Browsing by Author "Schmidt, Johanna"

Now showing 1 - 7 of 7
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    Augmenting Node-Link Diagrams with Topographic Attribute Maps
    (The Eurographics Association and John Wiley & Sons Ltd., 2020) Preiner, Reinhold; Schmidt, Johanna; Krösl, Katharina; Schreck, Tobias; Mistelbauer, Gabriel; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
    We propose a novel visualization technique for graphs that are attributed with scalar data. In many scenarios, these attributes (e.g., birth date in a family network) provide ambient context information for the graph structure, whose consideration is important for different visual graph analysis tasks. Graph attributes are usually conveyed using different visual representations (e.g., color, size, shape) or by reordering the graph structure according to the attribute domain (e.g., timelines). While visual encodings allow graphs to be arranged in a readable layout, assessing contextual information such as the relative similarities of attributes across the graph is often cumbersome. In contrast, attribute-based graph reordering serves the comparison task of attributes, but typically strongly impairs the readability of the structural information given by the graph's topology. In this work, we augment force-directed node-link diagrams with a continuous ambient representation of the attribute context. This way, we provide a consistent overview of the graph's topological structure as well as its attributes, supporting a wide range of graph-related analysis tasks. We resort to an intuitive height field metaphor, illustrated by a topographic map rendering using contour lines and suitable color maps. Contour lines visually connect nodes of similar attribute values, and depict their relative arrangement within the global context. Moreover, our contextual representation supports visualizing attribute value ranges associated with graph nodes (e.g., lifespans in a family network) as trajectories routed through this height field. We discuss how user interaction with both the structural and the contextual information fosters exploratory graph analysis tasks. The effectiveness and versatility of our technique is confirmed in a user study and case studies from various application domains.
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    EuroVis 2021 Posters: Frontmatter
    (The Eurographics Association, 2021) Byška, Jan; Jänicke, Stefan; Schmidt, Johanna; Byška, Jan and Jänicke, Stefan and Schmidt, Johanna
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    EuroVis 2022 Posters: Frontmatter
    (The Eurographics Association, 2022) Krone, Michael; Lenti, Simone; Schmidt, Johanna; Krone, Michael; Lenti, Simone; Schmidt, Johanna
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    Human-Based and Automatic Feature Ideation for Time Series Data: A Comparative Study
    (The Eurographics Association, 2023) Schmidt, Johanna; Piringer, Harald; Mühlbacher, Thomas; Bernard, Jürgen; Angelini, Marco; El-Assady, Mennatallah
    Feature ideation is a crucial early step in the feature extraction process, where new features are extracted from raw data. For phenomena existing in time series data, this often includes the ideation of statistical parameters, representations of trends and periodicity, or other geometrical and shape-based characteristics. The strengths of automatic feature ideation methods are their generalizability, applicability, and robustness across cases, whereas human-based feature ideation is most useful in uncharted real-world applications, where incorporating domain knowledge is key. Naturally, both types of methods have proven their right to exist. The motivation for this work is our observation that for time series data, surprisingly few human-based feature ideation approaches exist. In this work, we discuss requirements for human-based feature ideation for VA applications and outline a set of characteristics to assess the goodness of feature sets. Ultimately, we present the results of a comparative study of humanbased and automated feature ideation methods, for time series data in a real-world Industry 4.0 setting. One of our results and discussion items is a call to arms for more human-based feature ideation approaches.
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    Participatory Visualization Design as an Approach to Minimize the Gap between Research and Application
    (The Eurographics Association, 2020) Jänicke, Stefan; Kaur, Pawandeep; Kuzmicki, Pawel; Schmidt, Johanna; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomas
    Despite acceptance in our field, many sophisticated visualization projects suffer from failing acceptance by the targeted audience. Though the reasons for this circumstance might be manifold, we argue that they align with the typical pitfalls of software development. On the one hand, stakeholders are often not or only marginally integrated in the visualization design process, on the other hand, the goals we follow as visualization scholars do often not align with the goals of the stakeholders, reducing them to data deliverers. We provide case studies reporting on finished and ongoing projects following a participatory design approach. Those projects are initiated by the needs from users in digital humanities, biodiversity research, sports analysis and data science, and our results indicate that participatory visualization design leads to mutual benefits, reducing the gap between research and application in the targeted domain.
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    Selective Angular Brushing of Parallel Coordinate Plots
    (The Eurographics Association, 2021) Sahann, Raphael; Gajic, Ivana; Moeller, Torsten; Schmidt, Johanna; Agus, Marco and Garth, Christoph and Kerren, Andreas
    Parallel coordinates are an established technique to visualize multivariate data. Since these graphs are generally hard to read, we need interaction techniques to judge them accurately. Adding to the existing brushing techniques used in parallel coordinate plots, we present a triangular selection that highlights lines with a single click-and-drag mouse motion. Our selection starts by clicking on an axis and dragging the mouse away to select different ranges of lines. The position of the mouse determines the angle and the scope of the selection. We refined the interaction by running and adapting our method in two small user studies and present the most intuitive version to use.
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    Visualization in Notebook-Style Interfaces
    (The Eurographics Association, 2020) Schmidt, Johanna; Ortner, Thomas; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomas
    Visualization research has always stressed the need for visual tools for data exploration and sense making. Despite the fact that many visualization technologies are available nowadays, their application in modern data science workflows is limited. One of the manifold reasons behind this is the development of visual analytics tools as standalone applications, featuring the complete pipeline from data loading to visualization. Other tools are targeted towards specific use cases (e.g., data wrangling), but to not provide standardized interfaces for import and export. This does not reflect the approach of stitching together several tools as it is employed in data science workflows nowadays. In this paper we outline the differences between standalone tools and notebook-style workflows for a specific use case for time series analysis. The outcomes demonstrate the benefits of notebookstyle interfaces for tracking the steps in a data analysis workflow in a narrative way, for reporting, and for collaboration. We therefore argue that not considering the current developments towards the increased application of notebook-style interfaces for data science will lead to a reduced application and acceptance of visualization techniques in these domains. We outline the barriers for the integration of visualization techniques in narrative workflows, and describe directions for future research.