EuroVA14

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https://diglib.eg.org/handle/10.2312/7746

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Browsing EuroVA14 by Subject "I.3.8 [Computer Graphics]"

Now showing 1 - 4 of 4
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    From Ill-defined Problems to Informed Decisions
    (The Eurographics Association, 2014) Roberts, Jonathan; Keim, Daniel; Hanratty, Timothy; Rowlingson, Robert; Walker, Rick; Hall, Mark; Jackobson, Zack; Lavigne, Valerie; Rooney, Chris; Varga, Margaret; M. Pohl and J. Roberts
    Decision makers such as military leaders and security analysts are increasingly being asked to make decisions on ill-defined problems. These problems may contain uncertain or incomplete data, and are often complex to piece together. Consequently, decision makers rely heavily on intuition, knowledge and experience. We argue for rich narratives that encapsulate both explicit data and implicit knowledge, supported by three levels of provenance: data, analytical and reasoning. Our hypotheses is that visual analytics tools and methods can help to provide a valuable means to make sense of these complex data, and to help make this tacit knowledge explicit, to support the construction and presentation of the decision.
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    Interactively Visualizing Summaries of Rules and Exceptions
    (The Eurographics Association, 2014) Sharma, Geetika; Shroff, Gautam; Pandey, Aditeya; Agarwal, Puneet; Srinivasan, Ashwin; M. Pohl and J. Roberts
    Rules along with their exceptions have been used to explain large data sets in a comprehensible manner. In this paper we describe an interactive visualization scheme for rules and their exceptions. Our visual encoding is based on principles for creating perceptually effective visualizations from literature. Our visualization scheme presents an overview first, allows semantic zooming and then shows details on demand using established principles of interactive visualization. We assume that rules and exceptions have been mined and summarized using available techniques; however our visualization is applicable for more general rule hierarchies as well. We illustrate our visualization using rules and exceptions extracted from real customer surveys as well as on rule sets derived from past literature.
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    Ribbons: Enabling the Effective Use of HPC Utilization Data for System Support Staff
    (The Eurographics Association, 2014) Sisneros, Robert; Fullop, Joshi; Semeraro, B. David; Bauer, Greg; M. Pohl and J. Roberts
    Beyond raw computational power, a supercomputer offers the capability of generating and logging a significant amount of diagnostic data. While adding to the burden of maintenance, this data nevertheless represents compelling opportunities for development directed toward improved evaluations, diagnostics, analytics, etc. We have developed such a utility, a visual analytics tool for the support staff of the Blue Waters supercomputer. Our initial goal was broad: provide an informative illustration of current running jobs on the machine for the purpose of system monitoring. Additionally, we were able to collect diverse utilization data to the extent that both minimizing exclusion of as well as intuitively coordinating information were equally challenging. Our primary visual element is an extension of a stacked bar chart to increase horizontal continuity; resulting visualizations show system utilization as a series of concurrent job ''ribbons''. The remaining elements are common visual/interactive techniques offering expansive functionality. Together these components were deployed as a web application, which is referred to as the ''ribbon viewer'' by its regular users. In this paper we will highlight the design nuances and development complexities that are belied by the ribbon viewer's apparent simplicity. We will also discuss use-case scenarios in terms of both typical usage and specific examples.
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    Supporting an Early Detection of Diabetic Neuropathy by Visual Analytics
    (The Eurographics Association, 2014) Luboschik, Martin; Röhlig, Martin; Kundt, Günther; Stachs, Oliver; Peschel, Sabine; Zhivov, Andrey; Guthoff, Rudolf F.; Winter, Karsten; Schumann, Heidrun; M. Pohl and J. Roberts
    In this paper, we describe a step-wise approach to utilize ophthalmic markers for detecting early diabetic neuropathy (DN), the most common long-term complication of diabetes mellitus. Our approach is based on the Visual Analytics Mantra: First, we statistically analyze the data to identify those variables that separate DN patients from a control group. Afterwards, we show the important separating variables individually, but also in the context of all variables regarding a pre-defined classification. By doing so, we support the understanding of the categorization in respect of the value distribution of variables. This allows for zooming, filtering and further analysis like deleting non-relevant variables that do not contribute to the definition of markers as well as deleting data records with false data values or false classifications. Finally, outliers are observed and investigated in detail. So, a third group of potential DN patients can be introduced. In this way, the detection of early DN can be effectively supported.