Interactive Visual Analytics and Visualization for Decision Making - Making Sense of Big Data

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https://hdl.handle.net/10125/52033

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Now showing 1 - 4 of 4
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    Visual Decision-Making in Real-Time Business Intelligence: A Social Media Marketing Example
    ( 2018-01-03) Davcheva, Elena ; Benlian, Alexander
    This paper presents a study into the use of visualizations in real-time business intelligence. Different visualization designs for a social media marketing use case are tested and evaluated through the lens of cognitive load theory. By reducing the complexity of visualizations and subsequently cognitive load, end-users can achieve markedly improved decision-making performance in situations where time is critical and data is fast-paced.
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    Representation Effects and Loss Aversion in Analytical Behaviour: An Experimental Study into Decision Making Facilitated by Visual Analytics
    ( 2018-01-03) Booth, Paul ; Gibbins, Nicholas ; Galanis, Spryos
    This paper presents the results of an experiment into the relationship between the representation of data and decision-making. Three hundred participants online, were asked to choose between a series of financial investment opportunities using data presented in line charts. A single dependent variable of investment choice was examined over four levels of varying display conditions and randomised data. Three variations to line chart visualisations provided a controlled factor between subjects divided into three groups; -˜standard’ line charts, -˜tall’ line charts, and one dual-series line chart. The final results revealed a consistent main effect and two other interactions between certain display conditions and decision-making. The findings of this paper are significant to the study visualisation and to the field of visual analytics. This experiment was devised as part of a study into Analytical Behaviour, defined as decision-making facilitated by visual analytics - a new topic that encompasses existing research and real-world applications.
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    Immersive Visualization for Abnormal Detection in Heterogeneous Data for On-site Decision Making
    ( 2018-01-03) Mahfoud, Elias ; Wegba, Kodzo ; Li, Yuemeng ; Han, Honglei ; Lu, Aidong
    The latest advances in mixed reality promote new capabilities that allow head-mounted displays, such as Microsoft HoloLens, to visualize various data and information in a real physical environment. While such new features have great potential for new generations of visualization systems, they require fundamentally different visualization and interaction techniques that have not been well explored. This paper presents an immersive visualization approach for investigating abnormal events in heterogeneous, multi-source, and time-series sensor data collections in real-time on the site of the event. Our approach explores the essential components for an analyst to visualize complex data and explore hidden connections in mixed reality; it also combines automatic event detection algorithms to identify suspicious activities. We demonstrate our prototype system by using the developer version of Microsoft HoloLens and presenting case studies that require an analyst to investigate related data on site. We also discuss the limitations of the current infrastructure and potential applications for security visualization.
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