DocumentCode
879678
Title
Self-modeling databases
Author
Schlimmer, Jeffrey C.
Author_Institution
Sch. of Electr. Eng. & Comput. Sci., Washington State Univ., Pullman, WA, USA
Volume
8
Issue
2
fYear
1993
fDate
4/1/1993 12:00:00 AM
Firstpage
35
Lastpage
43
Abstract
The Carper system, which uses inductive learning to check database consistency, even in poorly understood domains, is described. The application of Carper to the Xcon expert system database is discussed. It is shown that Carper can detect five general error types in Xcon: using value naming conventions inconsistently, assigning legal but incorrect values to attributes, omitting obscure but necessary attribute values, assigning values to attributes that should be left undefined, and failing to update attribute values when dependent attribute values change.<>
Keywords
data integrity; deductive databases; expert systems; learning by example; program verification; Carper system; Xcon expert system database; attribute values; data integrity; database consistency; inductive learning; learning by example; self-modelling databases; value naming; Computer errors; Deductive databases; Displays; Encoding; Expert systems; Law; Learning systems; Legal factors; Query processing; Relational databases;
fLanguage
English
Journal_Title
IEEE Expert
Publisher
ieee
ISSN
0885-9000
Type
jour
DOI
10.1109/64.207427
Filename
207427
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