Learning improved integrity constraints and schemas from exceptions in databases and knowledge bases
On knowledge base management systems: integrating artificial intelligence and d atabase technologies
Object flavor evolution in an object-oriented database system
COCS '88 Proceedings of the ACM SIGOIS and IEEECS TC-OA 1988 conference on Office information systems
Expert systems for configuration at Digital: XCON and beyond
Communications of the ACM
Learning classification rules using Bayes
Proceedings of the sixth international workshop on Machine learning
Learning and relearning in Boltzmann machines
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Intelligent database tools & applications
Intelligent database tools & applications
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Principles of Database and Knowledge-Base Systems: Volume II: The New Technologies
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
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The Carper system, which uses inductive learning to check database consistency, even in poorly understood domains, is described. The application of Carper to the Xcon expert system database is discussed. It is shown that Carper can detect five general error types in Xcon: using value naming conventions inconsistently, assigning legal but incorrect values to attributes, omitting obscure but necessary attribute values, assigning values to attributes that should be left undefined, and failing to update attribute values when dependent attribute values change.