2021
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[Bibtex] @Article{Kristiansen-2021-SSG,
author = {Kristiansen, Y. S. and Garrison, L. and Bruckner, S.},
title = {Semantic Snapping for Guided Multi-View Visualization Design},
journal = {IEEE Transactions on Visualization and Computer Graphics},
year = {2021},
volume = {},
pages = {},
doi = {},
abstract = {Visual information displays are typically composed of multiple visualizations that are used to facilitate an understanding of the underlying data. A common example are dashboards, which are frequently used in domains such as finance, process monitoring and business intelligence. However, users may not be aware of existing guidelines and lack expert design knowledge when composing such multi-view visualizations. In this paper, we present semantic snapping, an approach to help non-expert users design effective multi-view visualizations from sets of pre-existing views. When a particular view is placed on a canvas, it is “aligned” with the remaining views–not with respect to its geometric layout, but based on aspects of the visual encoding itself, such as how data dimensions are mapped to channels. Our method uses an on-the-fly procedure to detect and suggest resolutions for conflicting, misleading, or ambiguous designs, as well as to provide suggestions for alternative presentations. With this approach, users can be guided to avoid common pitfalls encountered when composing visualizations. Our provided examples and case studies demonstrate the usefulness and validity of our approach.},
note = {Accepted for publication, to be presented at IEEE VIS 2021},
project = {MetaVis,VIDI},
pdf = {pdfs/Kristiansen-2021-SSG.pdf},
vid = {vids/Kristiansen-2021-SSG.mp4},
thumbnails = {images/Kristiansen-2021-SSG.png},
images = {images/Kristiansen-2021-SSG.jpg},
keywords = {tabular data, guidelines, mixed initiative human-machine analysis, coordinated and multiple views},
}@InProceedings{Garrison-2021-EPP,
author = {Laura Garrison and Monique Meuschke and Jennifer Fairman and Noeska Smit and Bernhard Preim and Stefan Bruckner},
title = {An Exploration of Practice and Preferences for the Visual Communication of Biomedical Processes},
booktitle = {Proceedings of VCBM},
year = {2021},
pages = {},
doi = {},
abstract = {The visual communication of biomedical processes draws from diverse techniques in both visualization and biomedical illustration. However, matching these techniques to their intended audience often relies on practice-based heuristics or narrow-scope evaluations. We present an exploratory study of the criteria that audiences use when evaluating a biomedical process visualization targeted for communication. Designed over a series of expert interviews and focus groups, our study focuses on common communication scenarios of five well-known biomedical processes and their standard visual representations. We framed these scenarios in a survey with participant expertise spanning from minimal to expert knowledge of a given topic. Our results show frequent overlap in abstraction preferences between expert and non-expert audiences, with similar prioritization of clarity and the ability of an asset to meet a given communication objective. We also found that some illustrative conventions are not as clear as we thought, e.g., glows have broadly ambiguous meaning, while other approaches were unexpectedly preferred, e.g., biomedical illustrations in place of data-driven visualizations. Our findings suggest numerous opportunities for the continued convergence of visualization and biomedical illustration techniques for targeted visualization design.},
note = {Accepted for publication, to be presented at VCBM 2021},
project = {VIDI,ttmedvis},
pdf = {pdfs/Garrison-2021-EPP.pdf},
thumbnails = {images/Garrison-2021-EPP.png},
images = {images/Garrison-2021-EPP.jpg},
url = {https://github.com/lauragarrison87/Biomedical_Process_Vis},
keywords = {biomedical illustration, visual communication, survey},
}![[DOI]](https://vis.uib.no/wp-content/plugins/papercite/img/external.png){kind=small}
[Bibtex] @article{brushingComparison,
author={Fan, Chaoran and Hauser, Helwig},
journal={IEEE Computer Graphics and Applications},
title={On sketch-based selections from scatterplots using KDE, compared to Mahalanobis and CNN brushing},
year={2021},
volume={},
number={},
pages={1-13},
doi={10.1109/MCG.2021.3097889},
abstract = {"Fast and accurate brushing is crucial in visual data exploration and sketch-based solutions are successful methods. In this paper, we detail a solution, based on kernel density estimation (KDE), which computes a data subset selection in a scatterplot from a simple click-and-drag interaction. We explain, how this technique relates to two alternative approaches, i.e., Mahalanobis brushing and CNN brushing. To study this relation, we conducted two user studies and present both a quantitative three-fold comparison as well as additional details about the prevalence of all possible cases in that each technique succeeds/fails. With this, we also provide a comparison between empirical modeling and implicit modeling by deep learning in terms of accuracy, efficiency, generality and interpretability."},
pdf = "pdfs/Fan-2021-brushingComparison.pdf",
images = "images/Fan-2021-brushingComparison.png",
thumbnails = "images/Fan-2021-brushingComparison.png",
}@incollection{Smit-2021-COMULIS,
author = {Smit, Noeska and Bühler, Katja and Vilanova, Anna and Falk, Martin},
title = {Visualisation for correlative multimodal imaging},
booktitle = {Imaging Modalities for Biological and Preclinical Research: A Compendium, Volume 2},
publisher = {IOP Publishing},
year = {2021},
series = {2053-2563},
type = {Book Chapter},
pages = {III.4.e-1 to III.4.e-10},
abstract = {In this chapter, we describe several approaches to interactive imaging data visualization in general, highlight several strategies for visualizing correlative multimodal imaging data, and provide examples and practical recommendations.},
url = {http://dx.doi.org/10.1088/978-0-7503-3747-2ch28},
doi = {10.1088/978-0-7503-3747-2ch28},
isbn = {978-0-7503-3747-2},
thumbnails = "images/Smit-2021-COMULIS.PNG",
images = "images/Smit-2021-COMULIS.PNG",
project = "ttmedvis",
abstract = {The field of visualisation deals with finding appropriate visual representations of data so people can effectively carry out tasks related to data exploration, analysis, or presentation using the power of the human visual perceptual system. In the context of biomedical imaging data, interactive visualisation techniques can be employed, for example, to visually explore data, as image processing quality assurance, or in publications to communicate findings. When dealing with correlative imaging, challenges arise in how to effectively convey the information from multiple sources. In particular, the information density leads to the need for a critical reflection on the visual design with respect to which parts of the data are important to show and at what level of importance they should be visualised. In this chapter, we describe several approaches to interactive imaging data visualisation in general, highlight several strategies for visualising correlative multimodal imaging data, and provide examples and practical recommendations.}
}![[YT]](https://vis.uib.no/wp-content/papercite-data/images/youtube.png)
[Bibtex] @article{Trautner-2021-LWI,
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 = "https://youtu.be/-hLF5XSR_ws",
project = "MetaVis",
git = "https://github.com/TTrautner/LineWeaver"
}@article{Diehl-2021-HTC,
author = {Alexandra Diehl and Rodrigo Pelorosso and Juan Ruiz and Renato Pajarola and Meister Eduard Gr\"{o}ller and Stefan Bruckner},
title = {Hornero: Thunderstorms Characterization using Visual Analytics},
journal = {Computer Graphics Forum},
volume = {40},
number = {3},
pages = {},
keywords = {visual analytics, weather forecasting, nowcasting},
doi = {},
abstract = {Analyzing the evolution of thunderstorms is critical in determining the potential for the development of severe weather events. Existing visualization systems for short-term weather forecasting (nowcasting) allow for basic analysis and prediction of storm developments. However, they lack advanced visual features for efficient decision-making. We developed a visual analytics tool for the detection of hazardous thunderstorms and their characterization, using a visual design centered on a reformulated expert task workflow that includes visual features to overview storms and quickly identify high-impact weather events, a novel storm graph visualization to inspect and analyze the storm structure, as well as a set of interactive views for efficient identification of similar storm cells (known as analogs) in historical data and their use for nowcasting. Our tool was designed with and evaluated by meteorologists and expert forecasters working in short-term operational weather forecasting of severe weather events. Results show that our solution suits the forecasters workflow. Our visual design is expressive, easy to use, and effective for prompt analysis and quick decision-making in the context of short-range operational weather forecasting.},
year = {2021},
pdf = "pdfs/Diehl-2021-HTC.pdf",
thumbnails = "images/Diehl-2021-HTC.png",
images = "images/Diehl-2021-HTC.jpg",
vid = "vids/Diehl-2021-HTC.mp4",
project = "MetaVis"
}