2021

F. Bolte, M. Nourani, E. Ragan, and S. Bruckner, "SplitStreams: A Visual Metaphor for Evolving Hierarchies," IEEE Transactions on Visualization and Computer Graphics, vol. 27, iss. 8, 2021.

[Bibtex]

@article{bolte2020splitstreams,
author= {Bolte, Fabian and Nourani, Mahsan and Ragan, Eric and Bruckner, Stefan},
journal= {IEEE Transactions on Visualization and Computer Graphics},
title= {SplitStreams: A Visual Metaphor for Evolving Hierarchies},
year= {2021},
keywords= {Information Visualization, Trees, Data Structures and Data Types, Visualization Techniques and Methodologies},
doi= {10.1109/TVCG.2020.2973564},
url= {https://arxiv.org/pdf/2002.03891.pdf},
volume = {27},
number = {8},
doi = {10.1109/TVCG.2020.2973564},
abstract= {The visualization of hierarchically structured data over time is an ongoing challenge and several approaches exist trying to solve it. Techniques such as animated or juxtaposed tree visualizations are not capable of providing a good overview of the time series and lack expressiveness in conveying changes over time. Nested streamgraphs provide a better understanding of the data evolution, but lack the clear outline of hierarchical structures at a given timestep. Furthermore, these approaches are often limited to static hierarchies or exclude complex hierarchical changes in the data, limiting their use cases. We propose a novel visual metaphor capable of providing a static overview of all hierarchical changes over time, as well as clearly outlining the hierarchical structure at each individual time step. Our method allows for smooth transitions between tree maps and nested streamgraphs, enabling the exploration of the trade-off between dynamic behavior and hierarchical structure. As our technique handles topological changes of all types, it is suitable for a wide range of applications. We demonstrate the utility of our method on several use cases, evaluate it with a user study, and provide its full source code.},
pdf= {pdfs/Bolte-2020-SplitStreams.pdf},
images= {images/Bolte-2020-SplitStreams.png},
thumbnails= {images/Bolte-2020-SplitStreams_thumb.png},
project = "MetaVis",
git = "https://github.com/cadanox/SplitStreams"
}

F. Bolte and S. Bruckner, "Vis-a-Vis: Visual Exploration of Visualization Source Code Evolution," IEEE Transactions on Visualization and Computer Graphics, vol. 27, iss. 7, p. 3153–3167, 2021.

[Bibtex]

@article{bolte2019visavis,
author= {Bolte, Fabian and Bruckner, Stefan},
journal= {IEEE Transactions on Visualization and Computer Graphics},
title= {Vis-a-Vis: Visual Exploration of Visualization Source Code Evolution},
year= {2021},
keywords= {Visualization System and Toolkit Design;User Interfaces;Integrating Spatial and Non-Spatial Data Visualization;Software Visualization},
doi= {10.1109/TVCG.2019.2963651},
issn= {2160-9306},
url= {https://arxiv.org/pdf/2001.02092.pdf},
abstract= {Developing an algorithm for a visualization prototype often involves the direct comparison of different development stages and design decisions, and even minor modifications may dramatically affect the results. While existing development tools provide visualizations for gaining general insight into performance and structural aspects of the source code, they neglect the central importance of result images unique to graphical algorithms. In this paper, we present a novel approach that enables visualization programmers to simultaneously explore the evolution of their algorithm during the development phase together with its corresponding visual outcomes by providing an automatically updating meta visualization. Our interactive system allows for the direct comparison of all development states on both the visual and the source code level, by providing easy to use navigation and comparison tools. The on-the-fly construction of difference images, source code differences, and a visual representation of the source code structure further enhance the user's insight into the states' interconnected changes over time. Our solution is accessible via a web-based interface that provides GPU-accelerated live execution of C++ and GLSL code, as well as supporting a domain-specific programming language for scientific visualization.},
pdf= {pdfs/Bolte-2019-Visavis.pdf},
images= {images/Bolte-2019-Visavis.png},
thumbnails= {images/Bolte-2019-Visavis_thumb.png},
youtube= {https://www.youtube.com/watch?v=5XO6BU4j1KQ},
volume = {27},
number = {7},
pages = {3153--3167},
project = "MetaVis"
}

2020

T. Trautner, F. Bolte, S. Stoppel, and S. Bruckner, "Sunspot Plots: Model-based Structure Enhancement for Dense Scatter Plots," Computer Graphics Forum, vol. 39, iss. 3, p. 551–563, 2020.

[Bibtex]

@article{Trautner-2020-SunspotPlots,
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 = "https://youtu.be/G6l-y6YGjzQ",
project = "MetaVis"
}

F. Bolte and S. Bruckner, "Organic Narrative Charts," in Proceedings of Eurographics 2020 (Short Papers), 2020, p. 93–96.

[Bibtex]

@INPROCEEDINGS {Bolte-2020-ONC,
author = "Fabian Bolte and Stefan Bruckner",
title = "Organic Narrative Charts",
booktitle = "Proceedings of Eurographics 2020 (Short Papers)",
year = "2020",
pages = "93--96",
doi = "10.2312/egs.20201026",
month = "may",
abstract = "Storyline visualizations display the interactions of groups and entities and their development over time. Existing approaches have successfully adopted the general layout from hand-drawn illustrations to automatically create similar depictions. Ward Shelley is the author of several diagrammatic paintings that show the timeline of art-related subjects, such as Downtown Body, a history of art scenes. His drawings include many stylistic elements that are not covered by existing storyline visualizations, like links between entities, splits and merges of streams, and tags or labels to describe the individual elements. We present a visualization method that provides a visual mapping for the complex relationships in the data, creates a layout for their display, and adopts a similar styling of elements to imitate the artistic appeal of such illustrations.We compare our results to the original drawings and provide an open-source authoring tool prototype.",
pdf = "pdfs/Bolte-2020-ONC.pdf",
images = "images/Bolte-2020-ONC.jpg",
thumbnails = "images/Bolte-2020-ONC.png",
event = "Eurographics 2020",
keywords = "narrative charts, storylines, aesthetics",
project = "MetaVis",
git = "https://github.com/cadanox/orcha"
}

F. Bolte and S. Bruckner, "Measures in Visualization Space," in Foundations of Data Visualization, Springer, 2020.

[Bibtex]

@incollection {Bolte-2019-MVS,
author = {Bolte, Fabian and Bruckner, Stefan},
title = {Measures in Visualization Space},
booktitle = {Foundations of Data Visualization},
chapter = {3},
publisher = {Springer},
year = {2020},
pdf = {pdfs/Bolte-2019-MVS.pdf},
images = {images/Bolte-2019-MVS.png},
thumbnails = {images/Bolte-2019-MVS.png},
abstract = {Measurement is an integral part of modern science, providing the fundamental means for evaluation, comparison, and prediction. In the context of visualization, several different types of measures have been proposed, ranging from approaches that evaluate particular aspects of individual visualization techniques, their perceptual characteristics, and even economic factors. Furthermore, there are approaches that attempt to provide means for measuring general properties of the visualization process as a whole. Measures can be quantitative or qualitative, and one of the primary goals is to provide objective means for reasoning about visualizations and their effectiveness. As such, they play a central role in the development of scientific theories for visualization. In this chapter, we provide an overview of the current state of the art, survey and classify different types of visualization measures, characterize their strengths and drawbacks, and provide an outline of open challenges for future research.},
note = {This is a preprint of a chapter for a planned book that was initiated by participants of the Dagstuhl Seminar 18041 ("Foundations of Data Visualization") and that is expected to be published by Springer. The final book chapter will differ from this preprint.},
url = {https://arxiv.org/abs/1909.05295},
project = "MetaVis",
isbn = {978-3-030-34443-6},
doi = {10.1007/978-3-030-34444-3_3}
}