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
Link To Document :
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