Uncertainty Management in Information Systems: From Needs to Solutions / Edition 1

by Amihai Motro, Philippe Smets

Overview

As its title suggests, "Uncertainty Management in Information Systems" is a book about how information systems can be made to manage information permeated with uncertainty. This subject is at the intersection of two areas of knowledge: information systems is an area that concentrates on the design of practical systems that can store and retrieve information; uncertainty modeling is an area in artificial intelligence concerned with accurate representation of uncertain information and with inference and decision-making under conditions infused with uncertainty. New applications of information systems require stronger capabilities in the area of uncertainty management. Our hope is that lasting interaction between these two areas would facilitate a new generation of information systems that will be capable of servicing these applications. Although there are researchers in information systems who have addressed themselves to issues of uncertainty, as well as researchers in uncertainty modeling who have considered the pragmatic demands and constraints of information systems, to a large extent there has been only limited interaction between these two areas. As the subtitle, "From Needs to Solutions," indicates, this book presents view­ points of information systems experts on the needs that challenge the uncer­ tainty capabilities of present information systems, and it provides a forum to researchers in uncertainty modeling to describe models and systems that can address these needs.

Product Details

ISBN-13:	9780792398035
Publisher:	Springer US
Publication date:	12/31/1996
Edition description:	1997
Pages:	464
Product dimensions:	6.10(w) x 9.25(h) x 0.04(d)

Table of Contents

Preface. 1. Introduction; A. Motro. 2. Sources of Uncertainty, Imprecision, and Inconsistency in Information Systems; A. Motro. 3. Imperfect Information in Relational Databases; E. Zimányi, A. Pirotte. 4. Uncertainty in Intelligent Databases; R. Demolombe. 5. Uncertain, Incomplete, and Inconsistent Data in Scientific and Statistical Databases; S. Kwan, et al. 6. Knowledge Discovery and Acquisition From Imperfect Information; G. Piatetsky-Shapiro. 7. Uncertainty in Information Retrieval Systems; H.R. Turtle, W.B. Croft. 8. Imperfect Information: Imprecision and Uncertainty; P. Smets. 9. Probabilistic and Bayesian Representations of Uncertainty in Information Systems: A Pragmatic Introduction; M. Henrion, et al. 10. An Introduction to the Fuzzy Set and Possibility Theory-Based Treatment of Flexible Queries and Uncertain or Imprecise Databases; P. Bosc, H. Prade. 11. Logical Handling of Inconsistent and Default Information; P. Besnard, et al. 12. The Transferable Belief Model for Belief Representation; P. Smets, R. Kruse. 13. Approximate Reasoning Systems: Handling Uncertainty and Imprecision in Information Systems; P.P. Bonissone. 14. On the Classification of Uncertainty Techniques in Relation to the Application Needs; E.H. Mamdani. 15. A Bibliography on Uncertainty Management in Information Systems; C.E. Dyreson. Index.

Editorial Reviews

Concerned not with managing uncertainty itself, but with information systems that can manage information permeated with uncertainty. Combines perspectives of information systems, which concentrates on systems that can store and retrieve information; with uncertainty modeling, an area of artificial intelligence concerned with the accurate representation of uncertain information and with inference and decision making under uncertain conditions. Considers sources of uncertainty, imperfect information in relational databases, inconsistent data in scientific and statistical databases, knowledge discovery and acquisition, probabilistic and Bayesian representations of uncertainty, the transferable belief model, and other topics. Annotation c. by Book News, Inc., Portland, Or.

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