Query generalization: a method for interpreting null answers
Proceedings from the first international workshop on Expert database systems
ACM Transactions on Database Systems (TODS)
VAGUE: a user interface to relational databases that permits vague queries
ACM Transactions on Information Systems (TOIS)
Models of incremental concept formation
Artificial Intelligence
Proceedings of the sixth international workshop on Machine learning
The Grid File: An Adaptable, Symmetric Multikey File Structure
ACM Transactions on Database Systems (TODS)
System R: relational approach to database management
ACM Transactions on Database Systems (TODS)
Machine Learning
Experiments with Incremental Concept Formation: UNIMEM
Machine Learning
Knowledge Acquisition Via Incremental Conceptual Clustering
Machine Learning
Building concept hierarchies for schema integration in HDDBS using incremental concept formation
CIKM '93 Proceedings of the second international conference on Information and knowledge management
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This paper examines the idea of incorporating machine learning algorithms into a database system for monitoring its stream of incoming queries and generating hierarchies with the most important concepts expressed in those queries. The goal is for these hierarchies to providevaluable input to the database administrator for dynamically modifying the physical and external schemas of a database for improved system performance and user productivity. The criteria for choosing the appropriate learning algorithms are analyzed, and based on them, two such algorithms, UNIMEM and COBWEB, are selected as the most suitable ones for the task. Standard UNIMEM and COBWEB implementations have been modified to support queries as input. Based on the results of experiments with these modified implementations, the whole approach appears to be quite promising, expecially if the concept hierarchy from which the learning algorithms start their processing is initialized with some of the most obvious concepts captured in the database.