Systems for knowledge discovery in databases

Author

Matheus, Christopher J. ; Chan, Philip K. ; Piatetsky-Shapiro, Gregory

Author_Institution

GTE Labs. Inc., Waltham, MA, USA

Volume

5

Issue

6

fYear

1993

fDate

12/1/1993 12:00:00 AM

Firstpage

903

Lastpage

913

Abstract

Knowledge-discovery systems face challenging problems from real-world databases, which tend to be dynamic, incomplete, redundant, noisy, sparse, and very large. These problems are addressed and some techniques for handling them are described. 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

Keywords

deductive databases; knowledge acquisition; knowledge based systems; learning (artificial intelligence); CoverStory; EXPLORA; KDD systems; Knowledge Discovery Workbench; future research; idealized knowledge-discovery system; knowledge acquisition; knowledge discovery; machine learning; real-world databases; Data analysis; Deductive databases; Filters; Information retrieval; Laboratories; Machine learning; Packaging; Pattern analysis; Relational databases; Transaction databases;

fLanguage

English

Journal_Title

Knowledge and Data Engineering, IEEE Transactions on

Publisher

ieee

ISSN

1041-4347

Type

jour

DOI

10.1109/69.250073

Filename

250073