2009•DROPS (Schloss Dagstuhl – Leibniz Center for Informatics)Open access

08421 Working Group: Imprecision, Diversity and Uncertainty: Disentangling Threads in Uncertainty Management

Myra Spiliopoulou, Maurice van Keulen, Hans-Joachim Lenz, Jef Wijsen, Matthias Renz, Rudolf Kruse, Mirco Stern

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Abstract

We report on the results of Workgroup 1 on "Imprecision, Diversity and Uncertainty". We set the scene by elaborating on where uncertainty comes from and what the ground truth is. In real world applications, the data observed may not be as expected: they may violate constraints, or, more generally, disagree with the anticipated model of the world. This leads to two orthogonal cases: The data may be erroneous, i.e. they must be corrected. Or, the model may outdated and must be adjusted to the data. After elaborating on this fundamental distinction, we address the issues of measuring uncertainty and exploiting uncertainty in real applications. We conclude with a list of challenges that should be addressed when dealing with uncertainty.

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What this paper is about

We report on the results of Workgroup 1 on "Imprecision, Diversity and Uncertainty". We set the scene by elaborating on where uncertainty comes from and what the ground truth is. In real world applications, the data observed may not be as expected: they may violate constraints, or, more generally, disagree with the anticipated model of the world. This leads to two orthogonal cases: The data may be erroneous, i.e. they must be corrected. Or, the model may outdated and must be adjusted to the data. After elaborating on this fundamental distinction, we address the issues of measuring uncertainty and exploiting uncertainty in real applications. We conclude with a list of challenges that should be addressed when dealing with uncertainty.

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Available abstract

We report on the results of Workgroup 1 on "Imprecision, Diversity and Uncertainty". We set the scene by elaborating on where uncertainty comes from and what the ground truth is. In real world applications, the data observed may not be as expected: they may violate constraints, or, more generally, disagree with the anticipated model of the world. This leads to two orthogonal cases: The data may be erroneous, i.e. they must be corrected. Or, the model may outdated and must be adjusted to the data. After elaborating on this fundamental distinction, we address the issues of measuring uncertainty and exploiting uncertainty in real applications. We conclude with a list of challenges that should be addressed when dealing with uncertainty.

Key concepts: Workgroup, Computer science, Diversity (politics), Measurement uncertainty, Set (abstract data type), Group (periodic table), Uncertainty quantification, Ground truth

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