Project deliverable Open Access

BigDataGrapes D5.2 - Uncertainty-aware Visual Analytic Components

Verbert, Katrien; Htun, Nyi-Nyi; Rojo, Diego


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    <dct:description>&lt;p&gt;In this deliverable (D5.2), we demonstrate uncertainty-aware visual analytic components that highlight uncertainties in prediction outcomes. Previous research has shown the importance of showing uncertainty as they could improve awareness and trust of the readers, particularly non-expert users. With a growing awareness of the uncertainty problem, researchers have proposed to extend traditional visualisation techniques to represent the uncertainty information associated with data. Nonetheless, the application of uncertainty visualisations is limited in the agriculture domain.&lt;/p&gt; &lt;p&gt;&lt;br&gt; To demonstrate our components, we initially used a stock price dataset obtained from Quandl. Later, as data from pilot partners became available, we integrated the data and redesigned the components. The dataset we used in this particular deliverable came from one of our pilot partners: INRA. The dataset contains alcoholic fermentation kinetics measured over time. The main attributes present in the dataset are the level of carbon dioxide (CO2), temperature and time at measure. In this document, we present our uncertainty-aware visual analytic components and the development framework used to implement the components.&lt;/p&gt; &lt;p&gt;&lt;br&gt; As the components are updated in the future versions, we will keep this document up to date accordingly. To keep track of such changes, please use this link: http://bit.ly/2L7yMbp.&lt;/p&gt;</dct:description>
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    <dct:title>Big Data to Enable Global Disruption of the Grapevine-powered Industries</dct:title>
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