem is exponential in the number of attributes none of the existing approaches can be applied on large datasets. We present a new algorithm DFD for discovering all functional dependencies in a dataset following a depth-first traversal strategy of the attribute lattice that combines aggressive pruning and efficient result verification. Our approach is able to scale far beyond existing algorithms for up to 7.5 million tuples, and is up to three orders of magnitude faster than existing approaches on smaller datasets." @default.
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- W2024443169 date "2014-11-03" @default.
- W2024443169 modified "2023-10-16" @default.
- W2024443169 title "DFD" @default.
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- W2024443169 doi "https://doi.org/10.1145/2661829.2661884" @default.
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