Contact: Stefan Bruckner
Members:
Fabian Bolte
Thomas Trautner
Stefan Bruckner
Yngve Kristiansen
Visualization is the transformation of data into readily comprehensible images, and has proven to be an indispensable part of the discovery process in many fields of contemporary science and engineering. A vast number of different visualization methods have been developed, some very general, some only applicable to specific types of data. This makes it increasingly difficult to chose between the many alternatives given a certain task. This project is devoted to the study of the space of visualizations itself and aims to develop means for its interactive exploration. A key realization of our approach is that by regarding the visualization process as a complex phenomenon, it becomes amenable to parameter space analysis techniques. Within the scope of this project, methods for systematically structuring this visualization space will be devised and embedded in an interactive web-based framework. In particular, we plan to investigate techniques to automatically infer the performance of individual visualizations for particular tasks based on a sparse user classification and to provide an environment for interactively presenting and refining these measures. The MetaVis project addresses an important challenge which affects visualization experts as well as users of visualization methods in their daily work.
Publications
2022
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[Bibtex] @phdthesis{trautner2022thesis,
title = {Visualization Hybridization with Spatialization Cues},
author = {Thomas Bernhard Trautner},
year = 2022,
month = {November},
isbn = 9788230855515,
url = {https://hdl.handle.net/11250/3031041},
school = {Department of Informatics, University of Bergen, Norway},
abstract = {
Visualization as a tool for visual processing of any underlying data has proven to be an accepted and legitimate part of the scientific reasoning process. Many different techniques help gaining new insights from captured phenomena, support the development or evaluation of hypotheses about collected data, reveal potential misconceptions or false assumptions, simplify communicating knowledge and novel findings, and enable a multitude of additional opportunities. The reason for this effectiveness is that the human visual system is ideally suited to capture and process visually encoded data. The development of visualization from a niche to an established scientific field has made a significant contribution to this success story. A large number of journals, conferences, seminars, and workshops regularly publish new results, evaluate presented approaches, and help making knowledge globally accessible. However, this large number of contributions tailored to variable user groups, the underlying data, and the wide variety of tasks that could be performed with them, emphasizes the plethora of available techniques and the resulting difficulty in choosing the most suitable visualizations.
Therefore, we investigated common data sets and analyzed typical tasks normally performed with them. Based on this, we selected well-established and most effective visualization techniques, combining them to form a hybrid representation. The goal of such a visualization hybridization was to merge advantages of individual techniques and, thereby, simultaneously eliminate their limitations. We present so-called hybrid vigors that make the underlying visualizations more widely applicable instead of either having to change required techniques sequentially, or not being able to perform certain tasks at all. Our contributions are intended to simplify the process of finding suitable visualizations for already established data sets. During our research, we focused on two-dimensional point data, depicted on the one hand as scatter plots and, on the other hand, as relationships between consecutive point such as in line charts. Our techniques can be used especially when data sets are so large, dense, and overplotted that conventional techniques reach their limits. We show that hybrid representations are well suited for combining discrete, continuous, or aggregated forms of visual representation. Our hybridizations additionally exploit spatialization cues. Such visual cues emphasize spatiality of the underlying data through shading, without having to embed the data in 3D space including its potential disadvantages. We chose this method of encoding as we consider it the most appropriate choice, given that visualization users interact naturally and preattentively with a spatial world on a daily basis.
},
pdf = {pdfs/Trautner-PhD-Thesis-2022.pdf},
images = {images/Trautner-2022-PhD.png},
thumbnails = {images/Trautner-2022-PhD.png},
project = {MetaVis}
}@article{Kristiansen2022ContentDriven,
title = {Content-Driven Layout for Visualization Design},
author = {Kristiansen, Yngve and Garrison, Laura and Bruckner, Stefan},
year = 2022,
journal = {Proceedings of the International Symposium on Visual Information Communication and Interaction (to appear)},
volume = {},
pages = {},
doi = {},
issn = {},
url = {},
abstract = {Multi-view visualizations are typically presented in a grid layout with elements positioned according to their bounding rectangles. These rectangles often contain unused white space. In cases where Tufte’s Shrink Principle can be applied to reduce non-data-ink without impairing the communication of information, unused white space can be utilized for the placement of other elements. This is often done in manually “hand-crafted” layouts by designers. However, upon changes to individual elements, this design process has to be repeated. To reduce non-data-ink and repetitive manual design, we contribute a method for automatically turning a grid layout into a content-driven layout, where elements are positioned with respect to their contents. Existing approaches have explored the use of a force simulation in conjunction with proxy geometries to simplify collision handling for irregular shapes. Such customized force directed layouts are usually unstable, and often require additional constraints to run properly. In addition, proxy geometries become less accurate and effective with more irregular shapes. To solve these shortcomings, we contribute an approach for identifying central elements in an original grid layout in order to set up corresponding attractive forces. Furthermore, we utilize an imagebased approach for collision detection and avoidance that works accurately for highly irregular shapes. We demonstrate the utility of our approach with three case studies.},
images = "images/Kristiansen-2022-LungsDt.PNG",
thumbnails = "images/Kristiansen-2022-LungsDt.PNG",
pdf = {pdfs/Kristiansen-2022-CDL.pdf},
project = "MetaVis",
}2021
![[DOI]](https://vis.uib.no/wp-content/plugins/papercite/img/external.png){kind=small}
![[VID]](https://vis.uib.no/wp-content/papercite-data/images/video.png)
[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},
doi = {10.1109/TVCG.2021.3114860},
}![[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"
}@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"
}@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
[Bibtex] @article{Kristiansen-2020-VIV,
author = {Yngve Sekse Kristiansen and Stefan Bruckner},
title = {Visception: An Interactive Visual Framework for Nested Visualization Design},
journal = {Computers \& Graphics},
volume = {92},
pages = {13--27},
keywords = {information visualization, nested visualizations, nesting},
doi = {10.1016/j.cag.2020.08.007},
abstract = {Nesting is the embedding of charts into the marks of another chart. Related to principles such as Tufteâs rule of utilizing micro/macro readings, nested visualizations have been employed to increase information density, providing compact representations of multi-dimensional and multi-typed data entities. Visual authoring tools are becoming increasingly prevalent, as they make visualization technology accessible to non-expert users such as data journalists, but existing frameworks provide no or only very limited functionality related to the creation of nested visualizations. In this paper, we present an interactive visual approach for the flexible generation of nested multilayer visualizations. Based on a hierarchical representation of nesting relationships coupled with a highly customizable mechanism for specifying data mappings, we contribute a flexible framework that enables defining and editing data-driven multi-level visualizations. As a demonstration of the viability of our framework, we contribute a visual builder for exploring, customizing and switching between different designs, along with example visualizations to demonstrate the range of expression. The resulting system allows for the generation of complex nested charts with a high degree of flexibility and fluidity using a drag and drop interface.},
year = {2020},
pdf = "pdfs/Kristiansen-2020-VIV.pdf",
thumbnails = "images/Kristiansen-2020-VIV.png",
images = "images/Kristiansen-2020-VIV.jpg",
project = "MetaVis"
}