Thomas Trautner

PhD Student

Visualization Hybridization

 Team Bruckner

Thomas is a PhD candidate in the Visualization Research Group at the Department of Informatics of the University of Bergen, Norway. He received his bachelor’s degree (2016) and master’s degree (2018), both in (Media Informatics and) Visual Computing, from TU Wien, Austria. He is a member of IEEE (Membership, Young Professionals, Computer Society Membership) and EG (Eurographics).

His research focuses on combining proven and well-established visualization techniques, so-called “Visualization Hybridization”, using spatialization cues. The aim is to maximize the benefits of individual techniques while simultaneously eliminating potential drawbacks. Using implicit visual encodings, such as spatial perception, this results in novel visualization techniques that are superior to their predecessors.








    [PDF] [DOI] [Bibtex]
    @inproceedings {Trautner-2022-HCP,
    author = {Trautner, Thomas and Sbardellati, Maximilian and Stoppel, Sergej and Bruckner, Stefan},
    title = {{Honeycomb Plots: Visual Enhancements for Hexagonal Maps}},
    booktitle = {Proc. of VMV 2022: Vision, Modeling, and Visualization},
    editor = {Bender, Jan and Botsch, Mario and Keim, Daniel A.},
    pages = {65--73},
    year = {2022},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-189-2},
    DOI = {10.2312/vmv.20221205},
    abstract = {Aggregation through binning is a commonly used technique for visualizing large, dense, and overplotted two-dimensional data sets. However, aggregation can hide nuanced data-distribution features and complicates the display of multiple data-dependent variables, since color mapping is the primary means of encoding. In this paper, we present novel techniques for enhancing hexplots with spatialization cues while avoiding common disadvantages of three-dimensional visualizations. In particular, we focus on techniques relying on preattentive features that exploit shading and shape cues to emphasize relative value differences. Furthermore, we introduce a novel visual encoding that conveys information about the data distributions or trends within individual tiles. Based on multiple usage examples from different domains and real-world scenarios, we generate expressive visualizations that increase the information content of classic hexplots and validate their effectiveness in a user study.},
    pdf = "pdfs/Trautner-2022-HCP.pdf",
    thumbnails = "images/Trautner-2022-HCP-thumb.png",
    images = "images/Trautner-2022-HCP-thumb.png",
    git = ""


    [PDF] [DOI] [VID] [YT] [Bibtex]
    author = {Trautner, Thomas and Bruckner, Stefan},
    title = {Line Weaver: Importance-Driven Order Enhanced Rendering of Dense Line Charts},
    journal = {Computer Graphics Forum},
    volume = {40},
    number = {3},
    pages = {399--410},
    keywords = {information visualization, visualization techniques, line charts},
    doi = {10.1111/cgf.14316},
    abstract = {Line charts are an effective and widely used technique for visualizing series of ordered two-dimensional data points. The relationship between consecutive points is indicated by connecting line segments, revealing potential trends or clusters in the underlying data. However, when dealing with an increasing number of lines, the render order substantially influences the resulting visualization. Rendering transparent lines can help but unfortunately the blending order is currently either ignored or naively used, for example, assuming it is implicitly given by the order in which the data was saved in a file. Due to the noncommutativity of classic alpha blending, this results in contradicting visualizations of the same underlying data set, so-called "hallucinators". In this paper, we therefore present line weaver, a novel visualization technique for dense line charts. Using an importance function, we developed an approach that correctly considers the blending order independently of the render order and without any prior sorting of the data. We allow for importance functions which are either explicitly given or implicitly derived from the geometric properties of the data if no external data is available. The importance can then be applied globally to entire lines, or locally per pixel which simultaneously supports various types of user interaction. Finally, we discuss the potential of our contribution based on different synthetic and real-world data sets where classic or naive approaches would fail.},
    year = {2021},
    pdf = "pdfs/Trautner-2021-LWI.pdf",
    thumbnails = "images/Trautner-2021-LWI-thumb.png",
    images = "images/Trautner-2021-LWI-thumb.png",
    vid = "vids/Trautner_2021_LineWeaver_video.mp4",
    youtube = "",
    project = "MetaVis",
    git = ""


    [PDF] [DOI] [VID] [YT] [Bibtex]
    author = {Trautner, T. and Bolte, F. and Stoppel, S. and Bruckner, S.},
    title = {Sunspot Plots: Model-based Structure Enhancement for Dense Scatter Plots},
    journal = {Computer Graphics Forum},
    volume = {39},
    number = {3},
    pages = {551--563},
    keywords = {information visualization, scatterplots, kernel density estimation},
    doi = {10.1111/cgf.14001},
    abstract = {Scatter plots are a powerful and well-established technique for visualizing the relationships between two variables as a collection of discrete points. However, especially when dealing with large and dense data, scatter plots often exhibit problems such as overplotting, making the data interpretation arduous. Density plots are able to overcome these limitations in highly populated regions, but fail to provide accurate information of individual data points. This is particularly problematic in sparse regions where the density estimate may not provide a good representation of the underlying data. In this paper, we present sunspot plots, a visualization technique that communicates dense data as a continuous data distribution, while preserving the discrete nature of data samples in sparsely populated areas. We furthermore demonstrate the advantages of our approach on typical failure cases of scatter plots within synthetic and real-world data sets and validate its effectiveness in a user study.},
    year = {2020},
    pdf = "pdfs/Trautner_2020_SunspotPlots_PDF.pdf",
    thumbnails = "images/Trautner_2020_SunspotPlots_thumb.png",
    images = "images/Trautner_2020_SunspotPlots_thumb.png",
    vid = "vids/Trautner_2020_SunspotPlots_video.mp4",
    youtube = "",
    project = "MetaVis"


    [PDF] [DOI] [Bibtex]
    author = {Byška, J. and Trautner, T. and Marques, S.M. and Damborský, J. and Kozlíková, B. and Waldner, M.},
    title = {Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization},
    journal = {Computer Graphics Forum},
    volume = {38},
    number = {3},
    pages = {441-453},
    keywords = {CCS Concepts, Human-centered computing -- Scientific visualization; User centered design},
    doi = {10.1111/cgf.13701},
    url = {},
    eprint = {},
    abstract = {Abstract Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio-temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.},
    year = {2019},
    pdf = "pdfs/AnalysisOfLongMolecularDynamicsSimulationsUsingInteractiveFocusAndContextVisualization_Trautner.pdf",
    images = "images/Byska-2019-LongMolecularDynamicsSimulations.png",
    thumbnails = "images/Byska-2019-LongMolecularDynamicsSimulations.png"