Deriving and Maintaining Rules in an Intelligent DBMS (Computer and Information Science)

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This research investigates the automatic generation of inference rules to support intelligent data processing. For example, in knowledge-based systems, production rules encode knowledge used for expert reasoning. In database systems, inference rules encode integrity constraints used for semantic query optimization. Such rule-based systems now depend on human experts to supply the rules. This proposal focuses on a database application, developing a semantic query optimization system that automatically derives and maintains rules, based on database usage patterns and changes to the database state. The automatic derivation of rules is a promising self-adaptive database optimization technique that combines areas of research from both database theory and artificial intelligence. The importance of this research is that it will lead to improvements in the performance of many kinds of rule-based computer programs. System-derived rules can be more flexible and efficient than a set developed by a human expert. In addition, the criteria developed for automatic evaluation of various rule sets may be of use to human experts as well.