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Balasubramanie [18] used traditional knowledge-based tool to define DSS. Williams [28]
described the benefits of using Ontologies and Argumentation for DSS. Suh in [25] applied
Database Management System (DBMS) in two-phased decision support system for resource
allocation.
To the best of our knowledge, there is no one particular or specific method for handling
variability as a knowledge representation technique in DSS. In addition to variability
representation, our proposed method could be used to deal with main challenges in
variability representation such as: constraint dependency rules, explanation, propagation
and delete-cascade, logic inconsistency detection and dead decision detection. Table 1
summarized the previous works in knowledge representation and validation regarding a
DSS. The columns are denoted as following: KR for Knowledge Representation, CDR for
Constraint Dependency Rules, Expl for Explanation, Pro and DC for Propagation and
delete-cascade, LID for Logic Inconsistency Detection and DDD for Dead Decision
Detection.
Technique
Ref.
KR
Reasoning
CDR
Expl
Pro
and
DC
LID
DDD Gap
ISO/IEC 11179
10
Yes
No
No
No
No
No
No
6/7
Traditional
artificial
intelligence
knowledge
representation
techniques such
as
frames, decision
trees, belief
networks, etc
16,1,12
Yes
Yes
No
No
No
No
No
5/7
Associational
cognitive
maps(ACM)
8
Yes
Yes
No
No
No
No
No
5/7
Bayesian network
1
Yes
Yes
Yes
Yes
No
No
No
3/7
Text, tables and
diagrams
13
Yes
No
No
No
No
No
No
6/7
Ontologies
3, 28
Yes
Yes
No
No
No
No
No
5/7
Temporal
database and
Semantic Web
4
Yes
Yes
Yes
Yes
No
No
No
3/7
Three layer
modeling(KADS)
24
Yes
Yes
No
No
No
No
No
5/7
DBMS
25
Yes
Yes
No
No
No
No
No
5/7
Table 1. Summary of Literature Review
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