Discovering Conditional Functional Dependencies

Wenfei Fan, Floris Geerts, Laks V. S. Lakshmanan, Ming Xiong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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-itemset mining to reduce search space. Our experimental results demonstrate the following. (a) CFDMiner can be multiple orders of magnitude faster than CTANE and FastCFD for constant CFD discovery. (b) CTANE works well when a given sample relation is large, but it does not scale well with the arity of the relation. (c) FastCFD is far more efficient than CTANE when the arity of the relation is large.
Original languageEnglish
Title of host publicationProceedings of the 25th International Conference on Data Engineering, ICDE 2009, March 29 2009 - April 2 2009, Shanghai, China
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages4
ISBN (Electronic)978-0-7695-3545-6
ISBN (Print)978-1-4244-3422-0
Publication statusPublished - 2009


Dive into the research topics of 'Discovering Conditional Functional Dependencies'. Together they form a unique fingerprint.

Cite this