Using knowledge discovery techniques for database schema refinement

This paper proposes the automated translation of rules extracted from data mining or knowledge discovery tools into active database rules. A new rule identification measure for categorizing knowledge discovery rules into semantic integrity constraints and probabilistic rules is introduced. Using this measure we estimate and model both the static and dynamic characteristics of a given rule. The rule classification process is followed by the generation of the ECA equivalents using a generic technique. Finally, the monitoring of the database scheme refinement process and the transition of a rule from one state to another are discussed.