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Titlebook: Incompleteness and Uncertainty in Information Systems; Proceedings of the S V. S. Alagar,S. Bergler,F. Q. Dong Conference proceedings 1994

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https://doi.org/10.1007/978-1-4471-3242-4Incompleteness; SQL; artificial intelligence; database; database system; databases; development; evolution;
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Knowledge and Uncertainty a Rough Set Approach,al importance to AI and cognitive sciences, in particular expert systems, decision support systems, machine learning, machine discovery, inductive reasoning pattern recognition, decision tables and others.
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,Situation Theory and Dempster-Shafer’s Theory of Evidence for Information Retrieval,y and Dempster-Shafer’s Theory of Evidence. These were selected because they allow us to tackle two of the main problems that confront any attempt to model an information retrieval system: the representation of information and its flow; and the uncertainty engendered by the complexity and ambiguity arising when dealing with information.
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Completeness in Statechart Models for Reactive Systems,itial requirements. In this paper, we introduce the notion of totality for requirements and formalise the notion of completeness of system specification with respect to requirements. The paper illustrates a method for checking completeness using disjunctive logic programs.
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Extracting Exact and Approximate Rules from Databases,dependencies as well as exact and approximate (probabilistic) implication rules between properties (descriptors). This paper presents algorithms for rule generation and shows that the lattice is an interesting framework for that purpose.
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