Discovering Conditional Functional Dependencies

This paper investigates the discovery of conditional functional dependencies (CFDs). CFDs are a recent extension of functional dependencies (FDs) by supporting patterns of semantically related constants, and can be used as rules for cleaning relational data. However, finding CFDs is an expensive process that involves intensive manual effort. To effectively identify data cleaning rules, we develop techniques for discovering CFDs from sample relations. We provide three methods for CFD discovery. The first, referred to as CFDMiner, is based on techniques for mining closed itemsets, and is used to discover constant CFDs, namely, CFDs with constant patterns only. The other two algorithms are developed for discovering general CFDs. The first algorithm, referred to as CTANE, is a levelwise algorithm that extends TANE, a well-known algorithm for mining FDs. The other, referred to as FastCFD, is based on the depthfirst approach used in FastFD, a method for discovering FDs. It leverages closed-i...

• Type: Conference
• Year: 2009
• Where: ICDE
• Authors: Wenfei Fan, Floris Geerts, Laks V. S. Lakshmanan, Ming Xiong
• Keywords: Conditional Functional Dependencies, Constant Cfd Discovery, Constant Cfds, Database, General Cfds, ICDE 2009, Mining Fds