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it - Information Technology 64(4-5) - Oktober 2022

https://dl.gi.de/handle/20.500.12116/39744
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Auflistung it - Information Technology 64(4-5) - Oktober 2022 nach Autor:in "Deussen, Oliver"

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  • Zeitschriftenartikel
    Robust visualization of trajectory data
    (it - Information Technology: Vol. 64, No. 4-5, 2022) Zhang, Ying; Klein, Karsten; Deussen, Oliver; Gutschlag, Theodor; Storandt,Sabine
    The analysis of movement trajectories plays a central role in many application areas, such as traffic management, sports analysis, and collective behavior research, where large and complex trajectory data sets are routinely collected these days. While automated analysis methods are available to extract characteristics of trajectories such as statistics on the geometry, movement patterns, and locations that might be associated with important events, human inspection is still required to interpret the results, derive parameters for the analysis, compare trajectories and patterns, and to further interpret the impact factors that influence trajectory shapes and their underlying movement processes. Every step in the acquisition and analysis pipeline might introduce artifacts or alterate trajectory features, which might bias the human interpretation or confound the automated analysis. Thus, visualization methods as well as the visualizations themselves need to take into account the corresponding factors in order to allow sound interpretation without adding or removing important trajectory features or putting a large strain on the analyst. In this paper, we provide an overview of the challenges arising in robust trajectory visualization tasks. We then discuss several methods that contribute to improved visualizations. In particular, we present practical algorithms for simplifying trajectory sets that take semantic and uncertainty information directly into account. Furthermore, we describe a complementary approach that allows to visualize the uncertainty along with the trajectories.
  • Zeitschriftenartikel
    Uncertainty visualization: Fundamentals and recent developments
    (it - Information Technology: Vol. 64, No. 4-5, 2022) Hägele, David; Schulz, Christoph; Beschle, Cedric; Booth, Hannah; Butt, Miriam; Barth, Andrea; Deussen, Oliver; Weiskopf, Daniel
    This paper provides a brief overview of uncertainty visualization along with some fundamental considerations on uncertainty propagation and modeling. Starting from the visualization pipeline, we discuss how the different stages along this pipeline can be affected by uncertainty and how they can deal with this and propagate uncertainty information to subsequent processing steps. We illustrate recent advances in the field with a number of examples from a wide range of applications: uncertainty visualization of hierarchical data, multivariate time series, stochastic partial differential equations, and data from linguistic annotation.