How Does an Expert System Get its Data? (Extended Abstract).
Yannis Vassiliou, James Clifford, Matthias Jarke:
How Does an Expert System Get its Data? (Extended Abstract).
VLDB 1983: 70-72@inproceedings{DBLP:conf/vldb/VassiliouCJ83,
author = {Yannis Vassiliou and
James Clifford and
Matthias Jarke},
editor = {Mario Schkolnick and
Costantino Thanos},
title = {How Does an Expert System Get its Data? (Extended Abstract)},
booktitle = {9th International Conference on Very Large Data Bases, October
31 - November 2, 1983, Florence, Italy, Proceedings},
publisher = {Morgan Kaufmann},
year = {1983},
isbn = {0-934613-15-X},
pages = {70-72},
ee = {db/conf/vldb/VassiliouCJ83.html},
crossref = {DBLP:conf/vldb/83},
bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX
Abstract
An Expert System (Es) is a problem-solving
computer system that incorporates enough
knowledge in somne specialized problem domain to
reach a level of performance comparable to that
of a human expert. In the heart of an expert
system lies the program that "reasons" and makes
deductions ("inference engine"). To reason,
knowledge both of general rules (e.g. if a
person works for a company then he/she gets
employee benefits) and of specific declarative
facts (e.g. john works for nyu) is needed.
With few exceptions, little attention is
given in ESs to the handling of very large
populations Of specific facts. Since early
prototype ESs represented specific facts which
were characterized by large variety and a very
small population, the inefficiency of data
handling was not an issue. As ESs increase in
sophistication and ambition, they deal with
applications requiring a very large population
of facts, often in the form of existing
databases manipulated by generalized DBMS.
This short paper (see Vassiliou et al 1983
for more details) investigates the technical
issues of enhancing expert systems with database
management facilities in four stages, leading to
the coupling of the ES with a large DBMS. Our
vehicles are first-order logic (with Prolog) and
relational database management.
Copyright © 1983 by the VLDB Endowment.
Permission to copy without fee all or part of this material is granted provided that the copies are not made or
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is by the permission of the Very Large Data Base
Endowment. To copy otherwise, or to republish, requires
a fee and/or special permission from the Endowment.
Online Paper
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BibTeX
Printed Edition
Mario Schkolnick, Costantino Thanos (Eds.):
9th International Conference on Very Large Data Bases, October 31 - November 2, 1983, Florence, Italy, Proceedings.
Morgan Kaufmann 1983, ISBN 0-934613-15-X
Contents BibTeX
References
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Referenced by
- Alexander Borgida, Ronald J. Brachman:
Loading Data into Description Reasoners.
SIGMOD Conference 1993: 217-226
- Qiming Chen:
A Rule-Based Object/Task Modelling Approach.
SIGMOD Conference 1986: 281-292
- K. Woehl:
Automatic Classification of Office Documents by Coupling Relational Data Bases and PROLOG Expert Systems.
VLDB 1984: 529-532
- Michel E. Adiba, Gia Toan Nguyen:
Information Processing for CAD/VLSI on a Generalized Data Management System.
VLDB 1984: 371-374
- Matthias Jarke, James Clifford, Yannis Vassiliou:
An Optimizing Prolog Front-End to a Relational Query System.
SIGMOD Conference 1984: 296-306
BibTeX
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