Browsing by Author "Schumann, Heidrun"

Now showing 1 - 4 of 4
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    Customizable Coordination of Independent Visual Analytics Tools
    (The Eurographics Association, 2021) Nonnemann, Lars; Hogräfer, Marius; Schumann, Heidrun; Urban, Bodo; Schulz, Hans-Jörg; Vrotsou, Katerina and Bernard, Jürgen
    While it is common to use multiple independent analysis tools in combination, it is still cumbersome to carry out a cross-tool visual analysis. Some dedicated frameworks addressing this issue exist, yet in order to use them, a Visual Analytics tool must support their API or architecture. In this paper, we do not rely on a single predetermined exchange mechanism for the whole ensemble of VA tools. Instead, we propose using any available channel for exchanging data between two subsequently used VA tools. This effectively allows to mix and match different data exchange strategies within one cross-tool analysis, which considerably reduces the overhead of adding a new VA tool to a given tool ensemble. We demonstrate our approach with a first implementation called AnyProc and its application to a use case of three VA tools in a Health IT data analysis scenario.
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    Exploring Time Series Segmentations Using Uncertainty and Focus+Context Techniques
    (The Eurographics Association, 2020) Bors, Christian; Eichner, Christian; Miksch, Silvia; Tominski, Christian; Schumann, Heidrun; Gschwandtner, Theresia; Kerren, Andreas and Garth, Christoph and Marai, G. Elisabeta
    Time series segmentation is employed in various domains and continues to be a relevant topic of research. A segmentation pipeline is composed of different steps involving several parameterizable algorithms. Existing Visual Analytics approaches can help experts determine appropriate parameterizations and corresponding segmentation results for a given dataset. However, the results may also be afflicted with different types of uncertainties. Hence, experts face the additional challenge of understanding the reliability of multiple alternative the segmentation results. So far, the influence of uncertainties in the context of time series segmentation could not be investigated. We present an uncertainty-aware exploration approach for analyzing large sets of multivariate time series segmentations. The approach features an overview of uncertainties and time series segmentations, while detailed exploration is facilitated by (1) a lens-based focus+context technique and (2) uncertainty-based re-arrangement. The suitability of our uncertainty-aware design was evaluated in a quantitative user study, which resulted in interesting findings of general validity.
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    Making Parameter Dependencies of Time‐Series Segmentation Visually Understandable
    (© 2020 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2020) Eichner, Christian; Schumann, Heidrun; Tominski, Christian; Benes, Bedrich and Hauser, Helwig
    This work presents an approach to support the visual analysis of parameter dependencies of time‐series segmentation. The goal is to help analysts understand which parameters have high influence and which segmentation properties are highly sensitive to parameter changes. Our approach first derives features from the segmentation output and then calculates correlations between the features and the parameters, more precisely, in parameter subranges to capture global and local dependencies. Dedicated overviews visualize the correlations to help users understand parameter impact and recognize distinct regions of influence in the parameter space. A detailed inspection of the segmentations is supported by means of visually emphasizing parameter ranges and segments participating in a dependency. This involves linking and highlighting, and also a special sorting mechanism that adjusts the visualization dynamically as users interactively explore individual dependencies. The approach is applied in the context of segmenting time series for activity recognition. Informal feedback from a domain expert suggests that our approach is a useful addition to the analyst's toolbox for time‐series segmentation.
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    Towards Understanding Edit Histories of Multivariate Graphs
    (The Eurographics Association, 2022) Berger, Philip; Schumann, Heidrun; Tominski, Christian; Bernard, Jürgen; Angelini, Marco
    The visual analysis of multivariate graphs increasingly involves not only exploring the data, but also editing them. Existing editing approaches for multivariate graphs support visual analytics workflows by facilitating a seamless switch between data exploration and editing. However, it remains difficult to comprehend performed editing operations in retrospect and to compare different editing results. Addressing these challenges, we propose a model describing what graph aspects can be edited and how. Based on this model, we develop a novel approach to visually track and understand data changes due to edit operations. To visualize the different graph states resulting from edits, we extend an existing graph visualization approach so that graph structure and the associated multivariate attributes can be represented together. Branching sequences of edits are visualized as a node-link tree layout where nodes represent graph states and edges visually encode the performed edit operations and the graph aspects they affect. Individual editing operations can be inspected by dynamically expanding edges to detail views on demand. In addition, we support the comparison of graph states through an interactive creation of attribute filters that can be applied to other states to highlight similarities.