The design of relational databases
The design of relational databases
On the complexity of inferring functional dependencies
Discrete Applied Mathematics - Special issue on combinatorial problems in databases
Approximate inference of functional dependencies from relations
ICDT '92 Selected papers of the fourth international conference on Database theory
Efficient Discovery of Functional and Approximate Dependencies Using Partitions
ICDE '98 Proceedings of the Fourteenth International Conference on Data Engineering
A Framework for Understanding Existing Databases
IDEAS '01 Proceedings of the International Database Engineering & Applications Symposium
Database dependency discovery: a machine learning approach
AI Communications
Discovering branching and fractional dependencies in databases
Data & Knowledge Engineering
Using error-correcting dependencies for collaborative filtering
Data & Knowledge Engineering
HLS: Tunable Mining of Approximate Functional Dependencies
BNCOD '08 Proceedings of the 25th British national conference on Databases: Sharing Data, Information and Knowledge
Depth first algorithms and inferencing for AFD mining
IDEAS '09 Proceedings of the 2009 International Database Engineering & Applications Symposium
Advancing the discovery of unique column combinations
Proceedings of the 20th ACM international conference on Information and knowledge management
| Hi-index | 0.00 |
Students in a typical database course are introduced to theoretical design from a functional dependency standpoint. Functional dependencies are rules of the form X→Y, where X and Y are attributes of a relation r(R). Those rules express the potential one-to-one, and many-to-one relationships among the atributes of R. Unfortunately finding the non-trivial rules X→Y from an existing arbitrary relation is a hard problem. We present an extension of the SQL-based algorithm of Bell and Brockhausen [1] to explore a relation and find its exact and approximate functional dependencies. We use the g3 measure of Kivinen and Mannila to express the degree of approximation of a dependency. This application could be used either as an example or a project in an advanced database course.