Database Consistency via Inductive Learning
Abstract
Building a large-scale system often involves creating a large knowledge store, and as these grow and are maintained by a number of individuals, errors are inevitable. Exploring databases as a specialization of knowledge stores, this paper studies the hypothesis that models may be learned to describe attribute usage; these models improve database consistency by serving as integrity constraints and definitions for computed attributes. To that end, this paper describes an implemented system called CARPER that has been applied to an engineering database. The results demonstrate the viability of the approach and establish a baseline of performance for future research. Keywords: Database consistency, integrity constraints, computed attributes, decision tree learning.
Cite
Text
Schlimmer. "Database Consistency via Inductive Learning." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50130-6Markdown
[Schlimmer. "Database Consistency via Inductive Learning." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/schlimmer1991icml-database/) doi:10.1016/B978-1-55860-200-7.50130-6BibTeX
@inproceedings{schlimmer1991icml-database,
title = {{Database Consistency via Inductive Learning}},
author = {Schlimmer, Jeffrey C.},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {640-644},
doi = {10.1016/B978-1-55860-200-7.50130-6},
url = {https://mlanthology.org/icml/1991/schlimmer1991icml-database/}
}