Comparison of Functional Dependency Extraction Methods and an Application of Depth First Search

Abstract

Extracting functional dependencies from existing databases is a useful technique in relational theory, database design and data mining.

Functional dependencies are a key property of relational schema design. A functional dependency is a database constraint between two sets of attributes. In this study we present a comparative study over TANE, FUN, FD_Mine, FastFDs and Dep_Miner, and we propose a new technique, KlipFind, to extract dependencies from relations efficiently. KlipFind employs a depth-first, heuristic driven approach as a solution. Our study indicates that KlipFind is more space efficient than any of the existing solutions and highly efficient in finding keys for relations.