Efficient Foreign Key Discovery Based on Nearest Neighbor Search

Abstract

With rapid growth of data size and schema complexity, many data sets are structured in tables but without explicit foreign key definitions. Automatically identifying foreign keys among relations will be beneficial to query optimization, schema matching, data integration and database design as well. This paper formulates foreign key discovery as a nearest neighbor search problem and proposes a fast foreign key discovery algorithm. To reduce foreign key candidates, we detect inclusion dependencies first. Then we choose statistical features to represent an attribute and define two attributes’s distance. Finally, foreign keys are discovered by finding nearest neighbors of all primary keys. Experiment results on real and synthetic data sets show that our algorithm can discover foreign keys efficiently.