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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Probabilistic Model-Based Assessment of Information Quality in Uncertain Domains
verfasst von : Steffen Michels, Marina Velikova, Peter J. F. Lucas
Erschienen in: AI 2012: Advances in Artificial Intelligence
Verlag: Springer Berlin Heidelberg
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In various domains, such as security and surveillance, a large amount of information from heterogeneous sources is continuously gathered to identify and prevent potential threats, but it is unknown in advance what the observed entity of interest should look like. The quality of the decisions made depends, of course, on the quality of the information they are based on. In this paper, we propose a novel method for assessing the quality of information taking into account uncertainty. Two properties –
soundness
and
completeness
– of the information are used to define the notion of information quality and their expected values are defined using a probabilistic model output. Simulation experiments with data from a maritime scenario demonstrates the usage of the proposed method and its potential for decision support in complex tasks such as surveillance.