The reliability of visual representations is becoming more and more relevant. The increasing use of visual analytics in data science workflows has raised questions about the reliability of features and patterns discovered in the data. Experiments indicated that users are often exposed to different cognitive stimuli and, as a result, may misinterpret incidental discoveries as significant patterns in the data. To overcome this problem, visualization researchers suggest incorporating statistical significance testing into visual analysis systems so that users can verify their discoveries. Moreover, it is essential to better understand the process of interpreting visual information by human viewers (from a cognitive and psychological perspective). The use of immersive analysis techniques can also further facilitate interaction with the data.
Research goals
The RAILING project will focus on the following research goals:
To develop methods for the statistical evaluation of findings in visualization, and the close integration of statistical significant tests. Better integration of evaluation and significance testing mechanisms will increase the trust and reliability of users in visual data exploration techniques.
To research new methodologies for perception-based representation and recommendation of visualization techniques. More understanding about how people read and interpret visualizations can be directly integrated into the design of new visualization techniques. This way it can be ensured that the proposed techniques are adapted to human perception.
To establish novel comparative visualization approaches for the comparison of complex data items. Comparisons over time are an important topic when working with time series data. New comparison mechanisms and dashboard techniques will enable users to compare complex and heterogeneous data parts.
To develop new techniques for progressive visual analytics for time-dependent data. When working with time series data, which can become very large, progressive techniques can ensure scalability.
To research novel interfaces and interaction concepts for immersive analytics. Directly working with the data in different spaces and accessing different senses can lead to a better understanding of the data and increase the positive experience when doing data analysis.
Head of Visual Analytics, Visual Analytics
johanna.schmidt(at)vrvis.at +43 1 908 98 92 302Start: 01.2021
End: 12.2024
AVL List GmbH, RHI Magnesita, HAKOM Time Series GmbH, Plasmo Industrietechnik GmbH, Austrian Power Grid AG, TU Wien, TU Graz, Uni Wien, University of Warwick
FFG - COMET
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