Automated Database Schema Design Using Mined Data Dependencies

Data dependencies are used in database schema design to enforce the correctness of a database as well as to reduce redundant data. These dependencies are usually determined from the semantics of the attributes and are then enforced upon the relations. This paper describes a bottom-up procedure for discovering multivalued dependencies (MVDs) in observed data without knowing a priori the relationships amongst the attributes. The proposed algorithm is an application of the technique we designed for learning conditional independen-cies in probabilistic reasoning. A prototype system for automated database schema design has been implemented. Experiments were carried out to demonstrate both the eeectiveness and eeciency of our method.

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