Systems for Knowledge Discovery in Databases (1993)
| Venue: | IEEE Transactions On Knowledge And Data Engineering |
| Citations: | 88 - 8 self |
BibTeX
@ARTICLE{Matheus93systemsfor,
author = {Christopher J. Matheus and Philip K. Chan and Gregory Piatetsky-shapiro},
title = {Systems for Knowledge Discovery in Databases},
journal = {IEEE Transactions On Knowledge And Data Engineering},
year = {1993},
volume = {5},
pages = {903--913}
}
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Abstract
The automated discovery of knowledge in databases is becoming increasingly important as the world's wealth of data continues to grow exponentially. Knowledge-discovery systems face challenging problems from real-world databases which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. This paper addresses these problems and describes some techniques for handling them. A model of an idealized knowledge-discovery system is presented as a reference for studying and designing new systems. This model is used in the comparison of three systems: CoverStory, EXPLORA, and the Knowledge Discovery Workbench. The deficiencies of existing systems relative to the model reveal several open problems for future research.
