EuroRVVV18

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https://diglib.eg.org/handle/10.2312/2632397

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Browsing EuroRVVV18 by Subject "I.3.3 [Computer Graphics]"

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    Toward Visualizing Subjective Uncertainty: A Conceptual Framework Addressing Perceived Uncertainty through Action Redundancy
    (The Eurographics Association, 2018) Li, Wei; Funk, Mathias; Brombacher, Aarnout C.; Kai Lawonn and Noeska Smit and Lars Linsen and Robert Kosara
    Uncertainty is usually technically defined with associated metrics by visualization researchers. Next to this rather objective description, there is a subjective notion to uncertainty considering human experiences eliciting a response to the perceived uncertainty. This article aims to complement the default technical notion with a subjective perspective of uncertainty as we experienced. As a starting point, we introduce a conceptual framework aiming to explain the consequential life-cycle of subjective uncertainty in relation with visualization methods. The framework is illustrated by a case in which the redundancy of logged game play behavior is visualized to assist the discovery of subjective uncertainty. Our preliminary results show that visualizing the Shannon entropy of categorical action labels can be a promising method to probe subjective uncertainty.
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    Visual Analytics-enabled Bayesian Network Approach to Reasoning about Public Camera Data
    (The Eurographics Association, 2018) Chuprikova, Ekaterina; MacEachren, Alan M.; Cron, Juliane; Meng, Liqiu; Kai Lawonn and Noeska Smit and Lars Linsen and Robert Kosara
    The Visual Analytics (VA) approach has become an important tool for gaining insights on various data sets. Thus, significant research has been conducted to integrate statistical methods in the interactive environment of VA where data visualization provides support to analysts in understanding and exploring the data. However, much of the data explored with VA is inherently uncertain due to limits of our knowledge about a phenomenon, randomness and indeterminism, and vagueness. The Bayesian Network (BN) is a graphical model that provides techniques for reasoning under conditions of uncertainty in a consistent and mathematically rigorous manner. While several software tools for visualizing and editing BNs exist, they have an evident shortcoming when spatial data. In this study, we propose a Visual Analytics-enabled BN approach for reasoning under uncertainty. We describe the implementation procedure using an example of heterogeneous data that includes locations of security surveillance cameras installed in public places.