Position within the page tree

Institute for Visualization and Interactive Systems
Team
Michael Sedlmair

Michael Sedlmair

Prof. Dr.

Professur Augmented Reality und Virtual Reality
Visualisierungsinstitut der Universität Stuttgart (VISUS)
Arbeitsgruppe Sedlmair

Contact

+49 711 685 88603
+49 711 685 88610
Email
Business card (VCF)

Allmandring 19
70569 Stuttgart
Deutschland
Room: 01.010

Publications

2023
1. R. Bauer et al., “Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment,” Transport in Porous Media, 2023, doi: 10.1007/s11242-023-02019-y.
  - BibTeX
  - Link
  BibTeX
  @article{bauer2023visual, abstract = {We study the question of how visual analysis can support the comparison of spatio-temporal ensemble data of liquid and gas flow in porous media. To this end, we focus on a case study, in which nine different research groups concurrently simulated the process of injecting CO2 into the subsurface. We explore different data aggregation and interactive visualization approaches to compare and analyze these nine simulations. In terms of data aggregation, one key component is the choice of similarity metrics that define the relationship between different simulations. We test different metrics and find that using the machine-learning model “S4” (tailored to the present study) as metric provides the best visualization results. Based on that, we propose different visualization methods. For overviewing the data, we use dimensionality reduction methods that allow us to plot and compare the different simulations in a scatterplot. To show details about the spatio-temporal data of each individual simulation, we employ a space-time cube volume rendering. All views support linking and brushing interaction to allow users to select and highlight subsets of the data simultaneously across multiple views. We use the resulting interactive, multi-view visual analysis tool to explore the nine simulations and also to compare them to data from experimental setups. Our main findings include new insights into ranking of simulation results with respect to experimental data, and the development of gravity fingers in simulations.}, archiveprefix = {arXiv}, author = {Bauer, Ruben and Ngo, Quynh Quang and Reina, Guido and Frey, Steffen and Flemisch, Bernd and Hauser, Helwig and Ertl, Thomas and Sedlmair, Michael}, doi = {10.1007/s11242-023-02019-y}, eprint = {2302.05447}, journal = {Transport in Porous Media}, primaryclass = {cs.HC}, title = {Visual Ensemble Analysis of Fluid Flow in Porous Media across Simulation Codes and Experiment}, url = {/brokenurl# https://link.springer.com/article/10.1007/s11242-023-02019-y#citeas}, year = 2023 }
  Link
  /brokenurl# https://link.springer.com/article/10.1007/s11242-023-02019-y#citeas
2. S. Beck, N. Doerr, F. Schmierer, M. Sedlmair, and S. Koch, “Visual Planning and Analysis of Latin Formation Dance Patterns,” in EuroVis 2023 - Posters, C. Gillmann, M. Krone, and S. Lenti, Eds., in EuroVis 2023 - Posters. The Eurographics Association, 2023. doi: 10.2312/evp.20231076.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.2312:evp.20231076, author = {Beck, Samuel and Doerr, Nina and Schmierer, Fabian and Sedlmair, Michael and Koch, Steffen}, booktitle = {EuroVis 2023 - Posters}, doi = {10.2312/evp.20231076}, editor = {Gillmann, Christina and Krone, Michael and Lenti, Simone}, isbn = {978-3-03868-220-2}, publisher = {The Eurographics Association}, title = {Visual Planning and Analysis of Latin Formation Dance Patterns}, url = {https://doi.org/10.2312/evp.20231076}, year = 2023 }
  Link
  https://doi.org/10.2312/evp.20231076
3. K.-T. Chen et al., “Reading Strategies for Graph Visualizations That Wrap Around in Torus Topology,” in Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, in Proceedings of the 2023 Symposium on Eye Tracking Research and Applications. Tubingen, Germany: Association for Computing Machinery, 2023. doi: 10.1145/3588015.3589841.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3588015.3589841, abstract = {We investigate reading strategies for node-link diagrams that wrap around the boundaries in a flattened torus topology by examining eye tracking data recorded in a previous controlled study. Prior work showed that torus drawing affords greater flexibility in clutter reduction than traditional node-link representations, but impedes link-and-path exploration tasks, while repeating tiles around boundaries aids comprehension. However, it remains unclear what strategies users apply in different wrapping settings. This is important for design implications for future work on more effective wrapped visualizations for network applications, and cyclic data that could benefit from wrapping. We perform visual-exploratory data analysis of gaze data, and conduct statistical tests derived from the patterns identified. Results show distinguishable gaze behaviors, with more visual glances and transitions between areas of interest in the non-replicated layout. Full-context has more successful visual searches than partial-context, but the gaze allocation indicates that the layout could be more space-efficient.}, address = {New York, NY, USA}, articleno = {67}, author = {Chen, Kun-Ting and Ngo, Quynh Quang and Kurzhals, Kuno and Marriott, Kim and Dwyer, Tim and Sedlmair, Michael and Weiskopf, Daniel}, booktitle = {Proceedings of the 2023 Symposium on Eye Tracking Research and Applications}, doi = {10.1145/3588015.3589841}, isbn = {9798400701504}, location = {Tubingen, Germany}, numpages = {7}, publisher = {Association for Computing Machinery}, series = {ETRA '23}, title = {Reading Strategies for Graph Visualizations That Wrap Around in Torus Topology}, url = {https://doi.org/10.1145/3588015.3589841}, year = 2023 }
  Link
  https://doi.org/10.1145/3588015.3589841
4. N. Doerr, K. Angerbauer, M. Reinelt, and M. Sedlmair, “Bees, Birds and Butterflies: Investigating the Influence of Distractors on Visual Attention Guidance Techniques,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. Hamburg, Germany: Association for Computing Machinery, 2023. doi: 10.1145/3544549.3585816.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3544549.3585816, abstract = {Visual attention guidance methods direct the viewer’s gaze in immersive environments by visually highlighting elements of interest. The highlighting can be done, for instance, by adding a colored circle around elements, adding animated swarms (HiveFive), or removing objects from one eye in a stereoscopic display (Deadeye). We contribute a controlled user experiment (N=30) comparing these three techniques under the influence of visual distractors, such as bees flying by. Our results show that Circle and HiveFive performed best in terms of task performance and qualitative feedback, and were largely robust against different levels of distractions. Furthermore, we discovered a high mental demand for Deadeye.}, address = {New York, NY, USA}, articleno = {51}, author = {Doerr, Nina and Angerbauer, Katrin and Reinelt, Melissa and Sedlmair, Michael}, booktitle = {Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3544549.3585816}, isbn = {9781450394222}, location = {Hamburg, Germany}, numpages = {7}, publisher = {Association for Computing Machinery}, series = {CHI EA '23}, title = {Bees, Birds and Butterflies: Investigating the Influence of Distractors on Visual Attention Guidance Techniques}, url = {https://doi.org/10.1145/3544549.3585816}, year = 2023 }
  Link
  https://doi.org/10.1145/3544549.3585816
5. J. Eirich et al., “ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 8, Art. no. 8, 2023, doi: 10.1109/TVCG.2023.3279857.
  - BibTeX
  - Link
  BibTeX
  @article{10155238, abstract = {We present ManuKnowVis, the result of a design study, in which we contextualize data from multiple knowledge repositories of a manufacturing process for battery modules used in electric vehicles. In data-driven analyses of manufacturing data, we observed a discrepancy between two stakeholder groups involved in serial manufacturing processes: Knowledge providers (e.g., engineers) have domain knowledge about the manufacturing process but have difficulties in implementing data-driven analyses. Knowledge consumers (e.g., data scientists) have no first-hand domain knowledge but are highly skilled in performing data-driven analyses. ManuKnowVis bridges the gap between providers and consumers and enables the creation and completion of manufacturing knowledge. We contribute a multi-stakeholder design study, where we developed ManuKnowVis in three main iterations with consumers and providers from an automotive company. The iterative development led us to a multiple linked view tool, in which, on the one hand, providers can describe and connect individual entities (e.g., stations or produced parts) of the manufacturing process based on their domain knowledge. On the other hand, consumers can leverage this enhanced data to better understand complex domain problems, thus, performing data analyses more efficiently. As such, our approach directly impacts the success of data-driven analyses from manufacturing data. To demonstrate the usefulness of our approach, we carried out a case study with seven domain experts, which demonstrates how providers can externalize their knowledge and consumers can implement data-driven analyses more efficiently.}, author = {Eirich, Joscha and Jäckle, Dominik and Sedlmair, Michael and Wehner, Christoph and Schmid, Ute and Bernard, Jürgen and Schreck, Tobias}, doi = {10.1109/TVCG.2023.3279857}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 8, pages = {3441-3457}, title = {ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories}, url = {https://ieeexplore.ieee.org/document/10155238}, volume = 29, year = 2023 }
  Link
  https://ieeexplore.ieee.org/document/10155238
6. T. Ge et al., “Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–13, 2023, doi: 10.1109/TVCG.2023.3284499.
  - BibTeX
  - Link
  BibTeX
  @article{10147241, abstract = {We propose to use optimally ordered orthogonal neighbor-joining (O 3 NJ) trees as a new way to visually explore cluster structures and outliers in multi-dimensional data. Neighbor-joining (NJ) trees are widely used in biology, and their visual representation is similar to that of dendrograms. The core difference to dendrograms, however, is that NJ trees correctly encode distances between data points, resulting in trees with varying edge lengths. We optimize NJ trees for their use in visual analysis in two ways. First, we propose to use a novel leaf sorting algorithm that helps users to better interpret adjacencies and proximities within such a tree. Second, we provide a new method to visually distill the cluster tree from an ordered NJ tree. Numerical evaluation and three case studies illustrate the benefits of this approach for exploring multi-dimensional data in areas such as biology or image analysis.}, author = {Ge, Tong and Luo, Xu and Wang, Yunhai and Sedlmair, Michael and Cheng, Zhanglin and Zhao, Ying and Liu, Xin and Deussen, Oliver and Chen, Baoquan}, doi = {10.1109/TVCG.2023.3284499}, journal = {IEEE Transactions on Visualization and Computer Graphics}, pages = {1-13}, title = {Optimally Ordered Orthogonal Neighbor Joining Trees for Hierarchical Cluster Analysis}, url = {https://ieeexplore.ieee.org/document/10147241}, year = 2023 }
  Link
  https://ieeexplore.ieee.org/document/10147241
7. F. Heyen, Q. Q. Ngo, and M. Sedlmair, “Visual Overviews for Sheet Music Structure,” in Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023, in Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023. Nov. 2023, pp. 692--699. doi: doi.org/10.48550/arXiv.2308.06140.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{heyen2023visual, abstract = {We propose different methods for alternative representation and visual augmentation of sheet music that help users gain an overview of general structure, repeating patterns, and the similarity of segments. To this end, we explored mapping the overall similarity between sections or bars to colors. For these mappings, we use dimensionality reduction or clustering to assign similar segments to similar colors and vice versa. To provide a better overview, we further designed simplified music notation representations, including hierarchical and compressed encodings. These overviews allow users to display whole pieces more compactly on a single screen without clutter and to find and navigate to distant segments more quickly. Our preliminary evaluation with guitarists and tablature shows that our design supports users in tasks such as analyzing structure, finding repetitions, and determining the similarity of specific segments to others.}, archiveprefix = {arXiv}, author = {Heyen, Frank and Ngo, Quynh Quang and Sedlmair, Michael}, booktitle = {Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR) 2023}, doi = {doi.org/10.48550/arXiv.2308.06140}, eprint = {2308.06140}, month = {11}, pages = { 692--699}, primaryclass = {cs.HC}, title = {Visual Overviews for Sheet Music Structure}, url = {https://arxiv.org/abs/2308.06140}, year = 2023 }
  Link
  https://arxiv.org/abs/2308.06140
8. C. Morariu, A. Bibal, R. Cutura, B. Frénay, and M. Sedlmair, “Predicting User Preferences of Dimensionality Reduction Embedding Quality,” IEEE Transactions on Visualization and Computer Graphics, vol. 29, no. 1, Art. no. 1, 2023, doi: 10.1109/TVCG.2022.3209449.
  - BibTeX
  - Link
  BibTeX
  @article{morariu2023dumbledr, abstract = {A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional hyper-parametrization (e.g., t-SNE, UMAP, etc.). Recent studies are showing that people often use dimensionality reduction as a black-box regardless of the specific properties the method itself preserves. Hence, evaluating and comparing 2D embeddings is usually qualitatively decided, by setting embeddings side-by-side and letting human judgment decide which embedding is the best. In this work, we propose a quantitative way of evaluating embeddings, that nonetheless places human perception at the center. We run a comparative study, where we ask people to select “good” and “misleading” views between scatterplots of low-dimensional embeddings of image datasets, simulating the way people usually select embeddings. We use the study data as labels for a set of quality metrics for a supervised machine learning model whose purpose is to discover and quantify what exactly people are looking for when deciding between embeddings. With the model as a proxy for human judgments, we use it to rank embeddings on new datasets, explain why they are relevant, and quantify the degree of subjectivity when people select preferred embeddings.}, author = {Morariu, Cristina and Bibal, Adrien and Cutura, Rene and Fr{\'e}nay, Beno{\^\i}t and Sedlmair, Michael}, doi = {10.1109/TVCG.2022.3209449}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 1, pages = {745-755}, title = {Predicting User Preferences of Dimensionality Reduction Embedding Quality}, url = {https://ieeexplore.ieee.org/document/9904619}, volume = 29, year = 2023 }
  Link
  https://ieeexplore.ieee.org/document/9904619
9. N. Pathmanathan, S. Öney, M. Becher, M. Sedlmair, D. Weiskopf, and K. Kurzhals, “Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments,” Computer Graphics Forum, vol. 42, no. 3, Art. no. 3, 2023, doi: https://doi.org/10.1111/cgf.14838.
  - BibTeX
  - Link
  BibTeX
  @article{https://doi.org/10.1111/cgf.14838, abstract = {Abstract The distribution of visual attention can be evaluated using eye tracking, providing valuable insights into usability issues and interaction patterns. However, when used in real, augmented, and collaborative environments, new challenges arise that go beyond desktop scenarios and purely virtual environments. Toward addressing these challenges, we present a visualization technique that provides complementary views on the movement and eye tracking data recorded from multiple people in real-world environments. Our method is based on a space-time cube visualization and a linked 3D replay of recorded data. We showcase our approach with an experiment that examines how people investigate an artwork collection. The visualization provides insights into how people moved and inspected individual pictures in their spatial context over time. In contrast to existing methods, this analysis is possible for multiple participants without extensive annotation of areas of interest. Our technique was evaluated with a think-aloud experiment to investigate analysis strategies and an interview with domain experts to examine the applicability in other research fields.}, author = {Pathmanathan, N. and Öney, S. and Becher, M. and Sedlmair, M. and Weiskopf, D. and Kurzhals, K.}, doi = {https://doi.org/10.1111/cgf.14838}, eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14838}, journal = {Computer Graphics Forum}, number = 3, pages = {385-396}, title = {Been There, Seen That: Visualization of Movement and 3D Eye Tracking Data from Real-World Environments}, url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14838}, volume = 42, year = 2023 }
  Link
  https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14838
10. S. Rigling, X. Yu, and M. Sedlmair, “‘In Your Face!’: Visualizing Fitness Tracker Data in Augmented Reality,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. Hamburg, Germany: Association for Computing Machinery, 2023. doi: 10.1145/3544549.3585912.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3544549.3585912, abstract = {The benefits of augmented reality (AR) have been demonstrated in both medicine and fitness, while its application in areas where these two fields overlap has been barely explored. We argue that AR opens up new opportunities to interact with, understand and share personal health data. To this end, we developed an app prototype that uses a Snapchat-like face filter to visualize personal health data from a fitness tracker in AR. We tested this prototype in two pilot studies and found that AR does have potential in this type of application. We suggest that AR cannot replace the current interfaces of smartwatches and mobile apps, but it can pick up where current technology falls short in creating intrinsic motivation and personal health awareness. We also provide ideas for future work in this direction.}, address = {New York, NY, USA}, articleno = {176}, author = {Rigling, Sebastian and Yu, Xingyao and Sedlmair, Michael}, booktitle = {Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3544549.3585912}, isbn = {9781450394222}, location = {Hamburg, Germany}, numpages = {7}, publisher = {Association for Computing Machinery}, series = {CHI EA '23}, title = {“In Your Face!”: Visualizing Fitness Tracker Data in Augmented Reality}, url = {https://doi.org/10.1145/3544549.3585912}, year = 2023 }
  Link
  https://doi.org/10.1145/3544549.3585912
11. M. Wieland, M. Sedlmair, and T.-K. Machulla, “VR, Gaze, and Visual Impairment: An Exploratory Study of the Perception of Eye Contact across Different Sensory Modalities for People with Visual Impairments in Virtual Reality,” in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, in Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems. Hamburg, Germany: Association for Computing Machinery, 2023. doi: 10.1145/3544549.3585726.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3544549.3585726, abstract = {As social virtual reality (VR) becomes more popular, avatars are being designed with realistic behaviors incorporating non-verbal cues like eye contact. However, perceiving eye contact during a conversation can be challenging for people with visual impairments. VR presents an opportunity to display eye contact cues in alternative ways, making them perceivable for people with visual impairments. We performed an exploratory study to gain initial insights on designing eye contact cues for people with visual impairments, including a focus group for a deeper understanding of the topic. We implemented eye contact cues via visual, auditory, and tactile sensory modalities in VR and tested these approaches with eleven participants with visual impairments and collected qualitative feedback. The results show that visual cues indicating the gaze direction were preferred, but auditory and tactile cues were also prevalent as they do not superimpose additional visual information.}, address = {New York, NY, USA}, articleno = {313}, author = {Wieland, Markus and Sedlmair, Michael and Machulla, Tonja-Katrin}, booktitle = {Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3544549.3585726}, isbn = {9781450394222}, location = {Hamburg, Germany}, numpages = {6}, publisher = {Association for Computing Machinery}, series = {CHI EA '23}, title = {VR, Gaze, and Visual Impairment: An Exploratory Study of the Perception of Eye Contact across Different Sensory Modalities for People with Visual Impairments in Virtual Reality}, url = {https://doi.org/10.1145/3544549.3585726}, year = 2023 }
  Link
  https://doi.org/10.1145/3544549.3585726
12. A.-K. Wortmeier, A. S. Calepso, C. Kropp, M. Sedlmair, and D. Weiskopf, “Configuring augmented reality users: analysing YouTube commercials to understand industry expectations,” Behaviour & Information Technology, vol. 0, no. 0, Art. no. 0, Jan. 2023, doi: 10.1080/0144929X.2022.2163693.
  - BibTeX
  - Link
  BibTeX
  @article{doi:10.1080/0144929X.2022.2163693, abstract = {Commercial videos are often used to familiarise potential buyers and users with new technologies and their possibilities. In addition, presenting visions of future applications is a way to configure users and define social worlds of technology use. We analyse 30 YouTube videos featuring augmented reality (AR) devices in industrial manufacturing and construction, to explore how these commercial videos situate AR technology and future users by showcasing techno-euphoric promises and imagined use cases. With a video analysis based on Grounded Theory and Situational Analysis, we untangle the promises of AR for manufacturing and construction work; second, we present two prevailing configurations of AR users: ‘experts in situ’ and ‘smart dummies’; and third, we discuss how YouTube videos put forward developmental expectations. In addition, we identify discrepancies between expectations and foreseeable requirements in construction work. Finally, our research could contribute to a more holistic understanding of workplaces and socially robust AR applications. }, author = {Wortmeier, Ann-Kathrin and Calepso, Aimée Sousa and Kropp, Cordula and Sedlmair, Michael and Weiskopf, Daniel}, doi = {10.1080/0144929X.2022.2163693}, eprint = {https://doi.org/10.1080/0144929X.2022.2163693}, journal = {Behaviour \& Information Technology}, month = {01}, number = 0, pages = {1-16}, publisher = {Taylor & Francis}, title = {Configuring augmented reality users: analysing YouTube commercials to understand industry expectations}, url = {https://doi.org/10.1080/0144929X.2022.2163693 }, volume = 0, year = 2023 }
  Link
  https://doi.org/10.1080/0144929X.2022.2163693
13. S. Öney, N. Pathmanathan, M. Becher, M. Sedlmair, D. Weiskopf, and K. Kurzhals, “Visual Gaze Labeling for Augmented Reality Studies,” Computer Graphics Forum, vol. 42, no. 3, Art. no. 3, 2023, doi: 10.1111/cgf.14837.
  - BibTeX
  - Link
  BibTeX
  @article{N2090D:2023, abstract = {Augmented Reality (AR) provides new ways for situated visualization and human-computer interaction in physical environments. Current evaluation procedures for AR applications rely primarily on questionnaires and interviews, providing qualitative means to assess usability and task solution strategies. Eye tracking extends these existing evaluation methodologies by providing indicators for visual attention to virtual and real elements in the environment. However, the analysis of viewing behavior, especially the comparison of multiple participants, is difficult to achieve in AR. Specifically, the definition of areas of interest (AOIs), which is often a prerequisite for such analysis, is cumbersome and tedious with existing approaches. To address this issue, we present a new visualization approach to define AOIs, label fixations, and investigate the resulting annotated scanpaths. Our approach utilizes automatic annotation of gaze on virtual objects and an image-based approach that also considers spatial context for the manual annotation of objects in the real world. Our results show, that with our approach, eye tracking data from AR scenes can be annotated and analyzed flexibly with respect to data aspects and annotation strategies.}, author = {Öney, Seyda and Pathmanathan, Nelusa and Becher, Michael and Sedlmair, Michael and Weiskopf, Daniel and Kurzhals, Kuno}, doi = {10.1111/cgf.14837}, editor = {Bujack, Roxana and Archambault, Daniel and Schreck, Tobias}, journal = {Computer Graphics Forum}, number = 3, pages = {373-38412 pages}, publisher = {The Eurographics Association and John Wiley & Sons Ltd.}, title = {Visual Gaze Labeling for Augmented Reality Studies}, url = {https://diglib.eg.org/xmlui/handle/10.1111/cgf14837}, volume = 42, year = 2023 }
  Link
  https://diglib.eg.org/xmlui/handle/10.1111/cgf14837
2022
1. M. Abdelaal et al., “Visualization for Architecture, Engineering, and Construction: Shaping the Future of Our Built World,” IEEE Computer Graphics and Applications, vol. 42, no. 2, Art. no. 2, Mar. 2022, doi: 10.1109/MCG.2022.3149837.
  - BibTeX
  BibTeX
  @article{9709159, abstract = {Our built world is one of the most important factors for a livable future, accounting for massive impact on resource and energy use, as well as climate change, but also the social and economic aspects that come with population growth. The architecture, engineering, and construction industry is facing the challenge that it needs to substantially increase its productivity, let alone the quality of buildings of the future. In this article, we discuss these challenges in more detail, focusing on how digitization can facilitate this transformation of the industry, and link them to opportunities for visualization and augmented reality research. We illustrate solution strategies for advanced building systems based on wood and fiber.}, author = {Abdelaal, Moataz and Amtsberg, Felix and Becher, Michael and Estrada, Rebeca Duque and Kannenberg, Fabian and Calepso, Aimée Sousa and Wagner, Hans Jakob and Reina, Guido and Sedlmair, Michael and Menges, Achim and Weiskopf, Daniel}, doi = {10.1109/MCG.2022.3149837}, issn = {1558-1756}, journal = {IEEE Computer Graphics and Applications}, month = {03}, number = 2, pages = {10-20}, title = {Visualization for Architecture, Engineering, and Construction: Shaping the Future of Our Built World}, volume = 42, year = 2022 }
2. M. Abdelaal, N. D. Schiele, K. Angerbauer, K. Kurzhals, M. Sedlmair, and D. Weiskopf, “Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–11, 2022, doi: 10.1109/TVCG.2022.3209427.
  - BibTeX
  BibTeX
  @article{9908291, abstract = {This work investigates and compares the performance of node-link diagrams, adjacency matrices, and bipartite layouts for visualizing networks. In a crowd-sourced user study (n = 150), we measure the task accuracy and completion time of the three representations for different network classes and properties. In contrast to the literature, which covers mostly topology-based tasks (e.g., path finding) in small datasets, we mainly focus on overview tasks for large and directed networks. We consider three overview tasks on networks with 500 nodes: (T1) network class identification, (T2) cluster detection, and (T3) network density estimation, and two detailed tasks: (T4) node in-degree vs. out-degree and (T5) representation mapping, on networks with 50 and 20 nodes, respectively. Our results show that bipartite layouts are beneficial for revealing the overall network structure, while adjacency matrices are most reliable across the different tasks.}, author = {Abdelaal, Moataz and Schiele, Nathan D. and Angerbauer, Katrin and Kurzhals, Kuno and Sedlmair, Michael and Weiskopf, Daniel}, doi = {10.1109/TVCG.2022.3209427}, issn = {1941-0506}, journal = {IEEE Transactions on Visualization and Computer Graphics}, pages = {1-11}, title = {Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations}, year = 2022 }
3. M. Abdelaal, N. D. Schiele, K. Angerbauer, K. Kurzhals, M. Sedlmair, and D. Weiskopf, “Supplemental Materials for: Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations.” DaRUS, 2022. doi: 10.18419/DARUS-3100.
  - BibTeX
  - Link
  BibTeX
  @misc{https://doi.org/10.18419/darus-3100, author = {Abdelaal, Moataz and Schiele, Nathan Daniel and Angerbauer, Katrin and Kurzhals, Kuno and Sedlmair, Michael and Weiskopf, Daniel}, doi = {10.18419/DARUS-3100}, publisher = {DaRUS}, title = {Supplemental Materials for: Comparative Evaluation of Bipartite, Node-Link, and Matrix-Based Network Representations}, url = {https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3100}, year = 2022 }
  Link
  https://darus.uni-stuttgart.de/citation?persistentId=doi:10.18419/darus-3100
4. A. Achberger, P. Arulrajah, M. Sedlmair, and K. Vidackovic, “STROE: An Ungrounded String-Based Weight Simulation Device,” in IEEE Conference on Virtual Reality and 3D User Interfaces (VR), in IEEE Conference on Virtual Reality and 3D User Interfaces (VR). 2022, pp. 112–120. doi: 10.1109/VR51125.2022.00029.
  - BibTeX
  BibTeX
  @inproceedings{9756818, author = {Achberger, Alexander and Arulrajah, Pirathipan and Sedlmair, Michael and Vidackovic, Kresimir}, booktitle = {IEEE Conference on Virtual Reality and 3D User Interfaces (VR)}, doi = {10.1109/VR51125.2022.00029}, pages = {112-120}, title = {STROE: An Ungrounded String-Based Weight Simulation Device}, year = 2022 }
5. A. Achberger, F. Heyen, K. Vidackovic, and M. Sedlmair, “Touching data with PropellerHand,” Journal of Visualization, 2022, doi: 10.1007/s12650-022-00859-2.
  - BibTeX
  - Link
  BibTeX
  @article{achberger2022touching, abstract = {Immersive analytics often takes place in virtual environments which promise the users immersion. To fulfill this promise, sensory feedback, such as haptics, is an important component, which is however not well supported yet. Existing haptic devices are often expensive, stationary, or occupy the user’s hand, preventing them from grasping objects or using a controller. We propose PropellerHand, an ungrounded hand-mounted haptic device with two rotatable propellers, that allows exerting forces on the hand without obstructing hand use. PropellerHand is able to simulate feedback such as weight and torque by generating thrust up to 11 N in 2-DOF and a torque of 1.87 Nm in 2-DOF. Its design builds on our experience from quantitative and qualitative experiments with different form factors and parts. We evaluated our prototype through a qualitative user study in various VR scenarios that required participants to manipulate virtual objects in different ways, while changing between torques and directional forces. Results show that PropellerHand improves users’ immersion in virtual reality. Additionally, we conducted a second user study in the field of immersive visualization to investigate the potential benefits of PropellerHand there.}, author = {Achberger, Alexander and Heyen, Frank and Vidackovic, Kresimir and Sedlmair, Michael}, doi = {10.1007/s12650-022-00859-2}, journal = {Journal of Visualization}, title = {Touching data with PropellerHand}, url = {https://doi.org/10.1007/s12650-022-00859-2}, year = 2022 }
  Link
  https://doi.org/10.1007/s12650-022-00859-2
6. K. Angerbauer et al., “Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures,” in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems, in Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems. New Orleans, LA, USA: Association for Computing Machinery, 2022. doi: 10.1145/3491102.3502133.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3491102.3502133, abstract = {We present an exploratory study on the accessibility of images in publications when viewed with color vision deficiencies (CVDs). The study is based on 1,710 images sampled from a visualization dataset (VIS30K) over five years. We simulated four CVDs on each image. First, four researchers (one with a CVD) identified existing issues and helpful aspects in a subset of the images. Based on the resulting labels, 200 crowdworkers provided  30,000 ratings on present CVD issues in the simulated images. We analyzed this data for correlations, clusters, trends, and free text comments to gain a first overview of paper figure accessibility. Overall, about 60 % of the images were rated accessible. Furthermore, our study indicates that accessibility issues are subjective and hard to detect. On a meta-level, we reflect on our study experience to point out challenges and opportunities of large-scale accessibility studies for future research directions.}, address = {New York, NY, USA}, articleno = {134}, author = {Angerbauer, Katrin and Rodrigues, Nils and Cutura, Rene and \"{O}ney, Seyda and Pathmanathan, Nelusa and Morariu, Cristina and Weiskopf, Daniel and Sedlmair, Michael}, booktitle = {Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3491102.3502133}, isbn = {9781450391573}, location = {New Orleans, LA, USA}, numpages = {23}, publisher = {Association for Computing Machinery}, series = {CHI '22}, title = {Accessibility for Color Vision Deficiencies: Challenges and Findings of a Large Scale Study on Paper Figures}, url = {https://doi.org/10.1145/3491102.3502133}, year = 2022 }
  Link
  https://doi.org/10.1145/3491102.3502133
7. K. Angerbauer and M. Sedlmair, “Toward Inclusion and Accessibility in Visualization Research: Speculations on Challenges, Solution Strategies, and Calls for Action (Position Paper),” in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV), in 2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV). Oct. 2022, pp. 20–27. doi: 10.1109/BELIV57783.2022.00007.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{9978448, abstract = {Inclusion and accessibility in visualization research have gained increasing attention in recent years. However, many challenges still remain to be solved on the road toward a more inclusive, shared-experience-driven visualization design and evaluation process. In this position paper, we discuss challenges and speculate about potential solutions, based on related work, our own research, as well as personal experiences. The goal of this paper is to start discussions on the role of accessibility and inclusion in visualization design and evaluation.}, author = {Angerbauer, Katrin and Sedlmair, Michael}, booktitle = {2022 IEEE Evaluation and Beyond - Methodological Approaches for Visualization (BELIV)}, doi = {10.1109/BELIV57783.2022.00007}, month = {10}, pages = {20-27}, title = {Toward Inclusion and Accessibility in Visualization Research: Speculations on Challenges, Solution Strategies, and Calls for Action (Position Paper)}, url = {https://ieeexplore.ieee.org/document/9978448}, year = 2022 }
  Link
  https://ieeexplore.ieee.org/document/9978448
8. S. Dosdall, K. Angerbauer, L. Merino, M. Sedlmair, and D. Weiskopf, “Toward In-Situ Authoring of Situated Visualization with Chorded Keyboards,” in VINCI ’22: Proceedings of the 15th International Symposium on Visual Information Communication and Interaction, M. Burch, G. Wallner, and D. Limberger, Eds., in VINCI ’22: Proceedings of the 15th International Symposium on Visual Information Communication and Interaction. ACM, Aug. 2022, pp. 1–5. doi: 10.1145/3554944.3554970.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{DosdallAMSW22, abstract = {Authoring situated visualizations in-situ is challenging due to the need of writing code in a mobile and highly dynamic fashion. To provide better support for that, we define requirements for text input methods that target situated visualization authoring. We identify wearable chorded keyboards as a potentially suitable method that fulfills some of these requirements. To further investigate this approach, we tailored a chorded keyboard device to visualization authoring, developed a learning application, and conducted a pilot user study. Our results confirm that learning a high number of chords is the main barrier for adoption, as in other application areas. Based on that, we discuss ideas on how chorded keyboards with a strongly reduced alphabet, hand gestures, and voice recognition might be used as a viable, multi-modal support for authoring situated visualizations in-situ.}, author = {Dosdall, Sarah and Angerbauer, Katrin and Merino, Leonel and Sedlmair, Michael and Weiskopf, Daniel}, booktitle = {VINCI '22: Proceedings of the 15th International Symposium on Visual Information Communication and Interaction}, doi = {10.1145/3554944.3554970}, editor = {Burch, Michael and Wallner, G{\"{u}}nter and Limberger, Daniel}, month = {08}, number = 14, pages = {1-5}, publisher = {{ACM}}, title = {Toward In-Situ Authoring of Situated Visualization with Chorded Keyboards}, url = {https://doi.org/10.1145/3554944.3554970}, year = 2022 }
  Link
  https://doi.org/10.1145/3554944.3554970
9. L. Eirich, M. Münch, D. Jäckle, M. Sedlmair, J. Bonart, and T. Schreck, “RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines,” 2022.
  - BibTeX
  BibTeX
  @article{noauthororeditor2022design, author = {Eirich, L and Münch, M. and Jäckle, D. and Sedlmair, M. and Bonart, J. and Schreck, T.}, title = {RfX: A Design Study for the Interactive Exploration of a Random Forest to Enhance Testing Procedures for Electrical Engines}, year = 2022 }
10. P. Fleck, A. Sousa Calepso, S. Hubenschmid, M. Sedlmair, and D. Schmalstieg, “RagRug: A Toolkit for Situated Analytics,” IEEE Transactions on Visualization and Computer Graphics, 2022, doi: 10.1109/TVCG.2022.3157058.
  - BibTeX
  - Link
  BibTeX
  @article{Fleck2022-03-07RagRu-56856, abstract = {We present RagRug, an open-source toolkit for situated analytics. The abilities of RagRug go beyond previous immersive analytics toolkits by focusing on specific requirements emerging when using augmented reality (AR) rather than virtual reality. RagRug combines state of the art visual encoding capabilities with a comprehensive physical-virtual model, which lets application developers systematically describe the physical objects in the real world and their role in AR. We connect AR visualization with data streams from the Internet of Things using distributed dataflow. To this aim, we use reactive programming patterns so that visualizations become context-aware, i.e., they adapt to events coming in from the environment. The resulting authoring system is low-code; it emphasises describing the physical and the virtual world and the dataflow between the elements contained therein. We describe the technical design and implementation of RagRug, and report on five example applications illustrating the toolkit's abilities. }, author = {Fleck, Philipp and Sousa Calepso, Aimée and Hubenschmid, Sebastian and Sedlmair, Michael and Schmalstieg, Dieter}, doi = {10.1109/TVCG.2022.3157058}, issn = {1077-2626}, journal = {IEEE Transactions on Visualization and Computer Graphics}, title = {RagRug: A Toolkit for Situated Analytics}, url = {https://pubmed.ncbi.nlm.nih.gov/35254986/}, year = 2022 }
  Link
  https://pubmed.ncbi.nlm.nih.gov/35254986/
11. P. Gebhardt, X. Yu, A. Köhn, and M. Sedlmair, “MolecuSense: Using Force-Feedback Gloves for Creating and Interacting with Ball-and-Stick Molecules in VR,” in Proceedings of the 15th International Symposium on Visual Information Communication and Interaction, in Proceedings of the 15th International Symposium on Visual Information Communication and Interaction. Chur, Switzerland: Association for Computing Machinery, Oct. 2022, pp. 1–5. doi: 10.1145/3554944.3554956.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{Gebhardt2022, abstract = {We contribute MolecuSense, a virtual version of a physical molecule construction kit, based on visualization in Virtual Reality (VR) and interaction with force-feedback gloves. Targeting at chemistry education, our goal is to make virtual molecule structures more tangible. Results of an initial user study indicate that the VR molecular construction kit was positively received. Compared to a physical construction kit, the VR molecular construction kit is on the same level in terms of natural interaction. Besides, it fosters the typical digital advantages though, such as saving, exporting, and sharing of molecules. Feedback from the study participants has also revealed potential future avenues for tangible molecule visualizations.}, address = {New York, NY, USA}, author = {Gebhardt, Patrick and Yu, Xingyao and Köhn, Andreas and Sedlmair, Michael}, booktitle = {Proceedings of the 15th International Symposium on Visual Information Communication and Interaction}, day = 31, doi = {10.1145/3554944.3554956}, isbn = {9781450398060}, location = {Chur, Switzerland}, month = {10}, pages = {1–5}, publisher = {Association for Computing Machinery}, series = {VINCI '22}, title = {MolecuSense: Using Force-Feedback Gloves for Creating and Interacting with Ball-and-Stick Molecules in VR}, url = {https://doi.org/10.1145/3554944.3554956}, year = 2022 }
  Link
  https://doi.org/10.1145/3554944.3554956
12. F. Heyen, D. Aygün, and M. Sedlmair, “A Web-Based MIDI Controller for Music Live Coding.” 2022. [Online]. Available: https://ismir2022program.ismir.net/lbd_379.html
  - BibTeX
  - Link
  BibTeX
  @misc{heyen2022webbased, abstract = {We contribute an interactive visual frontend to live coding environments, which allows live coders and performers to influence the behavior of their code more quickly and efficiently. Users can trigger actions and change parameters via instruments, buttons, and sliders, instead of only inside the code. For instance, toggling a loop or controlling a fading effect through mouse or touch interaction on a screen is faster than editing code. While this kind of control has already been possible with hardware MIDI devices, we provide a more accessible, easy-to-use, and customizable alternative that only requires a web browser. With examples, we show how users perform live-coded music faster and more easily with our design compared to using pure code.}, author = {Heyen, Frank and Aygün, Dilara and Sedlmair, Michael}, howpublished = {International Society for Music Information Retrieval Conference 2022 Late-Breaking Demo }, title = {A Web-Based MIDI Controller for Music Live Coding}, url = {https://ismir2022program.ismir.net/lbd_379.html}, year = 2022 }
  Link
  https://ismir2022program.ismir.net/lbd_379.html
13. F. Heyen, Q. Q. Ngo, K. Kurzhals, and M. Sedlmair, “Data-Driven Visual Reflection on Music Instrument Practice,” in ACM CHI Workshop on Intelligent Music Interfaces (IMI), in ACM CHI Workshop on Intelligent Music Interfaces (IMI). ACM, 2022. doi: 10.48550/arxiv.2203.13320.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{heyen2022datadriven, abstract = {We propose a data-driven approach to music instrument practice that allows studying patterns and long-term trends through visualization. Inspired by life logging and fitness tracking, we imagine musicians to record their practice sessions over the span of months or years. The resulting data in the form of MIDI or audio recordings can then be analyzed sporadically to track progress and guide decisions. Toward this vision, we started exploring various visualization designs together with a group of nine guitarists, who provided us with data and feedback over the course of three months.}, author = {Heyen, Frank and Ngo, Quynh Quang and Kurzhals, Kuno and Sedlmair, Michael}, booktitle = {ACM CHI Workshop on Intelligent Music Interfaces (IMI)}, doi = {10.48550/arxiv.2203.13320}, publisher = {ACM}, title = {Data-Driven Visual Reflection on Music Instrument Practice}, url = {https://doi.org/10.48550/arxiv.2203.13320}, year = 2022 }
  Link
  https://doi.org/10.48550/arxiv.2203.13320
14. F. Heyen and M. Sedlmair, “Augmented Reality Visualization for Musical Instrument Learning.” 2022. [Online]. Available: https://ismir2022program.ismir.net/lbd_376.html
  - BibTeX
  - Link
  BibTeX
  @misc{heyen2022augmented, abstract = {We contribute two design studies for augmented reality visualizations that support learning musical instruments. First, we designed simple, glanceable encodings for drum kits, which we display through a projector. As second instrument, we chose guitar and designed visualizations to be displayed either on a screen as an augmented mirror or an an optical see-through AR headset. These modalities allow us to also show information around the instrument and in 3D. We evaluated our prototypes through case studies and our results demonstrate the general effectivity and revealed design-related and technical limitations.}, author = {Heyen, Frank and Sedlmair, Michael}, howpublished = {International Society for Music Information Retrieval Conference 2022 Late-Breaking Demo}, title = {Augmented Reality Visualization for Musical Instrument Learning}, url = {https://ismir2022program.ismir.net/lbd_376.html}, year = 2022 }
  Link
  https://ismir2022program.ismir.net/lbd_376.html
15. N. Hube, A. Achberger, P. Liepert, J. Vogelsang, K. Vidačković, and M. Sedlmair, “Study on the Influence of Upper Limb Representations and Haptic Feedback in Virtual Reality,” in 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), in 2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). 2022, pp. 802–807. doi: 10.1109/ISMAR-Adjunct57072.2022.00172.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{9974563, abstract = {Visual representations are used in various digitization processes to display objects in immersive environments. In industrial environments, the self-representation can be very helpful when reviewing 3D models to conform realistic proportions, particularly in combi-nation with haptic feedback that goes beyond vibrating controllers. For instance, haptic feedback in combination with a virtual representation supports working on use cases where collision is an important part to maintain data quality. To understand the dependency of both, we conducted a pilot study with 15 users from the automotive sector to examine the influence of upper limb representations on haptic feedback in Virtual Reality. Each participant was assigned with one of three upper limb representations which was used in different scenarios using haptic feedback devices. Overall, we found that the realistic arm representation was rated highest in terms of perceived realism and achieved the best task performance.}, author = {Hube, Natalie and Achberger, Alexander and Liepert, Philipp and Vogelsang, Jonas and Vidačković, Krešimir and Sedlmair, Michael}, booktitle = {2022 IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, doi = {10.1109/ISMAR-Adjunct57072.2022.00172}, pages = {802-807}, title = {Study on the Influence of Upper Limb Representations and Haptic Feedback in Virtual Reality}, url = {https://ieeexplore.ieee.org/document/9974563}, year = 2022 }
  Link
  https://ieeexplore.ieee.org/document/9974563
16. N. Hube, K. Vidačković, and M. Sedlmair, “Using Expressive Avatars to Increase Emotion Recognition: A Pilot Study,” in Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHIEA’22), in Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHIEA’22). New York, NY, USA: Association for Computing Machinery, 2022. doi: 10.1145/3491101.3519822.
  - BibTeX
  BibTeX
  @inproceedings{hube2022using, address = {New York, NY, USA}, author = {Hube, Natalie and Vidačković, Krešimir and Sedlmair, Michael}, booktitle = {Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHIEA'22)}, doi = {10.1145/3491101.3519822}, publisher = {Association for Computing Machinery}, title = {Using Expressive Avatars to Increase Emotion Recognition: A Pilot Study}, year = 2022 }
17. K. Klein, M. Sedlmair, and F. Schreiber, “Immersive Analytics: An Overview,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0037.
  - BibTeX
  - Link
  BibTeX
  @article{KleinSedlmairSchreiber+2022+155+168, abstract = {Immersive Analytics is concerned with the systematic examination of the benefits and challenges of using immersive environments for data analysis, and the development of corresponding designs that improve the quality and efficiency of the analysis process. While immersive technologies are now broadly available, practical solutions haven’t received broad acceptance in real-world applications outside of several core areas, and proper guidelines on the design of such solutions are still under development. Both fundamental research and applications bring together topics and questions from several fields, and open a wide range of directions regarding underlying theory, evidence from user studies, and practical solutions tailored towards the requirements of application areas. We give an overview on the concepts, topics, research questions, and challenges.}, author = {Klein, Karsten and Sedlmair, Michael and Schreiber, Falk}, doi = {doi:10.1515/itit-2022-0037}, journal = {it - Information Technology}, lastchecked = {2022-09-20}, number = {4-5}, pages = {155–168}, title = {Immersive Analytics: An Overview}, url = {https://doi.org/10.1515/itit-2022-0037}, volume = 64, year = 2022 }
  Link
  https://doi.org/10.1515/itit-2022-0037
18. Q. Q. Ngo, F. L. Dennig, D. A. Keim, and M. Sedlmair, “Machine Learning Meets Visualization – Experiences and Lessons Learned,” it - Information Technology, vol. 64, no. 4–5, Art. no. 4–5, 2022, doi: doi:10.1515/itit-2022-0034.
  - BibTeX
  - Link
  BibTeX
  @article{NgoDennigKeimSedlmair+2022+169+180, abstract = {In this article, we discuss how Visualization (VIS) with Machine Learning (ML) could mutually benefit from each other. We do so through the lens of our own experience working at this intersection for the last decade. Particularly we focus on describing how VIS supports explaining ML models and aids ML-based Dimensionality Reduction techniques in solving tasks such as parameter space analysis. In the other direction, we discuss approaches showing how ML helps improve VIS, such as applying ML-based automation to improve visualization design. Based on the examples and our own perspective, we describe a number of open research challenges that we frequently encountered in our endeavors to combine ML and VIS.}, author = {Ngo, Quynh Quang and Dennig, Frederik L. and Keim, Daniel A. and Sedlmair, Michael}, doi = {doi:10.1515/itit-2022-0034}, journal = {it - Information Technology}, lastchecked = {2022-09-20}, number = {4-5}, pages = {169–180}, title = {Machine Learning Meets Visualization – Experiences and Lessons Learned}, url = {https://doi.org/10.1515/itit-2022-0034}, volume = 64, year = 2022 }
  Link
  https://doi.org/10.1515/itit-2022-0034
19. S. Rau, F. Heyen, S. Wagner, and M. Sedlmair, “Visualization for AI-Assisted Composing,” in Proceedings of the 23rd International Society for Music Information Retrieval Conference, in Proceedings of the 23rd International Society for Music Information Retrieval Conference. ISMIR, Dec. 2022, pp. 151–159. doi: 10.5281/zenodo.7316618.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{simeon_rau_2022_7316618, abstract = {We propose a visual approach for interactive, AI-assisted composition that serves as a compromise between fully automatic and fully manual composition. Instead of generating a whole piece, the AI takes on the role of an assistant that generates short melodies for the composer to choose from and adapt. In an iterative process, the composer queries the AI for continuations or alternative fill-ins, chooses a suggestion, and adds it to the piece. As listening to many suggestions would take time, we explore different ways to visualize them, to allow the composer to focus on the most interesting-looking melodies. We also present the results of a qualitative evaluation with five composers.}, address = {Bengaluru, India}, author = {Rau, Simeon and Heyen, Frank and Wagner, Stefan and Sedlmair, Michael}, booktitle = {Proceedings of the 23rd International Society for Music Information Retrieval Conference}, doi = {10.5281/zenodo.7316618}, month = {12}, pages = {151-159}, publisher = {ISMIR}, title = {Visualization for AI-Assisted Composing}, url = {https://doi.org/10.5281/zenodo.7316618}, venue = {Bengaluru, India}, year = 2022 }
  Link
  https://doi.org/10.5281/zenodo.7316618
20. G. Richer, A. Pister, M. Abdelaal, J.-D. Fekete, M. Sedlmair, and D. Weiskopf, “Scalability in Visualization,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–15, 2022, doi: 10.1109/TVCG.2022.3231230.
  - BibTeX
  BibTeX
  @article{10003102, abstract = {We introduce a conceptual model for scalability designed for visualization research. With this model, we systematically analyze over 120 visualization publications from 1990 to 2020 to characterize the different notions of scalability in these works. While many papers have addressed scalability issues, our survey identifies a lack of consistency in the use of the term in the visualization research community. We address this issue by introducing a consistent terminology meant to help visualization researchers better characterize the scalability aspects in their research. It also helps in providing multiple methods for supporting the claim that a work is “scalable.” Our model is centered around an effort function with inputs and outputs. The inputs are the problem size and resources, whereas the outputs are the actual efforts, for instance, in terms of computational run time or visual clutter. We select representative examples to illustrate different approaches and facets of what scalability can mean in visualization literature. Finally, targeting the diverse crowd of visualization researchers without a scalability tradition, we provide a set of recommendations for how scalability can be presented in a clear and consistent way to improve fair comparison between visualization techniques and systems and foster reproducibility.}, author = {Richer, Gaëlle and Pister, Alexis and Abdelaal, Moataz and Fekete, Jean-Daniel and Sedlmair, Michael and Weiskopf, Daniel}, doi = {10.1109/TVCG.2022.3231230}, issn = {1941-0506}, journal = {IEEE Transactions on Visualization and Computer Graphics}, pages = {1-15}, title = {Scalability in Visualization}, year = 2022 }
21. A. Sousa Calepso, N. Hube, N. Berenguel Senn, V. Brandt, and M. Sedlmair, “cARdLearner: Using expressive Virtual Agents when learning vocabulary in Augmented Reality,” ACM, 2022.
  - BibTeX
  BibTeX
  @preprint{sousacalepso2022cardlearner, abstract = {Augmented reality (AR) has a diverse range of applications, including language teaching. When studying a foreign language, one of the biggest challenges learners face is memorizing new vocabulary. While augmented holograms are a promising means of supporting this memorization process, few studies have explored their potential in the language learning context. We demonstrate the possibility of using flashcard along with an expressive holographic agent on vocabulary learning. Users scan a flashcard and play an animation that is connected with an emotion related to the word they are seeing. Our goal is to propose an alternative to the traditional use of flashcards, and also introduce another way of using AR in the association process.}, author = {Sousa Calepso, Aimée and Hube, Natalie and Berenguel Senn, Noah and Brandt, Vincent and Sedlmair, Michael}, publisher = {ACM}, title = {cARdLearner: Using expressive Virtual Agents when learning vocabulary in Augmented Reality}, year = 2022 }
2021
1. M. Abbas, E. Ullah, A. Baggag, H. Bensmail, M. Sedlmair, and M. Aupetit, “ClustRank: A Visual Quality Measure Trained on Perceptual Data for Sorting Scatterplots by Cluster Patterns,” arXiv preprint, Technical Report arXiv:2106.00599, 2021. [Online]. Available: https://arxiv.org/pdf/2106.00599.pdf
  - BibTeX
  - Link
  BibTeX
  @techreport{abbas2021arxiv, abstract = {Visual quality measures (VQMs) are designed to support analysts by automatically detecting and quantifying patterns in visualizations. We propose a new data-driven technique called ClustRank that allows to rank scatterplots according to visible grouping patterns. Our model first encodes scatterplots in the parametric space of a Gaussian Mixture Model, and then uses a classifier trained on human judgment data to estimate the perceptual complexity of grouping patterns. The numbers of initial mixture components and final combined groups determine the rank of the scatterplot. ClustRank improves on existing VQM techniques by mimicking human judgments on two-Gaussian cluster patterns, and gives more accuracy when ranking general cluster patterns in scatterplots. We demonstrate its benefit by analyzing kinship data for genome-wide association studies, a domain in which experts rely on the visual analysis of large sets of scatterplots. We make the three benchmark datasets and the ClustRank VQM available for practical use and further improvements.}, author = {Abbas, Mostafa and Ullah, Ehsan and Baggag, Abdelkader and Bensmail, Halima and Sedlmair, Michael and Aupetit, Michael}, institution = {{arXiv} preprint}, number = {arXiv:2106.00599}, title = {{ClustRank}: A Visual Quality Measure Trained on Perceptual Data for Sorting Scatterplots by Cluster Patterns}, type = {Technical Report}, url = {https://arxiv.org/pdf/2106.00599.pdf}, year = 2021 }
  Link
  https://arxiv.org/pdf/2106.00599.pdf
2. A. Achberger, F. Aust, D. Pohlandt, K. Vidackovic, and M. Sedlmair, “STRIVE: String-Based Force Feedback for Automotive Engineering,” in ACM Symposium on User Interface Software and Technology (UIST), in ACM Symposium on User Interface Software and Technology (UIST). 2021, pp. 841--853. doi: 10.1145/3472749.3474790.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{achberger2021uist, abstract = {The large potential of force feedback devices for interacting in Virtual Reality (VR) has been illustrated in a plethora of research prototypes. Yet, these devices are still rarely used in practice and it remains an open challenge how to move this research into practice. To that end, we contribute a participatory design study on the use of haptic feedback devices in the automotive industry. Based on a 10-month observing process with 13 engineers, we developed STRIVE, a string-based haptic feedback device. In addition to the design of STRIVE, this process led to a set of requirements for introducing haptic devices into industrial settings, which center around a need for flexibility regarding forces, comfort, and mobility. We evaluated STRIVE with 16 engineers in five different day-to-day automotive VR use cases. The main results show an increased level of trust and perceived safety as well as further challenges towards moving haptics research into practice.}, author = {Achberger, Alexander and Aust, Fabian and Pohlandt, Daniel and Vidackovic, Kresimir and Sedlmair, Michael}, booktitle = {ACM Symposium on User Interface Software and Technology (UIST)}, doi = {10.1145/3472749.3474790}, pages = {841--853}, title = {{STRIVE}: String-Based Force Feedback for Automotive Engineering}, url = {https://doi.org/10.1145/3472749.3474790}, year = 2021 }
  Link
  https://doi.org/10.1145/3472749.3474790
3. A. Achberger, F. Heyen, K. Vidackovic, and M. Sedlmair, “PropellerHand: A Hand-Mounted, Propeller-Based Force Feedback Device,” in International Symposium on Visual Information Communication and Interaction (VINCI), in International Symposium on Visual Information Communication and Interaction (VINCI). ACM, 2021, pp. 4:1--4:8. doi: 10.1145/3481549.3481563.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{achberger2021vinci, abstract = {Immersive analytics is a fast growing field that is often applied in virtual reality (VR). VR environments often lack immersion due to missing sensory feedback when interacting with data. Existing haptic devices are often expensive, stationary, or occupy the user’s hand, preventing them from grasping objects or using a controller. We propose PropellerHand, an ungrounded hand-mounted haptic device with two rotatable propellers, that allows exerting forces on the hand without obstructing hand use. PropellerHand is able to simulate feedback such as weight and torque by generating thrust up to 11 N in 2-DOF and a torque of 1.87 Nm in 2-DOF. Its design builds on our experience from quantitative and qualitative experiments with different form factors and parts. We evaluated our final version through a qualitative user study in various VR scenarios that required participants to manipulate virtual objects in different ways, while changing between torques and directional forces. Results show that PropellerHand improves users’ immersion in virtual reality.}, author = {Achberger, Alexander and Heyen, Frank and Vidackovic, Kresimir and Sedlmair, Michael}, booktitle = {International Symposium on Visual Information Communication and Interaction (VINCI)}, doi = {10.1145/3481549.3481563}, pages = {4:1--4:8}, publisher = {ACM}, title = {PropellerHand: {A} Hand-Mounted, Propeller-Based Force Feedback Device}, url = {https://doi.org/10.1145/3481549.3481563}, year = 2021 }
  Link
  https://doi.org/10.1145/3481549.3481563
4. J. Bernard, M. Hutter, M. Sedlmair, M. Zeppelzauer, and T. Munzner, “A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling,” ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 11, no. 3–4, Art. no. 3–4, 2021, doi: 10.1145/3439333.
  - BibTeX
  - Link
  BibTeX
  @article{bernard2021tiis, abstract = {Strategies for selecting the next data instance to label, in service of generating labeled data for machine learning, have been considered separately in the machine learning literature on active learning and in the visual analytics literature on human-centered approaches. We propose a unified design space for instance selection strategies to support detailed and fine-grained analysis covering both of these perspectives. We identify a concise set of 15 properties, namely measureable characteristics of datasets or of machine learning models applied to them, that cover most of the strategies in these literatures. To quantify these properties, we introduce Property Measures (PM) as fine-grained building blocks that can be used to formalize instance selection strategies. In addition, we present a taxonomy of PMs to support the description, evaluation, and generation of PMs across four dimensions: machine learning (ML) Model Output, Instance Relations, Measure Functionality, and Measure Valence. We also create computational infrastructure to support qualitative visual data analysis: a visual analytics explainer for PMs built around an implementation of PMs using cascades of eight atomic functions. It supports eight analysis tasks, covering the analysis of datasets and ML models using visual comparison within and between PMs and groups of PMs, and over time during the interactive labeling process. We iteratively refined the PM taxonomy, the explainer, and the task abstraction in parallel with each other during a two-year formative process, and show evidence of their utility through a summative evaluation with the same infrastructure. This research builds a formal baseline for the better understanding of the commonalities and differences of instance selection strategies, which can serve as the stepping stone for the synthesis of novel strategies in future work.}, author = {Bernard, J{\"u}rgen and Hutter, Marco and Sedlmair, Michael and Zeppelzauer, Matthias and Munzner, Tamara}, doi = {10.1145/3439333}, journal = {ACM Transactions on Interactive Intelligent Systems (TiiS)}, number = {3-4}, pages = {1--42}, title = {A Taxonomy of Property Measures to Unify Active Learning and Human-centered Approaches to Data Labeling}, url = {https://doi.org/10.1145/3439333}, volume = 11, year = 2021 }
  Link
  https://doi.org/10.1145/3439333
5. J. Bernard, M. Hutter, M. Zeppelzauer, M. Sedlmair, and T. Munzner, “ProSeCo: Visual analysis of class separation measures and dataset characteristics,” Computers & Graphics, vol. 96, pp. 48–60, 2021, doi: https://doi.org/10.1016/j.cag.2021.03.004.
  - BibTeX
  - Link
  BibTeX
  @article{BERNARD202148, abstract = {Class separation is an important concept in machine learning and visual analytics. We address the visual analysis of class separation measures for both high-dimensional data and its corresponding projections into 2D through dimensionality reduction (DR) methods. Although a plethora of separation measures have been proposed, it is difficult to compare class separation between multiple datasets with different characteristics, multiple separation measures, and multiple DR methods. We present ProSeCo, an interactive visualization approach to support comparison between up to 20 class separation measures and up to 4 DR methods, with respect to any of 7 dataset characteristics: dataset size, dataset dimensions, class counts, class size variability, class size skewness, outlieriness, and real-world vs. synthetically generated data. ProSeCo supports (1) comparing across measures, (2) comparing high-dimensional to dimensionally-reduced 2D data across measures, (3) comparing between different DR methods across measures, (4) partitioning with respect to a dataset characteristic, (5) comparing partitions for a selected characteristic across measures, and (6) inspecting individual datasets in detail. We demonstrate the utility of ProSeCo in two usage scenarios, using datasets [1] posted at https://osf.io/epcf9/.}, author = {Bernard, Jürgen and Hutter, Marco and Zeppelzauer, Matthias and Sedlmair, Michael and Munzner, Tamara}, doi = {https://doi.org/10.1016/j.cag.2021.03.004}, issn = {0097-8493}, journal = {Computers & Graphics}, pages = {48-60}, title = {ProSeCo: Visual analysis of class separation measures and dataset characteristics}, url = {https://www.sciencedirect.com/science/article/pii/S0097849321000406}, volume = 96, year = 2021 }
  Link
  https://www.sciencedirect.com/science/article/pii/S0097849321000406
6. C. Bu et al., “SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects,” IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, Art. no. 2, 2021, doi: 10.1109/TVCG.2020.3030404.
  - BibTeX
  - Link
  BibTeX
  @article{9222035, abstract = {In this paper, we propose SineStream, a new variant of streamgraphs that improves their readability by minimizing sine illusion effects. Such effects reflect the tendency of humans to take the orthogonal rather than the vertical distance between two curves as their distance. In SineStream, we connect the readability of streamgraphs with minimizing sine illusions and by doing so provide a perceptual foundation for their design. As the geometry of a streamgraph is controlled by its baseline (the bottom-most curve) and the ordering of the layers, we re-interpret baseline computation and layer ordering algorithms in terms of reducing sine illusion effects. For baseline computation, we improve previous methods by introducing a Gaussian weight to penalize layers with large thickness changes. For layer ordering, three design requirements are proposed and implemented through a hierarchical clustering algorithm. Quantitative experiments and user studies demonstrate that SineStream improves the readability and aesthetics of streamgraphs compared to state-of-the-art methods.}, author = {Bu, C. and Zhang, Q. and Wang, Q. and Zhang, J. and Sedlmair, M. and Deussen, O. and Wang, Y.}, doi = {10.1109/TVCG.2020.3030404}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 2, pages = {1634-1643}, title = {SineStream: Improving the Readability of Streamgraphs by Minimizing Sine Illusion Effects}, url = {https://ieeexplore.ieee.org/document/9222035}, volume = 27, year = 2021 }
  Link
  https://ieeexplore.ieee.org/document/9222035
7. J. Chen et al., “VIS30K: A collection of figures and tables from IEEE visualization conference publications,” IEEE Transactions on Visualization and Computer Graphics (TVCG), vol. 27, no. 9, Art. no. 9, 2021, doi: 10.1109/TVCG.2021.3054916.
  - BibTeX
  - Link
  BibTeX
  @article{chen2021tvcg, abstract = {We present the VIS30K dataset, a collection of 29,689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST). VIS30K's comprehensive coverage of the scientific literature in visualization not only reflects the progress of the field but also enables researchers to study the evolution of the state-of-the-art and to find relevant work based on graphical content. We describe the dataset and our semi-automatic collection process, which couples convolutional neural networks (CNN) with curation. Extracting figures and tables semi-automatically allows us to verify that no images are overlooked or extracted erroneously. To improve quality further, we engaged in a peer-search process for high-quality figures from early IEEE Visualization papers. With the resulting data, we also contribute VISImageNavigator (VIN, visimagenavigator.github.io ), a web-based tool that facilitates searching and exploring VIS30K by author names, paper keywords, title and abstract, and years.}, author = {Chen, Jian and Ling, Meng and Li, Rui and Isenberg, Petra and Isenberg, Tobias and Sedlmair, Michael and M{\"o}ller, Torsten and Laramee, Robert S and Shen, Han-Wei and W{\"u}nsche, Katharina and Wang, Qiru}, doi = {10.1109/TVCG.2021.3054916}, journal = {IEEE Transactions on Visualization and Computer Graphics (TVCG)}, number = 9, pages = {3826--3833}, title = {{VIS30K}: A collection of figures and tables from {IEEE} visualization conference publications}, url = {https://doi.org/10.1109/TVCG.2021.3054916}, volume = 27, year = 2021 }
  Link
  https://doi.org/10.1109/TVCG.2021.3054916
8. R. Cutura, K. Angerbauer, F. Heyen, N. Hube, and M. Sedlmair, “DaRt: Generative Art using Dimensionality Reduction Algorithms,” in IEEE VIS Arts Program (VISAP), in IEEE VIS Arts Program (VISAP). IEEE, 2021, pp. 59--72. doi: 10.1109/VISAP52981.2021.00013.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{cutura2021generative, abstract = {Dimensionality Reduction (DR) is a popular technique that is often used in Machine Learning and Visualization communities to analyze high-dimensional data. The approach is empirically proven to be powerful for uncovering previously unseen structures in the data. While observing the results of the intermediate optimization steps of DR algorithms, we coincidently discovered the artistic beauty of the DR process. With enthusiasm for the beauty, we decided to look at DR from a generative art lens rather than their technical application aspects and use DR techniques to create artwork. Particularly, we use the optimization process to generate images, by drawing each intermediate step of the optimization process with some opacity over the previous intermediate result. As another alternative input, we used a neural-network model for face-landmark detection, to apply DR to portraits, while maintaining some facial properties, resulting in abstracted facial avatars. In this work, we provide such a collection of such artwork.}, author = {Cutura, Rene and Angerbauer, Katrin and Heyen, Frank and Hube, Natalie and Sedlmair, Michael}, booktitle = {IEEE VIS Arts Program (VISAP)}, doi = {10.1109/VISAP52981.2021.00013}, organization = {IEEE}, pages = {59--72}, title = {DaRt: Generative Art using Dimensionality Reduction Algorithms}, url = {https://ieeexplore.ieee.org/document/9622987}, year = 2021 }
  Link
  https://ieeexplore.ieee.org/document/9622987
9. R. Cutura, C. Morariu, Z. Cheng, Y. Wang, D. Weiskopf, and M. Sedlmair, “Hagrid — Gridify Scatterplots with Hilbert and Gosper Curves,” in The 14th International Symposium on Visual Information Communication and Interaction, in The 14th International Symposium on Visual Information Communication and Interaction. Potsdam, Germany: Association for Computing Machinery, 2021, p. 1:1—1:8. doi: 10.1145/3481549.3481569.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3481549.3481569, abstract = { A common enhancement of scatterplots represents points as small multiples, glyphs, or thumbnail images. As this encoding often results in overlaps, a general strategy is to alter the position of the data points, for instance, to a grid-like structure. Previous approaches rely on solving expensive optimization problems or on dividing the space that alter the global structure of the scatterplot. To find a good balance between efficiency and neighborhood and layout preservation, we propose Hagrid, a technique that uses space-filling curves (SFCs) to “gridify” a scatterplot without employing expensive collision detection and handling mechanisms. Using SFCs ensures that the points are plotted close to their original position, retaining approximately the same global structure. The resulting scatterplot is mapped onto a rectangular or hexagonal grid, using Hilbert and Gosper curves. We discuss and evaluate the theoretic runtime of our approach and quantitatively compare our approach to three state-of-the-art gridifying approaches, DGrid, Small multiples with gaps SMWG, and CorrelatedMultiples CMDS, in an evaluation comprising 339 scatterplots. Here, we compute several quality measures for neighborhood preservation together with an analysis of the actual runtimes. The main results show that, compared to the best other technique, Hagrid is faster by a factor of four, while achieving similar or even better quality of the gridified layout. Due to its computational efficiency, our approach also allows novel applications of gridifying approaches in interactive settings, such as removing local overlap upon hovering over a scatterplot.}, address = {New York, NY, USA}, articleno = {1}, author = {Cutura, Rene and Morariu, Cristina and Cheng, Zhanglin and Wang, Yunhai and Weiskopf, Daniel and Sedlmair, Michael}, booktitle = {The 14th International Symposium on Visual Information Communication and Interaction}, doi = {10.1145/3481549.3481569}, isbn = {9781450386470}, location = {Potsdam, Germany}, numpages = {8}, pages = {1:1—1:8}, publisher = {Association for Computing Machinery}, series = {VINCI 2021}, title = {Hagrid — Gridify Scatterplots with Hilbert and Gosper Curves}, url = {https://doi.org/10.1145/3481549.3481569}, year = 2021 }
  Link
  https://doi.org/10.1145/3481549.3481569
10. J. Eirich et al., “IRVINE: A Design Study on Analyzing Correlation Patterns of Electrical Engines,” IEEE Trans. Visualization and Computer Graphics (TVCG, Proc. VIS 2021), 2021, doi: 10.1109/TVCG.2021.3114797.
  - BibTeX
  - Link
  BibTeX
  @article{eirich2021vis, abstract = {In this design study, we present IRVINE, a Visual Analytics (VA) system, which facilitates the analysis of acoustic data to detect and understand previously unknown errors in the manufacturing of electrical engines. In serial manufacturing processes, signatures from acoustic data provide valuable information on how the relationship between multiple produced engines serves to detect and understand previously unknown errors. To analyze such signatures, IRVINE leverages interactive clustering and data labeling techniques, allowing users to analyze clusters of engines with similar signatures, drill down to groups of engines, and select an engine of interest. Furthermore, IRVINE allows to assign labels to engines and clusters and annotate the cause of an error in the acoustic raw measurement of an engine. Since labels and annotations represent valuable knowledge, they are conserved in a knowledge database to be available for other stakeholders. We contribute a design study, where we developed IRVINE in four main iterations with engineers from a company in the automotive sector. To validate IRVINE, we conducted a field study with six domain experts. Our results suggest a high usability and usefulness of IRVINE as part of the improvement of a real-world manufacturing process. Specifically, with IRVINE domain experts were able to label and annotate produced electrical engines more than 30% faster.}, author = {Eirich, Joscha and Bonart, Jakob and J{\"a}ckle, Dominik and Sedlmair, Michael and Schmid, Ute and Fischbach, Kai and Schreck, Tobias and Bernard, J{\"u}rgen}, doi = {10.1109/TVCG.2021.3114797}, journal = {IEEE Trans. Visualization and Computer Graphics (TVCG, Proc. VIS 2021)}, note = {To appear. Best paper award}, title = {{IRVINE}: A Design Study on Analyzing Correlation Patterns of Electrical Engines}, url = {https://doi.org/10.1109/TVCG.2021.3114797}, year = 2021 }
  Link
  https://doi.org/10.1109/TVCG.2021.3114797
11. N. Grossmann, J. Bernard, M. Sedlmair, and M. Waldner, “Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation,” in IEEE Visualization Conference (VIS, Short Paper), in IEEE Visualization Conference (VIS, Short Paper). 2021, pp. 61--65. doi: 10.1109/VIS49827.2021.9623326.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{grossmann2021vis, abstract = {In visual interactive labeling, users iteratively assign labels to data items until the machine model reaches an acceptable accuracy. A crucial step of this process is to inspect the model's accuracy and decide whether it is necessary to label additional elements. In scenarios with no or very little labeled data, visual inspection of the predictions is required. Similarity-preserving scatterplots created through a dimensionality reduction algorithm are a common visualization that is used in these cases. Previous studies investigated the effects of layout and image complexity on tasks like labeling. However, model evaluation has not been studied systematically. We present the results of an experiment studying the influence of image complexity and visual grouping of images on model accuracy estimation. We found that users outperform traditional automated approaches when estimating a model's accuracy. Furthermore, while the complexity of images impacts the overall performance, the layout of the items in the plot has little to no effect on estimations.}, author = {Grossmann, Nicolas and Bernard, Jürgen and Sedlmair, Michael and Waldner, Manuela}, booktitle = {IEEE Visualization Conference (VIS, Short Paper)}, doi = {10.1109/VIS49827.2021.9623326}, pages = {61--65}, title = {Does the Layout Really Matter? A Study on Visual Model Accuracy Estimation}, url = {https://arxiv.org/abs/2110.07188}, year = 2021 }
  Link
  https://arxiv.org/abs/2110.07188
12. N. Hube, K. Angerbauer, D. Pohlandt, K. Vidačković, and M. Sedlmair, “VR Collaboration in Large Companies: An Interview Study on the Role of Avatars,” in IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), in IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). IEEE, 2021, pp. 139--144. doi: 10.1109/ISMAR-Adjunct54149.2021.00037.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{hube2021collaboration, abstract = {Collaboration is essential in companies and often physical presence is required, thus, more and more Virtual Reality (VR) systems are used to work together remotely. To support social interaction, human representations in form of avatars are used in collaborative virtual environment (CVE) tools. However, up to now, the avatar representations often are limited in their design and functionality, which may hinder effective collaboration. In our interview study, we explored the status quo of VR collaboration in a large automotive company setting with a special focus on the role of avatars. We collected inter-view data from 21 participants, from which we identified challenges of current avatar representations used in our setting. Based on these findings, we discuss design suggestions for avatars in a company setting, which aim to improve social interaction. As opposed to state-of-the-art research, we found that users within the context of a large automotive company have an altered need with respect to avatar representations.}, author = {Hube, Natalie and Angerbauer, Katrin and Pohlandt, Daniel and Vidačković, Krešimir and Sedlmair, Michael}, booktitle = {IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, doi = {10.1109/ISMAR-Adjunct54149.2021.00037}, organization = {IEEE}, pages = {139--144}, title = {VR Collaboration in Large Companies: An Interview Study on the Role of Avatars}, url = {https://doi.org/10.1109/ISMAR-Adjunct54149.2021.00037}, year = 2021 }
  Link
  https://doi.org/10.1109/ISMAR-Adjunct54149.2021.00037
13. M. Kraus, K. Klein, J. Fuchs, D. A. Keim, F. Schreiber, and M. Sedlmair, “The Value of Immersive Visualization,” IEEE Computer Graphics and Applications (CG&A), vol. 41, no. 4, Art. no. 4, 2021, doi: 10.1109/MCG.2021.3075258.
  - BibTeX
  - Link
  BibTeX
  @article{kraus2021cga, abstract = {In recent years, research on immersive environments has experienced a new wave of interest, and immersive analytics has been established as a new research field. Every year, a vast amount of different techniques, applications, and user studies are published that focus on employing immersive environments for visualizing and analyzing data. Nevertheless, immersive analytics is still a relatively unexplored field that needs more basic research in many aspects and is still viewed with skepticism. Rightly so, because in our opinion, many researchers do not fully exploit the possibilities offered by immersive environments and, on the contrary, sometimes even overestimate the power of immersive visualizations. Although a growing body of papers has demonstrated individual advantages of immersive analytics for specific tasks and problems, the general benefit of using immersive environments for effective analytic tasks remains controversial. In this article, we reflect on when and how immersion may be appropriate for the analysis and present four guiding scenarios. We report on our experiences, discuss the landscape of assessment strategies, and point out the directions where we believe immersive visualizations have the greatest potential.}, author = {Kraus, Matthias and Klein, Karsten and Fuchs, Johannes and Keim, Daniel A and Schreiber, Falk and Sedlmair, Michael}, doi = {10.1109/MCG.2021.3075258}, journal = {IEEE Computer Graphics and Applications (CG\&A)}, number = 4, pages = {125-132}, title = {The Value of Immersive Visualization}, url = {https://doi.org/10.1109/MCG.2021.3075258}, volume = 41, year = 2021 }
  Link
  https://doi.org/10.1109/MCG.2021.3075258
14. C. Krauter, J. Vogelsang, A. S. Calepso, K. Angerbauer, and M. Sedlmair, “Don’t Catch It: An Interactive Virtual-Reality Environment to Learn About COVID-19 Measures Using Gamification Elements,” in Mensch und Computer, in Mensch und Computer. ACM, 2021, pp. 593--596. doi: 10.1145/3473856.3474031.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{krauter2021muc, abstract = {The world is still under the influence of the COVID-19 pandemic. Even though vaccines are deployed as rapidly as possible, it is still necessary to use other measures to reduce the spread of the virus. Measures such as social distancing or wearing a mask receive a lot of criticism. Therefore, we want to demonstrate a serious game to help the players understand these measures better and show them why they are still necessary. The player of the game has to avoid other agents to keep their risk of a COVID-19 infection low. The game uses Virtual Reality through a Head-Mounted-Display to deliver an immersive and enjoyable experience. Gamification elements are used to engage the user with the game while they explore various environments. We also implemented visualizations that help the user with social distancing.}, author = {Krauter, Christian and Vogelsang, Jonas and Calepso, Aimee Sousa and Angerbauer, Katrin and Sedlmair, Michael}, booktitle = {Mensch und Computer}, doi = {10.1145/3473856.3474031}, pages = {593--596}, publisher = {ACM}, title = {Don’t Catch It: An Interactive Virtual-Reality Environment to Learn About {COVID-19} Measures Using Gamification Elements}, url = {https://doi.org/10.1145/3473856.3474031}, year = 2021 }
  Link
  https://doi.org/10.1145/3473856.3474031
15. M. Ling et al., “Document Domain Randomization for Deep Learning Document Layout Extraction,” in Document Analysis and Recognition (ICDAR), in Document Analysis and Recognition (ICDAR). Springer International Publishing, 2021, pp. 497--513. doi: 10.1007/978-3-030-86549-8_32.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{ling2021icdar, abstract = {We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation. DDR renders pseudo-document pages by modeling randomized textual and non-textual contents of interest, with user-defined layout and font styles to support joint learning of fine-grained classes. We demonstrate competitive results using our DDR approach to extract nine document classes from the benchmark CS-150 and papers published in two domains, namely annual meetings of Association for Computational Linguistics (ACL) and IEEE Visualization (VIS). We compare DDR to conditions of style mismatch, fewer or more noisy samples that are more easily obtained in the real world. We show that high-fidelity semantic information is not necessary to label semantic classes but style mismatch between train and test can lower model accuracy. Using smaller training samples had a slightly detrimental effect. Finally, network models still achieved high test accuracy when correct labels are diluted towards confusing labels; this behavior hold across several classes.}, author = {Ling, Meng and Chen, Jian and M{\"o}ller, Torsten and Isenberg, Petra and Isenberg, Tobias and Sedlmair, Michael and Laramee, Robert S and Shen, Han-Wei and Wu, Jian and Giles, C Lee}, booktitle = {Document Analysis and Recognition (ICDAR)}, doi = {10.1007/978-3-030-86549-8_32}, isbn = {978-3-030-86549-8}, pages = {497--513}, publisher = {Springer International Publishing}, title = {Document Domain Randomization for Deep Learning Document Layout Extraction}, url = {https://arxiv.org/abs/2105.14931}, year = 2021 }
  Link
  https://arxiv.org/abs/2105.14931
16. K. Lu et al., “Palettailor: Discriminable Colorization for Categorical Data,” IEEE Transactions on Visualization & Computer Graphics, vol. 27, no. 02, Art. no. 02, Feb. 2021, doi: 10.1109/TVCG.2020.3030406.
  - BibTeX
  - Link
  BibTeX
  @article{9222351, abstract = {We present an integrated approach for creating and assigning color palettes to different visualizations such as multi-class scatterplots, line, and bar charts. While other methods separate the creation of colors from their assignment, our approach takes data characteristics into account to produce color palettes, which are then assigned in a way that fosters better visual discrimination of classes. To do so, we use a customized optimization based on simulated annealing to maximize the combination of three carefully designed color scoring functions: point distinctness, name difference, and color discrimination. We compare our approach to state-of-the-art palettes with a controlled user study for scatterplots and line charts, furthermore we performed a case study. Our results show that Palettailor, as a fully-automated approach, generates color palettes with a higher discrimination quality than existing approaches. The efficiency of our optimization allows us also to incorporate user modifications into the color selection process.}, address = {Los Alamitos, CA, USA}, author = {Lu, K. and Feng, M. and Chen, X. and Sedlmair, M. and Deussen, O. and Lischinski, D. and Cheng, Z. and Wang, Y.}, doi = {10.1109/TVCG.2020.3030406}, issn = {1941-0506}, journal = {IEEE Transactions on Visualization & Computer Graphics}, month = {02}, number = 02, pages = {475-484}, publisher = {IEEE Computer Society}, title = {Palettailor: Discriminable Colorization for Categorical Data}, url = {https://ieeexplore.ieee.org/document/9222351}, volume = 27, year = 2021 }
  Link
  https://ieeexplore.ieee.org/document/9222351
17. C. Morariu, A. Bibal, R. Cutura, B. Frenay, and M. Sedlmair, “DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality,” arXiv preprint, Technical Report arXiv:2105.09275, 2021. [Online]. Available: https://arxiv.org/abs/2105.09275
  - BibTeX
  - Link
  BibTeX
  @techreport{morariu2021arxiv, abstract = {A plethora of dimensionality reduction techniques have emerged over the past decades, leaving researchers and analysts with a wide variety of choices for reducing their data, all the more so given some techniques come with additional parametrization (e.g. t-SNE, UMAP, etc.). Recent studies are showing that people often use dimensionality reduction as a black-box regardless of the specific properties the method itself preserves. Hence, evaluating and comparing 2D projections is usually qualitatively decided, by setting projections side-by-side and letting human judgment decide which projection is the best. In this work, we propose a quantitative way of evaluating projections, that nonetheless places human perception at the center. We run a comparative study, where we ask people to select 'good' and 'misleading' views between scatterplots of low-level projections of image datasets, simulating the way people usually select projections. We use the study data as labels for a set of quality metrics whose purpose is to discover and quantify what exactly people are looking for when deciding between projections. With this proxy for human judgments, we use it to rank projections on new datasets, explain why they are relevant, and quantify the degree of subjectivity in projections selected.}, author = {Morariu, Cristina and Bibal, Adrien and Cutura, Rene and Frenay, Benoit and Sedlmair, Michael}, institution = {{arXiv} preprint}, number = {arXiv:2105.09275}, title = {DumbleDR: Predicting User Preferences of Dimensionality Reduction Projection Quality}, type = {Technical Report}, url = {https://arxiv.org/abs/2105.09275}, year = 2021 }
  Link
  https://arxiv.org/abs/2105.09275
18. S. Rau, F. Heyen, and M. Sedlmair, “Visual Support for Human-AI Co-Composition,” in Extended Abstracts for the Late-Breaking Demo Session of the 22nd Int. Society for Music Information Retrieval Conf. (ISMIR), in Extended Abstracts for the Late-Breaking Demo Session of the 22nd Int. Society for Music Information Retrieval Conf. (ISMIR). ISMIR, Nov. 2021. [Online]. Available: https://archives.ismir.net/ismir2021/latebreaking/000014.pdf
  - BibTeX
  - Link
  BibTeX
  @inproceedings{simeon_rau_2021, abstract = {We propose a visual approach for AI-assisted music composition, where the user interactively generates, selects, and adapts short melodies. Based on an entered start melody, we automatically generate multiple continuation samples. Repeating this step and in turn generating continuations for these samples results in a tree or graph of melodies. We visualize this structure with two visualizations, where nodes display the piano roll of the corresponding sample. By interacting with these visualizations, the user can quickly listen to, choose, and adapt melodies, to iteratively create a composition. A third visualization provides an overview over larger numbers of samples, allowing for insights into the AI's predictions and the sample space.}, author = {Rau, Simeon and Heyen, Frank and Sedlmair, Michael}, booktitle = {Extended Abstracts for the Late-Breaking Demo Session of the 22nd Int. Society for Music Information Retrieval Conf. (ISMIR)}, month = {11}, publisher = {ISMIR}, tags = {myown vis(us) visus:heyenfk visus:sedlmaml cybervalley peerreviewed visus visus:rausn sent:unibiblio for:visus}, title = {Visual Support for Human-AI Co-Composition}, url = {https://archives.ismir.net/ismir2021/latebreaking/000014.pdf}, year = 2021 }
  Link
  https://archives.ismir.net/ismir2021/latebreaking/000014.pdf
19. G. J. Rijken et al., “Illegible Semantics: Exploring the Design Space of Metal Logos,” in IEEE VIS alt.VIS Workshop, in IEEE VIS alt.VIS Workshop. 2021. [Online]. Available: https://arxiv.org/abs/2109.01688
  - BibTeX
  - Link
  BibTeX
  @inproceedings{rijken2021altvis, abstract = {The logos of metal bands can be by turns gaudy, uncouth, or nearly illegible. Yet, these logos work: they communicate sophisticated notions of genre and emotional affect. In this paper we use the design considerations of metal logos to explore the space of "illegible semantics": the ways that text can communicate information at the cost of readability, which is not always the most important objective. In this work, drawing on formative visualization theory, professional design expertise, and empirical assessments of a corpus of metal band logos, we describe a design space of metal logos and present a tool through which logo characteristics can be explored through visualization. We investigate ways in which logo designers imbue their text with meaning and consider opportunities and implications for visualization more widely.}, author = {Rijken, Gerrit J and Cutura, Rene and Heyen, Frank and Sedlmair, Michael and Correll, Michael and Dykes, Jason and Smit, Noeska}, booktitle = {IEEE VIS alt.VIS Workshop}, title = {Illegible Semantics: Exploring the Design Space of Metal Logos}, url = {https://arxiv.org/abs/2109.01688}, year = 2021 }
  Link
  https://arxiv.org/abs/2109.01688
20. K. Schatz et al., “2019 IEEE Scientific Visualization Contest Winner: Visual Analysis of Structure Formation in Cosmic Evolution,” IEEE Computer Graphics and Applications (CG&A), vol. 41, no. 06, Art. no. 06, Nov. 2021, doi: 10.1109/MCG.2020.3004613.
  - BibTeX
  - Link
  BibTeX
  @article{9126163, abstract = {Simulations of cosmic evolution are a means to explain the formation of the universe as we see it today. The resulting data of such simulations comprise numerous physical quantities, which turns their analysis into a complex task. Here, we analyze such high-dimensional and time-varying particle data using various visualization techniques from the fields of particle visualization, flow visualization, volume visualization, and information visualization. Our approach employs specialized filters to extract and highlight the development of so-called active galactic nuclei and filament structures formed by the particles. Additionally, we calculate X-ray emission of the evolving structures in a pre-processing step to complement visual analysis. Our approach is integrated into a single visual analytics framework to allow for analysis of star formation at interactive frame rates. Finally, we lay out the methodological aspects of our work that led to success at the 2019 IEEE SciVis Contest.}, author = {Schatz, K. and Müller, C. and Gralka, P. and Heinemann, M. and Straub, A. and Schulz, C. and Braun, M. and Rau, T. and Becher, M. and Frey, S. and Reina, G. and Sedlmair, M. and Weiskopf, D. and Ertl, T. and Diehl, P. and Marcello, D. and Frank, J. and Müller, T.}, doi = {10.1109/MCG.2020.3004613}, issn = {1558-1756}, journal = {IEEE Computer Graphics and Applications (CG&A)}, month = {11}, number = 06, pages = {101-110}, title = {2019 IEEE Scientific Visualization Contest Winner: Visual Analysis of Structure Formation in Cosmic Evolution}, url = {https://ieeexplore.ieee.org/document/9126163}, volume = 41, year = 2021 }
  Link
  https://ieeexplore.ieee.org/document/9126163
21. M. Waldner, T. Geymayer, D. Schmalstieg, and M. Sedlmair, “Linking unstructured evidence to structured observations,” Information Visualization, vol. 20, no. 1, Art. no. 1, Jan. 2021, doi: 10.1177/1473871620986249.
  - BibTeX
  - Link
  BibTeX
  @article{waldner-2021-leo, abstract = {Many professionals, like journalists, writers, or consultants, need to acquire information from various sources, make sense of this unstructured evidence, structure their observations, and finally create and deliver their product, such as a report or a presentation. In formative interviews, we found that tools allowing structuring of observations are often disconnected from the corresponding evidence. Therefore, we designed a sensemaking environment with a flexible observation graph that visually ties together evidence in unstructured documents with the user’s structured knowledge. This is achieved through bi-directional deep links between highlighted document portions and nodes in the observation graph. In a controlled study, we compared users’ sensemaking strategies using either the observation graph or a simple text editor on a large display. Results show that the observation graph represents a holistic, compact representation of users’ observations, which can be linked to unstructured evidence on demand. In contrast, users taking textual notes required much more display space to spatially organize source documents containing unstructured evidence. This implies that spatial organization is a powerful strategy to structure observations even if the available space is limited.}, author = {Waldner, Manuela and Geymayer, Thomas and Schmalstieg, Dieter and Sedlmair, Michael}, doi = {10.1177/1473871620986249}, journal = {Information Visualization}, month = {01}, number = 1, pages = {47--65}, title = {Linking unstructured evidence to structured observations}, url = {https://doi.org/10.1177/1473871620986249}, volume = 20, year = 2021 }
  Link
  https://doi.org/10.1177/1473871620986249
2020
1. A. Achberger, R. Cutura, O. Türksoy, and M. Sedlmair, “Caarvida: Visual Analytics for Test Drive Videos,” in Proceedings of the International Conference on Advanced Visual Interfaces, in Proceedings of the International Conference on Advanced Visual Interfaces. 2020, pp. 1--9. doi: 10.1145/3399715.3399862.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{achberger2020caarvida, abstract = {We report on an interdisciplinary visual analytics project wherein automotive engineers analyze test drive videos. These videos are annotated with navigation-specific augmented reality (AR) con�tent, and the engineers need to identify issues and evaluate the behavior of the underlying AR navigation system. With the increas�ing amount of video data, traditional analysis approaches can no longer be conducted in an acceptable timeframe. To address this issue, we collaboratively developed Caarvida, a visual analytics tool that helps engineers to accomplish their tasks faster and handle an increased number of videos. Caarvida combines automatic video analysis with interactive and visual user interfaces. We conducted two case studies which show that Caarvida successfully supports domain experts and speeds up their task completion time.}, author = {Achberger, Alexander and Cutura, René and Türksoy, Oguzhan and Sedlmair, Michael}, booktitle = {Proceedings of the International Conference on Advanced Visual Interfaces}, doi = {10.1145/3399715.3399862}, pages = {1--9}, title = {Caarvida: Visual Analytics for Test Drive Videos}, url = {https://doi.org/10.1145/3399715.3399862}, year = 2020 }
  Link
  https://doi.org/10.1145/3399715.3399862
2. P. Balestrucci et al., “Pipelines Bent, Pipelines Broken: Interdisciplinary Self-Reflection on the Impact of COVID-19 on Current and Future Research (Position Paper),” in 2020 IEEE Workshop on Evaluation and Beyond-Methodological Approaches to Visualization (BELIV), in 2020 IEEE Workshop on Evaluation and Beyond-Methodological Approaches to Visualization (BELIV). IEEE, 2020, pp. 11--18. doi: 10.1109/BELIV51497.2020.00009.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{balestrucci2020pipelines, abstract = {Among the many changes brought about by the COVID-19 pandemic, one of the most pressing for scientific research concerns user testing. For the researchers who conduct studies with human participants, the requirements for social distancing have created a need for reflecting on methodologies that previously seemed relatively straightforward. It has become clear from the emerging literature on the topic and from first-hand experiences of researchers that the restrictions due to the pandemic affect every aspect of the research pipeline. The current paper offers an initial reflection on user-based research, drawing on the authors' own experiences and on the results of a survey that was conducted among researchers in different disciplines, primarily psychology, human-computer interaction (HCI), and visualization communities. While this sampling of researchers is by no means comprehensive, the multi-disciplinary approach and the consideration of different aspects of the research pipeline allow us to examine current and future challenges for user-based research. Through an exploration of these issues, this paper also invites others in the VIS-as well as in the wider-research community, to reflect on and discuss the ways in which the current crisis might also present new and previously unexplored opportunities.}, author = {Balestrucci, Priscilla and Angerbauer, Katrin and Morariu, Cristina and Welsch, Robin and Chuang, Lewis L and Weiskopf, Daniel and Ernst, Marc O and Sedlmair, Michael}, booktitle = {2020 IEEE Workshop on Evaluation and Beyond-Methodological Approaches to Visualization (BELIV)}, doi = {10.1109/BELIV51497.2020.00009}, organization = {IEEE}, pages = {11--18}, title = {Pipelines Bent, Pipelines Broken: Interdisciplinary Self-Reflection on the Impact of COVID-19 on Current and Future Research (Position Paper)}, url = {https://ieeexplore.ieee.org/abstract/document/9307759}, year = 2020 }
  Link
  https://ieeexplore.ieee.org/abstract/document/9307759
3. J. Bernard, M. Hutter, M. Zeppelzauer, M. Sedlmair, and T. Munzner, “SepEx: Visual Analysis of Class Separation Measures,” in Proceedings of the International Workshop on Visual Analytics (EuroVA), C. Turkay and K. Vrotsou, Eds., in Proceedings of the International Workshop on Visual Analytics (EuroVA). The Eurographics Association, 2020, pp. 1–5. doi: 10.2312/eurova.20201079.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{rova.20201079, abstract = {Class separation is an important concept in machine learning and visual analytics. However, the comparison of class separation for datasets with varying dimensionality is non-trivial, given a) the various possible structural characteristics of datasets and b) the plethora of separation measures that exist. Building upon recent findings in visualization research about the qualitative and quantitative evaluation of class separation for 2D dimensionally reduced data using scatterplots, this research addresses the visual analysis of class separation measures for high-dimensional data. We present SepEx, an interactive visualization approach for the assessment and comparison of class separation measures for multiple datasets. SepEx supports analysts with the comparison of multiple separation measures over many high-dimensional datasets, the effect of dimensionality reduction on measure outputs by supporting nD to 2D comparison, and the comparison of the effect of different dimensionality reduction methods on measure outputs. We demonstrate SepEx in a scenario on 100 two-class 5D datasets with a linearly increasing amount of separation between the classes, illustrating both similarities and nonlinearities across 11 measures.}, author = {Bernard, Jürgen and Hutter, Marco and Zeppelzauer, Matthias and Sedlmair, Michael and Munzner, Tamara}, booktitle = {Proceedings of the International Workshop on Visual Analytics (EuroVA)}, doi = {10.2312/eurova.20201079}, editor = {Turkay, Cagatay and Vrotsou, Katerina}, isbn = {978-3-03868-116-8}, issn = {2664-4487}, pages = {1-5}, publisher = {The Eurographics Association}, title = {SepEx: Visual Analysis of Class Separation Measures}, url = {https://doi.org/10.2312/eurova.20201079}, year = 2020 }
  Link
  https://doi.org/10.2312/eurova.20201079
4. N. Boukhelifa et al., “Challenges in Evaluating Interactive Visual Machine Learning Systems,” IEEE Computer Graphics and Applications, vol. 40, no. 6, Art. no. 6, 2020, doi: 10.1109/MCG.2020.3017064.
  - BibTeX
  - Link
  BibTeX
  @article{9238590, abstract = {In interactive visual machine learning (IVML), humans and machine learning algorithms collaborate to achieve tasks mediated by interactive visual interfaces. This human-in-the-loop approach to machine learning brings forth not only numerous intelligibility, trust, and usability issues, but also many open questions with respect to the evaluation of the IVML system, both as separate components, and as a holistic entity that includes both human and machine intelligence. This article describes the challenges and research gaps identified in an IEEE VIS workshop on the evaluation of IVML systems.}, author = {Boukhelifa, N. and Bezerianos, A. and Chang, R. and Collins, C. and Drucker, S. and Endert, A. and Hullman, J. and North, C. and Sedlmair, M.}, doi = {10.1109/MCG.2020.3017064}, journal = {IEEE Computer Graphics and Applications}, number = 6, pages = {88-96}, title = {Challenges in Evaluating Interactive Visual Machine Learning Systems}, url = {https://ieeexplore.ieee.org/document/9238590}, volume = 40, year = 2020 }
  Link
  https://ieeexplore.ieee.org/document/9238590
5. F. Heyen et al., “ClaVis: An Interactive Visual Comparison System for Classifiers,” in Proceedings of the International Conference on Advanced Visual Interfaces (AVI), in Proceedings of the International Conference on Advanced Visual Interfaces (AVI). ACM, 2020, pp. 9:1-9:9. doi: 10.1145/3399715.3399814.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3399715.3399814, abstract = {We propose ClaVis, a visual analytics system for comparative analysis of classification models. ClaVis allows users to visually compare the performance and behavior of tens to hundreds of classifiers trained with different hyperparameter configurations. Our approach is plugin-based and classifier-agnostic and allows users to add their own datasets and classifier implementations. It provides multiple visualizations, including a multivariate ranking, a similarity map, a scatterplot that reveals correlations between parameters and scores, and a training history chart. We demonstrate the effectivity of our approach in multiple case studies for training classification models in the domain of natural language processing.}, author = {Heyen, Frank and Munz, Tanja and Neumann, Michael and Ortega, Daniel and Vu, Ngoc Thang and Weiskopf, Daniel and Sedlmair, Michael}, booktitle = {Proceedings of the International Conference on Advanced Visual Interfaces (AVI)}, doi = {10.1145/3399715.3399814}, isbn = {9781450375351}, pages = {9:1-9:9}, publisher = {ACM}, series = {AVI '20}, title = {ClaVis: An Interactive Visual Comparison System for Classifiers}, url = {https://doi.org/10.1145/3399715.3399814}, year = 2020 }
  Link
  https://doi.org/10.1145/3399715.3399814
6. N. Hube, O. Lenz, L. Engeln, R. Groh, and M. Sedlmair, “Comparing Methods for Mapping Facial Expressions to Enhance Immersive Collaboration with Signs of Emotion,” in IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct), in IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct). IEEE, 2020, pp. 30–35. doi: 10.1109/ISMAR-Adjunct51615.2020.00023.
  - BibTeX
  BibTeX
  @inproceedings{hube2020comparing, abstract = {We present a user study comparing a pre-evaluated mapping approach with a state-of-the-art direct mapping method of facial expressions for emotion judgment in an immersive setting. At its heart, the pre-evaluated approach leverages semiotics, a theory used in linguistic. In doing so, we want to compare pre-evaluation with an approach that seeks to directly map real facial expressions onto their virtual counterparts. To evaluate both approaches, we conduct a controlled lab study with 22 participants. The results show that users are significantly more accurate in judging virtual facial expressions with pre-evaluated mapping. Additionally, participants were slightly more confident when deciding on a presented emotion. We could not find any differences regarding potential Uncanny Valley effects. However, the pre-evaluated mapping shows potential to be more convenient in a conversational scenario.}, author = {Hube, Natalie and Lenz, Oliver and Engeln, Lars and Groh, Rainer and Sedlmair, Michael}, booktitle = {IEEE International Symposium on Mixed and Augmented Reality Adjunct (ISMAR-Adjunct)}, doi = {10.1109/ISMAR-Adjunct51615.2020.00023}, organization = {IEEE}, pages = {30-35}, title = {Comparing Methods for Mapping Facial Expressions to Enhance Immersive Collaboration with Signs of Emotion}, year = 2020 }
7. M. Kraus et al., “A Comparative Study of Orientation Support Tools in Virtual Reality Environments with Virtual Teleportation,” in 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR), in 2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 2020, pp. 227–238. doi: 10.1109/ISMAR50242.2020.00046.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{9284697, abstract = {Movement-compensating interactions like teleportation are commonly deployed techniques in virtual reality environments. Although practical, they tend to cause disorientation while navigating. Previous studies show the effectiveness of orientation-supporting tools, such as trails, in reducing such disorientation and reveal different strengths and weaknesses of individual tools. However, to date, there is a lack of a systematic comparison of those tools when teleportation is used as a movement-compensating technique, in particular under consideration of different tasks. In this paper, we compare the effects of three orientation-supporting tools, namely minimap, trail, and heatmap. We conducted a quantitative user study with 48 participants to investigate the accuracy and efficiency when executing four exploration and search tasks. As dependent variables, task performance, completion time, space coverage, amount of revisiting, retracing time, and memorability were measured. Overall, our results indicate that orientation-supporting tools improve task completion times and revisiting behavior. The trail and heatmap tools were particularly useful for speed-focused tasks, minimal revisiting, and space coverage. The minimap increased memorability and especially supported retracing tasks. These results suggest that virtual reality systems should provide orientation aid tailored to the specific tasks of the users.}, author = {Kraus, M. and Schäfer, H. and Meschenmoser, P. and Schweitzer, D. and Keim, D. A. and Sedlmair, M. and Fuchs, J.}, booktitle = {2020 IEEE International Symposium on Mixed and Augmented Reality (ISMAR)}, doi = {10.1109/ISMAR50242.2020.00046}, pages = {227-238}, title = {A Comparative Study of Orientation Support Tools in Virtual Reality Environments with Virtual Teleportation}, url = {https://ieeexplore.ieee.org/abstract/document/9284697}, year = 2020 }
  Link
  https://ieeexplore.ieee.org/abstract/document/9284697
8. M. Kraus et al., “Assessing 2D and 3D Heatmaps for Comparative Analysis: An Empirical Study,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, in Proceedings of the CHI Conference on Human Factors in Computing Systems. 2020, pp. 546:1–546:14. doi: 10.1145/3313831.3376675.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3313831.3376675, abstract = {Heatmaps are a popular visualization technique that encode 2D density distributions using color or brightness. Experimental studies have shown though that both of these visual variables are inaccurate when reading and comparing numeric data values. A potential remedy might be to use 3D heatmaps by introducing height as a third dimension to encode the data. Encoding abstract data in 3D, however, poses many problems, too. To better understand this tradeoff, we conducted an empirical study (N=48) to evaluate the user performance of 2D and 3D heatmaps for comparative analysis tasks. We test our conditions on a conventional 2D screen, but also in a virtual reality environment to allow for real stereoscopic vision. Our main results show that 3D heatmaps are superior in terms of error rate when reading and comparing single data items. However, for overview tasks, the well-established 2D heatmap performs better.}, author = {Kraus, Matthias and Angerbauer, Katrin and Buchm\"{u}ller, Juri and Schweitzer, Daniel and Keim, Daniel A. and Sedlmair, Michael and Fuchs, Johannes}, booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3313831.3376675}, isbn = {9781450367080}, numpages = {14}, pages = {546:1–546:14}, title = {Assessing 2D and 3D Heatmaps for Comparative Analysis: An Empirical Study}, url = {https://dl.acm.org/doi/abs/10.1145/3313831.3376675}, year = 2020 }
  Link
  https://dl.acm.org/doi/abs/10.1145/3313831.3376675
9. K. Kurzhals, F. Göbel, K. Angerbauer, M. Sedlmair, and M. Raubal, “A View on the Viewer: Gaze-Adaptive Captions for Videos,” in Proceedings of the CHI Conference on Human Factors in Computing Systems, in Proceedings of the CHI Conference on Human Factors in Computing Systems. 2020, pp. 139:1–139:12. doi: 10.1145/3313831.3376266.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3313831.3376266, abstract = {Subtitles play a crucial role in cross-lingual distribution of multimedia content and help communicate information where auditory content is not feasible (loud environments, hearing impairments, unknown languages). Established methods utilize text at the bottom of the screen, which may distract from the video. Alternative techniques place captions closer to related content (e.g., faces) but are not applicable to arbitrary videos such as documentations. Hence, we propose to leverage live gaze as indirect input method to adapt captions to individual viewing behavior. We implemented two gaze-adaptive methods and compared them in a user study (n=54) to traditional captions and audio-only videos. The results show that viewers with less experience with captions prefer our gaze-adaptive methods as they assist them in reading. Furthermore, gaze distributions resulting from our methods are closer to natural viewing behavior compared to the traditional approach. Based on these results, we provide design implications for gaze-adaptive captions.}, author = {Kurzhals, Kuno and G\"{o}bel, Fabian and Angerbauer, Katrin and Sedlmair, Michael and Raubal, Martin}, booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems}, doi = {10.1145/3313831.3376266}, isbn = {9781450367080}, numpages = {12}, pages = {139:1–139:12}, title = {A View on the Viewer: Gaze-Adaptive Captions for Videos}, url = {https://dl.acm.org/doi/abs/10.1145/3313831.3376266}, year = 2020 }
  Link
  https://dl.acm.org/doi/abs/10.1145/3313831.3376266
10. L. Merino, M. Schwarzl, M. Kraus, M. Sedlmair, D. Schmalstieg, and D. Weiskopf, “Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009 -- 2019),” in IEEE International Symposium on Mixed and Augmented Reality (ISMAR), in IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 2020. doi: doi: 10.1109/ISMAR50242.2020.00069.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{merino2020ismar, abstract = {We present a systematic review of 45S papers that report on evaluations in mixed and augmented reality (MR/AR) published in ISMAR, CHI, IEEE VR, and UIST over a span of 11 years (2009-2019). Our goal is to provide guidance for future evaluations of MR/AR approaches. To this end, we characterize publications by paper type (e.g., technique, design study), research topic (e.g., tracking, rendering), evaluation scenario (e.g., algorithm performance, user performance), cognitive aspects (e.g., perception, emotion), and the context in which evaluations were conducted (e.g., lab vs. in-thewild). We found a strong coupling of types, topics, and scenarios. We observe two groups: (a) technology-centric performance evaluations of algorithms that focus on improving tracking, displays, reconstruction, rendering, and calibration, and (b) human-centric studies that analyze implications of applications and design, human factors on perception, usability, decision making, emotion, and attention. Amongst the 458 papers, we identified 248 user studies that involved 5,761 participants in total, of whom only 1,619 were identified as female. We identified 43 data collection methods used to analyze 10 cognitive aspects. We found nine objective methods, and eight methods that support qualitative analysis. A majority (216/248) of user studies are conducted in a laboratory setting. Often (138/248), such studies involve participants in a static way. However, we also found a fair number (30/248) of in-the-wild studies that involve participants in a mobile fashion. We consider this paper to be relevant to academia and industry alike in presenting the state-of-the-art and guiding the steps to designing, conducting, and analyzing results of evaluations in MR/AR.}, author = {Merino, Leonel and Schwarzl, Magdalena and Kraus, Matthias and Sedlmair, Michael and Schmalstieg, Dieter and Weiskopf, Daniel}, booktitle = {IEEE International Symposium on Mixed and Augmented Reality (ISMAR)}, doi = {doi: 10.1109/ISMAR50242.2020.00069.}, title = {Evaluating Mixed and Augmented Reality: A Systematic Literature Review (2009 -- 2019)}, url = {https://ieeexplore.ieee.org/abstract/document/9284762}, year = 2020 }
  Link
  https://ieeexplore.ieee.org/abstract/document/9284762
11. L. Merino et al., “Toward Agile Situated Visualization: An Exploratory User Study,” in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA), in Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA). 2020, p. LBW087:1–LBW087:7. doi: 10.1145/3334480.3383017.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3334480.3383017, abstract = {We introduce AVAR, a prototypical implementation of an agile situated visualization (SV) toolkit targeting liveness, integration, and expressiveness. We report on results of an exploratory study with AVAR and seven expert users. In it, participants wore a Microsoft HoloLens device and used a Bluetooth keyboard to program a visualization script for a given dataset. To support our analysis, we (i) video recorded sessions, (ii) tracked users' interactions, and (iii) collected data of participants' impressions. Our prototype confirms that agile SV is feasible. That is, liveness boosted participants' engagement when programming an SV, and so, the sessions were highly interactive and participants were willing to spend much time using our toolkit (i.e., median ≥ 1.5 hours). Participants used our integrated toolkit to deal with data transformations, visual mappings, and view transformations without leaving the immersive environment. Finally, participants benefited from our expressive toolkit and employed multiple of the available features when programming an SV.}, author = {Merino, Leonel and Sotomayor-G\'{o}mez, Boris and Yu, Xingyao and Salgado, Ronie and Bergel, Alexandre and Sedlmair, Michael and Weiskopf, Daniel}, booktitle = {Proceedings of the CHI Conference on Human Factors in Computing Systems-Extended Abstracts (CHI-EA)}, doi = {10.1145/3334480.3383017}, isbn = {9781450368193}, numpages = {7}, pages = {LBW087:1–LBW087:7}, title = {Toward Agile Situated Visualization: An Exploratory User Study}, url = {https://dl.acm.org/doi/abs/10.1145/3334480.3383017}, year = 2020 }
  Link
  https://dl.acm.org/doi/abs/10.1145/3334480.3383017
12. N. Pathmanathan et al., “Eye vs. Head: Comparing Gaze Methods for Interaction in Augmented Reality,” in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA), in Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA). Stuttgart, Germany: ACM, 2020, pp. 50:1-50:5. doi: 10.1145/3379156.3391829.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3379156.3391829, abstract = {Visualization in virtual 3D environments can provide a natural way for users to explore data. Often, arm and short head movements are required for interaction in augmented reality, which can be tiring and strenuous though. In an effort toward more user-friendly interaction, we developed a prototype that allows users to manipulate virtual objects using a combination of eye gaze and an external clicker device. Using this prototype, we performed a user study comparing four different input methods of which head gaze plus clicker was preferred by most participants.}, articleno = {50}, author = {Pathmanathan, Nelusa and Becher, Michael and Rodrigues, Nils and Reina, Guido and Ertl, Thomas and Weiskopf, Daniel and Sedlmair, Michael}, booktitle = {Proceedings of the Symposium on Eye Tracking Research & Applications (ETRA)}, doi = {10.1145/3379156.3391829}, isbn = {9781450371346}, location = {Stuttgart, Germany}, numpages = {5}, pages = {50:1-50:5}, publisher = {ACM}, title = {Eye vs. Head: Comparing Gaze Methods for Interaction in Augmented Reality}, url = {https://doi.org/10.1145/3379156.3391829}, year = 2020 }
  Link
  https://doi.org/10.1145/3379156.3391829
13. A. Streichert, K. Angerbauer, M. Schwarzl, and M. Sedlmair, “Comparing Input Modalities for Shape Drawing Tasks,” in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Papers (ETRA-SP), in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Papers (ETRA-SP). ACM, 2020, pp. 1–5. doi: 10.1145/3379156.3391830.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{10.1145/3379156.3391830, abstract = {With the growing interest in Immersive Analytics, there is also a need for novel and suitable input modalities for such applications. We explore eye tracking, head tracking, hand motion tracking, and data gloves as input methods for a 2D tracing task and compare them to touch input as a baseline in an exploratory user study (N=20). We compare these methods in terms of user experience, workload, accuracy, and time required for input. The results show that the input method has a significant influence on these measured variables. While touch input surpasses all other input methods in terms of user experience, workload, and accuracy, eye tracking shows promise in respect of the input time. The results form a starting point for future research investigating input methods.}, author = {Streichert, Annalena and Angerbauer, Katrin and Schwarzl, Magdalena and Sedlmair, Michael}, booktitle = {Proceedings of the Symposium on Eye Tracking Research & Applications-Short Papers (ETRA-SP)}, doi = {10.1145/3379156.3391830}, isbn = {9781450371346}, number = 51, pages = {1-5}, publisher = {ACM}, series = {ETRA '20 Short Papers}, title = {Comparing Input Modalities for Shape Drawing Tasks}, url = {https://doi.org/10.1145/3379156.3391830}, year = 2020 }
  Link
  https://doi.org/10.1145/3379156.3391830
14. M. Weiß, K. Angerbauer, A. Voit, M. Schwarzl, M. Sedlmair, and S. Mayer, “Revisited: Comparison of Empirical Methods to Evaluate Visualizations Supporting Crafting and Assembly Purposes,” IEEE Transactions on Visualization and Computer Graphics, pp. 1–10, 2020, doi: 10.1109/TVCG.2020.3030400.
  - BibTeX
  - Link
  BibTeX
  @article{9225008, abstract = {Ubiquitous, situated, and physical visualizations create entirely new possibilities for tasks contextualized in the real world,such as doctors inserting needles. During the development of situated visualizations, evaluating visualizations is a core requirement.However, performing such evaluations is intrinsically hard as the real scenarios are safety-critical or expensive to test. To overcomethese issues, researchers and practitioners adapt classical approaches from ubiquitous computing and use surrogate empiricalmethods such as Augmented Reality (AR), Virtual Reality (VR) prototypes, or merely online demonstrations. This approach’s primaryassumption is that meaningful insights can also be gained from different, usually cheaper and less cumbersome empirical methods.Nevertheless, recent efforts in the Human-Computer Interaction (HCI) community have found evidence against this assumption, whichwould impede the use of surrogate empirical methods. Currently, these insights rely on a single investigation of four interactive objects.The goal of this work is to investigate if these prior findings also hold for situated visualizations. Therefore, we first created a scenariowhere situated visualizations support users in do-it-yourself (DIY) tasks such as crafting and assembly. We then set up five empiricalstudy methods to evaluate the four tasks using an online survey, as well as VR, AR, laboratory, and in-situ studies. Using this studydesign, we conducted a new study with 60 participants. Our results show that the situated visualizations we investigated in this studyare not prone to the same dependency on the empirical method, as found in previous work. Our study provides the first evidence thatanalyzing situated visualizations through different empirical (surrogate) methods might lead to comparable result}, author = {Weiß, M. and Angerbauer, K. and Voit, A. and Schwarzl, M. and Sedlmair, M. and Mayer, S.}, doi = {10.1109/TVCG.2020.3030400}, issn = {1941-0506}, journal = {IEEE Transactions on Visualization and Computer Graphics}, pages = {1-10}, title = {Revisited: Comparison of Empirical Methods to Evaluate Visualizations Supporting Crafting and Assembly Purposes}, url = {https://ieeexplore.ieee.org/document/9225008}, year = 2020 }
  Link
  https://ieeexplore.ieee.org/document/9225008
15. X. Yu, K. Angerbauer, P. Mohr, D. Kalkofen, and M. Sedlmair, “Perspective Matters: Design Implications for Motion Guidance in Mixed Reality,” in Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR), in Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR). 2020.
  - BibTeX
  BibTeX
  @inproceedings{yu2020perspective, author = {Yu, Xingyao and Angerbauer, Katrin and Mohr, Peter and Kalkofen, Denis and Sedlmair, Michael}, booktitle = {Proceedings of the IEEE International Symposium on Mixed and Augmented Reality (ISMAR)}, title = {Perspective Matters: Design Implications for Motion Guidance in Mixed Reality}, year = 2020 }
16. S. Öney et al., “Evaluation of Gaze Depth Estimation from Eye Tracking in Augmented Reality,” in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Paper (ETRA-SP), in Proceedings of the Symposium on Eye Tracking Research & Applications-Short Paper (ETRA-SP). ACM, 2020, pp. 49:1-49:5. doi: 10.1145/3379156.3391835.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{oney_evaluation_2020, abstract = {Gaze tracking in 3D has the potential to improve interaction with objects and visualizations in augmented reality. However, previous research showed that subjective perception of distance varies between real and virtual surroundings. We wanted to determine whether objectively measured 3D gaze depth through eye tracking also exhibits differences between entirely real and augmented environments. To this end, we conducted an experiment (N = 25) in which we used Microsoft HoloLens with a binocular eye tracking add-on from Pupil Labs. Participants performed a task that required them to look at stationary real and virtual objects while wearing a HoloLens device. We were not able to find significant differences in the gaze depth measured by eye tracking. Finally, we discuss our findings and their implications for gaze interaction in immersive analytics, and the quality of the collected gaze data.}, author = {Öney, Seyda and Rodrigues, Nils and Becher, Michael and Reina, Guido and Ertl, Thomas and Sedlmair, Michael and Weiskopf, Daniel}, booktitle = {Proceedings of the Symposium on Eye Tracking Research & Applications-Short Paper (ETRA-SP)}, doi = {10.1145/3379156.3391835}, pages = {49:1-49:5}, publisher = {ACM}, title = {Evaluation of Gaze Depth Estimation from Eye Tracking in Augmented Reality}, url = {https://doi.org/10.1145/3379156.3391835}, year = 2020 }
  Link
  https://doi.org/10.1145/3379156.3391835
2019
1. M. M. Abbas, M. Aupetit, M. Sedlmair, and H. Bensmail, “ClustMe: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns.,” Comput. Graph. Forum, vol. 38, no. 3, Art. no. 3, 2019, [Online]. Available: http://dblp.uni-trier.de/db/journals/cgf/cgf38.html#AbbasASB19
  - BibTeX
  - Link
  BibTeX
  @article{journals/cgf/AbbasASB19, author = {Abbas, Mostafa M. and Aupetit, Michaël and Sedlmair, Michael and Bensmail, Halima}, ee = {https://doi.org/10.1111/cgf.13684}, journal = {Comput. Graph. Forum}, number = 3, pages = {225-236}, title = {ClustMe: A Visual Quality Measure for Ranking Monochrome Scatterplots based on Cluster Patterns.}, url = {http://dblp.uni-trier.de/db/journals/cgf/cgf38.html#AbbasASB19}, volume = 38, year = 2019 }
  Link
  http://dblp.uni-trier.de/db/journals/cgf/cgf38.html#AbbasASB19
2. M. Aupetit, M. Sedlmair, M. M. Abbas, A. Baggag, and H. Bensmail, “Toward Perception-based Evaluation of Clustering Techniques for Visual Analytics,” in Proceedings of the IEEE Visualization Conference (VIS), in Proceedings of the IEEE Visualization Conference (VIS). IEEE, 2019, pp. 141–145. doi: 10.1109/VISUAL.2019.8933620.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{conf/visualization/AupetitSABB19, abstract = {Automatic clustering techniques play a central role in Visual Analytics by helping analysts to discover interesting patterns in high-dimensional data. Evaluating these clustering techniques, however, is difficult due to the lack of universal ground truth. Instead, clustering approaches are usually evaluated based on a subjective visual judgment of low-dimensional scatterplots of different datasets. As clustering is an inherent human-in-the-loop task, we propose a more systematic way of evaluating clustering algorithms based on quantification of human perception of clusters in 2D scatterplots. The core question we are asking is in how far existing clustering techniques align with clusters perceived by humans. To do so, we build on a dataset from a previous study [1], in which 34 human subjects la-beled 1000 synthetic scatterplots in terms of whether they could see one or more than one cluster. Here, we use this dataset to benchmark state-of-the-art clustering techniques in terms of how far they agree with these human judgments. More specifically, we assess 1437 variants of K-means, Gaussian Mixture Models, CLIQUE, DBSCAN, and Agglomerative Clustering techniques on these benchmarks data. We get unexpected results. For instance, CLIQUE and DBSCAN are at best in slight agreement on this basic cluster counting task, while model-agnostic Agglomerative clustering can be up to a substantial agreement with human subjects depending on the variants. We discuss how to extend this perception-based clustering benchmark approach, and how it could lead to the design of perception-based clustering techniques that would better support more trustworthy and explainable models of cluster patterns.}, author = {Aupetit, Michaël and Sedlmair, Michael and Abbas, Mostafa M. and Baggag, Abdelkader and Bensmail, Halima}, booktitle = {Proceedings of the IEEE Visualization Conference (VIS)}, doi = {10.1109/VISUAL.2019.8933620}, ee = {https://doi.org/10.1109/VISUAL.2019.8933620}, isbn = {978-1-7281-4941-7}, pages = {141-145}, publisher = {IEEE}, title = {Toward Perception-based Evaluation of Clustering Techniques for Visual Analytics}, url = {https://ieeexplore.ieee.org/document/8933620}, year = 2019 }
  Link
  https://ieeexplore.ieee.org/document/8933620
3. J. Bernard, M. Hutter, C. Ritter, M. Lehmann, M. Sedlmair, and M. Zeppelzauer, “Visual Analysis of Degree-of-Interest Functions to Support Selection Strategies for Instance Labeling,” in Proceedings of the International Workshop on Visual Analytics (EuroVA), T. von Landesberger and C. Turkay, Eds., in Proceedings of the International Workshop on Visual Analytics (EuroVA). Eurographics Association, 2019, pp. 1–5. doi: 10.2312/eurova.20191116.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{conf/vissym/BernardHRLSZ19, abstract = {Manually labeling data sets is a time-consuming and expensive task that can be accelerated by interactive machine learning andvisual analytics approaches. At the core of these approaches are strategies for the selection of candidate instances to label. Weintroduce degree-of-interest (DOI) functions as atomic building blocks to formalize candidate selection strategies. We introducea taxonomy of DOI functions and an approach for the visual analysis of DOI functions, which provide novel complementaryviews on labeling strategies and DOIs, support their in-depth analysis and facilitate their interpretation. Our method shallsupport the generation of novel and better explanation of existing labeling strategies in futu}, author = {Bernard, Jürgen and Hutter, Marco and Ritter, Christian and Lehmann, Markus and Sedlmair, Michael and Zeppelzauer, Matthias}, booktitle = {Proceedings of the International Workshop on Visual Analytics (EuroVA)}, crossref = {conf/vissym/2019va}, doi = {10.2312/eurova.20191116}, editor = {von Landesberger, Tatiana and Turkay, Cagatay}, ee = {https://doi.org/10.2312/eurova.20191116}, isbn = {978-3-03868-087-1}, pages = {1-5}, publisher = {Eurographics Association}, title = {Visual Analysis of Degree-of-Interest Functions to Support Selection Strategies for Instance Labeling}, url = {https://doi.org/10.2312/eurova.20191116 }, year = 2019 }
  Link
  https://doi.org/10.2312/eurova.20191116
4. U. Brandes and M. Sedlmair, “Network Visualization,” in Network Science: An Aerial View, F. Biagini, G. Kauermann, and T. Meyer-Brandis, Eds., in Network Science: An Aerial View. , Cham: Springer International Publishing, 2019, pp. 5–21. doi: 10.1007/978-3-030-26814-5_2.
  - BibTeX
  - Link
  BibTeX
  @inbook{Brandes2019, abstract = {Data visualization is the art and science of mapping data to graphical variables. In this context, networks give rise to unique difficulties because of inherent dependencies among their elements. We provide a high-level overview of the main challenges and common techniques to address them. They are illustrated with examples from two application domains, social networks and automotive engineering. The chapter concludes with opportunities for future work in network visualization.}, address = {Cham}, author = {Brandes, Ulrik and Sedlmair, Michael}, booktitle = {Network Science: An Aerial View}, doi = {10.1007/978-3-030-26814-5_2}, editor = {Biagini, Francesca and Kauermann, Göran and Meyer-Brandis, Thilo}, isbn = {978-3-030-26814-5}, pages = {5-21}, publisher = {Springer International Publishing}, title = {Network Visualization}, url = {https://doi.org/10.1007/978-3-030-26814-5_2}, year = 2019 }
  Link
  https://doi.org/10.1007/978-3-030-26814-5_2
5. C. Schulz et al., “A Framework for Pervasive Visual Deficiency Simulation,” in Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (VR), in Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (VR). 2019, pp. 1852–1857. doi: 10.1109/VR44988.2019.9044164.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{schulz2019framework, abstract = {We present a framework for rapid prototyping of pervasive visual deficiency simulation in the context of graphical interfaces, virtual reality, and augmented reality. Our framework facilitates the emulation of various visual deficiencies for a wide range of applications, which allows users with normal vision to experience combinations of conditions such as myopia, hyperopia, presbyopia, cataract, nyctalopia, protanopia, deuteranopia, tritanopia, and achromatopsia. Our framework provides an infrastructure to encourage researchers to evaluate visualization and other display techniques regarding visual deficiencies, and opens up the field of visual disease simulation to a broader audience. The benefits of our framework are easy integration, configuration, fast prototyping, and portability to new emerging hardware. To demonstrate the applicability of our framework, we showcase a desktop application and an Android application that transform commodity hardware into glasses for visual deficiency simulation. We expect that this work promotes a greater understanding of visual impairments, leads to better product design for the visually impaired, and forms a basis for research to compensate for these impairments as everyday help}, author = {Schulz, Christoph and Rodrigues, Nils and Amann, Marco and Baumgartner, Daniel and Mielke, Arman and Baumann, Christian and Sedlmair, Michael and Weiskopf, Daniel}, booktitle = {Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces (VR)}, doi = {10.1109/VR44988.2019.9044164}, pages = {1852-1857}, title = {A Framework for Pervasive Visual Deficiency Simulation}, url = {https://ieeexplore.ieee.org/document/9044164}, year = 2019 }
  Link
  https://ieeexplore.ieee.org/document/9044164
6. S. Sippl, M. Sedlmair, and M. Waldner, “Collecting and Structuring Information in the Information Collage,” Technical Report, CoRR, vol. abs/1909.00608, pp. 1–21, 2019, [Online]. Available: https://arxiv.org/abs/1909.00608
  - BibTeX
  - Link
  BibTeX
  @article{journals/corr/abs-1909-00608, abstract = {Knowledge workers, such as scientists, journalists, or consultants, adaptively seek, gather, and consume information. These processes are often inefficient as existing user interfaces provide limited possibilities to combine information from various sources and different formats into a common knowledge representation. In this paper, we present the concept of an information collage (IC) -- a web browser extension combining manual spatial organization of gathered information fragments and automatic text analysis for interactive content exploration and expressive visual summaries. We used IC for case studies with knowledge workers from different domains and longer-term field studies over a period of one month. We identified three different ways how users collect and structure information and provide design recommendations how to support these observed usage strategies.}, author = {Sippl, Sebastian and Sedlmair, Michael and Waldner, Manuela}, ee = {http://arxiv.org/abs/1909.00608}, journal = {Technical Report, CoRR}, pages = {1-21}, title = {Collecting and Structuring Information in the Information Collage}, url = {https://arxiv.org/abs/1909.00608}, volume = {abs/1909.00608}, year = 2019 }
  Link
  https://arxiv.org/abs/1909.00608
7. F. Sperrle, J. Bernard, M. Sedlmair, D. A. Keim, and M. El-Assady, “Speculative Execution for Guided Visual Analytics,” Workshop forWorkshop for Machine Learning from User Interaction for Visualization and Analytics at IEEE VIS, 2018, vol. abs/1908.02627, 2019, [Online]. Available: http://dblp.uni-trier.de/db/journals/corr/corr1908.html#abs-1908-02627
  - BibTeX
  - Link
  BibTeX
  @article{journals/corr/abs-1908-02627, abstract = {We propose the concept of Speculative Execution for Visual Analytics and discuss its effectiveness for model exploration and optimization. Speculative Execution enables the automatic generation of alternative, competing model configurations that do not alter the current model state unless explicitly confirmed by the user. These alternatives are computed based on either user interactions or model quality measures and can be explored using delta-visualizations. By automatically proposing modeling alternatives, systems employing Speculative Execution can shorten the gap between users and models, reduce the confirmation bias and speed up optimization processes. In this paper, we have assembled five application scenarios showcasing the potential of Speculative Execution, as well as a potential for further research.}, author = {Sperrle, Fabian and Bernard, Jürgen and Sedlmair, Michael and Keim, Daniel A. and El-Assady, Mennatallah}, ee = {http://arxiv.org/abs/1908.02627}, journal = {Workshop forWorkshop for Machine Learning from User Interaction for Visualization and Analytics at IEEE VIS, 2018}, title = {Speculative Execution for Guided Visual Analytics}, url = {http://dblp.uni-trier.de/db/journals/corr/corr1908.html#abs-1908-02627}, volume = {abs/1908.02627}, year = 2019 }
  Link
  http://dblp.uni-trier.de/db/journals/corr/corr1908.html#abs-1908-02627
8. C. Stoiber et al., “netflower: Dynamic Network Visualization for Data Journalists.,” Comput. Graph. Forum, vol. 38, no. 3, Art. no. 3, 2019, [Online]. Available: http://dblp.uni-trier.de/db/journals/cgf/cgf38.html#StoiberRGGGSEA19
  - BibTeX
  - Link
  BibTeX
  @article{journals/cgf/StoiberRGGGSEA19, author = {Stoiber, Christina and Rind, Alexander and Grassinger, Florian and Gutounig, Robert and Goldgruber, Eva and Sedlmair, Michael and Emrich, Stefan and Aigner, Wolfgang}, ee = {https://doi.org/10.1111/cgf.13721}, journal = {Comput. Graph. Forum}, number = 3, pages = {699-711}, title = {netflower: Dynamic Network Visualization for Data Journalists.}, url = {http://dblp.uni-trier.de/db/journals/cgf/cgf38.html#StoiberRGGGSEA19}, volume = 38, year = 2019 }
  Link
  http://dblp.uni-trier.de/db/journals/cgf/cgf38.html#StoiberRGGGSEA19
9. Y. Wang et al., “Optimizing Color Assignment for Perception of Class Separability in Multiclass Scatterplots,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, Art. no. 1, 2019, doi: 10.1109/TVCG.2018.2864912.
  - BibTeX
  BibTeX
  @article{wang2019optimizing, affiliation = {Wang, YH (Reprint Author), Shandong Univ, Jinan, Shandong, Peoples R China. Wang, Yunhai; Chen, Xin; Ge, Tong; Bao, Chen, Shandong Univ, Jinan, Shandong, Peoples R China. Chen, Baoquan, Peking Univ, Beijing, Peoples R China. Fu, Chi-Wing, Chinese Univ Hong Kong, Hong Kong, Peoples R China. Sedlmair, Michael, Univ Stuttgart, VISUS, Stuttgart, Germany. Deussen, Oliver, Konstanz Univ, Constance, Germany. Deussen, Oliver, SIAT, Shenzhen VisuCA Key Lab, Shenzhen, Peoples R China.}, author = {Wang, Yunhai and Chen, Xin and Ge, Tong and Bao, Chen and Sedlmair, Michael and Fu, Chi-Wing and Deussen, Oliver and Chen, Baoquan}, doi = {10.1109/TVCG.2018.2864912}, issn = {{1077-2626} and {1941-0506}}, journal = {IEEE Transactions on Visualization and Computer Graphics}, language = {eng}, number = 1, pages = {820-829}, publisher = {IEEE}, research-areas = {Computer Science}, title = {Optimizing Color Assignment for Perception of Class Separability in Multiclass Scatterplots}, unique-id = {ISI:000452640000078}, volume = 25, year = 2019 }
10. Y. Wang et al., “Structure-aware Fisheye Views for Efficient Large Graph Exploration,” IEEE Transactions on Visualization and Computer Graphics, vol. 25, no. 1, Art. no. 1, 2019, [Online]. Available: http://dblp.uni-trier.de/db/journals/tvcg/tvcg25.html#WangWZSFSCD19
  - BibTeX
  - Link
  BibTeX
  @article{journals/tvcg/WangWZSFSCD19, author = {Wang, Yunhai and Wang, Yanyan and Zhang, Haifeng and Sun, Yinqi and Fu, Chi-Wing and Sedlmair, Michael and Chen, Baoquan and Deussen, Oliver}, ee = {http://doi.ieeecomputersociety.org/10.1109/TVCG.2018.2864911}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 1, pages = {566-575}, title = {Structure-aware Fisheye Views for Efficient Large Graph Exploration}, url = {http://dblp.uni-trier.de/db/journals/tvcg/tvcg25.html#WangWZSFSCD19}, volume = 25, year = 2019 }
  Link
  http://dblp.uni-trier.de/db/journals/tvcg/tvcg25.html#WangWZSFSCD19
11. Y. Wang et al., “Improving the Robustness of Scagnostics,” IEEE Transactions on Visualization and Computer Graphics, vol. 26, no. 1, Art. no. 1, 2019, doi: 10.1109/TVCG.2019.2934796.
  - BibTeX
  - Link
  BibTeX
  @article{journals/tvcg/WangWLCCDS20, abstract = {In this paper, we examine the robustness of scagnostics through a series of theoretical and empirical studies. First, we investigate the sensitivity of scagnostics by employing perturbing operations on more than 60M synthetic and real-world scatterplots. We found that two scagnostic measures, Outlying and Clumpy, are overly sensitive to data binning. To understand how these measures align with human judgments of visual features, we conducted a study with 24 participants, which reveals that i) humans are not sensitive to small perturbations of the data that cause large changes in both measures, and ii) the perception of clumpiness heavily depends on per-cluster topologies and structures. Motivated by these results, we propose Robust Scagnostics (RScag) by combining adaptive binning with a hierarchy-based form of scagnostics. An analysis shows that RScag improves on the robustness of original scagnostics, aligns better with human judgments, and is equally fast as the traditional scagnostic measures.}, author = {Wang, Yunhai and Wang, Zeyu and Liu, Tingting and Correll, Michael and Cheng, Zhanglin and Deussen, Oliver and Sedlmair, Michael}, doi = {10.1109/TVCG.2019.2934796}, ee = {https://doi.org/10.1109/TVCG.2019.2934796}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 1, pages = {759-769}, title = {Improving the Robustness of Scagnostics}, url = {https://ieeexplore.ieee.org/document/8807247}, volume = 26, year = 2019 }
  Link
  https://ieeexplore.ieee.org/document/8807247
12. M. Zhao, H. Qu, and M. Sedlmair, “Neighborhood Perception in Bar Charts.,” in Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI), S. A. Brewster, G. Fitzpatrick, A. L. Cox, and V. Kostakos, Eds., in Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI). ACM, 2019, p. 232. [Online]. Available: http://dblp.uni-trier.de/db/conf/chi/chi2019.html#ZhaoQS19
  - BibTeX
  - Link
  BibTeX
  @inproceedings{conf/chi/ZhaoQS19, author = {Zhao, Mingqian and Qu, Huamin and Sedlmair, Michael}, booktitle = {Proceedings of the ACM Conference on Human Factors in Computing Systems (CHI)}, crossref = {conf/chi/2019}, editor = {Brewster, Stephen A. and Fitzpatrick, Geraldine and Cox, Anna L. and Kostakos, Vassilis}, ee = {https://doi.org/10.1145/3290605.3300462}, isbn = {978-1-4503-5970-2}, pages = 232, publisher = {ACM}, title = {Neighborhood Perception in Bar Charts.}, url = {http://dblp.uni-trier.de/db/conf/chi/chi2019.html#ZhaoQS19}, year = 2019 }
  Link
  http://dblp.uni-trier.de/db/conf/chi/chi2019.html#ZhaoQS19
2018
1. J. Bernard, M. Zeppelzauer, M. Lehmann, M. Müller, and M. Sedlmair, “Towards User-Centered Active Learning Algorithms,” Computer Graphics Forum, vol. 37, no. 3, Art. no. 3, 2018, [Online]. Available: http://dblp.uni-trier.de/db/journals/cgf/cgf37.html#BernardZLMS18
  - BibTeX
  - Link
  BibTeX
  @article{journals/cgf/BernardZLMS18, author = {Bernard, Jürgen and Zeppelzauer, Matthias and Lehmann, Markus and Müller, Martin and Sedlmair, Michael}, ee = {https://doi.org/10.1111/cgf.13406}, journal = {Computer Graphics Forum}, number = 3, pages = {121-132}, title = {Towards User-Centered Active Learning Algorithms}, url = {http://dblp.uni-trier.de/db/journals/cgf/cgf37.html#BernardZLMS18}, volume = 37, year = 2018 }
  Link
  http://dblp.uni-trier.de/db/journals/cgf/cgf37.html#BernardZLMS18
2. M. El-Assady, F. Sperrle, R. Sevastjanova, M. Sedlmair, and D. A. Keim, “LTMA: Layered Topic Matching for the Comparative Exploration, Evaluation, and Refinement of Topic Modeling Results,” in Proceedings of the International Symposium on Big Data Visual and Immersive Analytics (BDVA), in Proceedings of the International Symposium on Big Data Visual and Immersive Analytics (BDVA). IEEE, 2018, pp. 1–10. doi: 10.1109/BDVA.2018.8534018.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{conf/bdva/El-AssadySSSK18, abstract = {We present LTMA, a Layered Topic Matching approach for the unsupervised comparative analysis of topic modeling results. Due to the vast number of available modeling algorithms, an efficient and effective comparison of their results is detrimental to a data- and task-driven selection of a model. LTMA automates this comparative analysis by providing topic matching based on two layers (document-overlap and keyword-similarity), creating a novel topic-match data structure. This data structure builds a basis for model exploration and optimization, thus, allowing for an efficient evaluation of their performance in the context of a given type of text data and task. This is especially important for text types where an annotated gold standard dataset is not readily available and, therefore, quantitative evaluation methods are not applicable. We confirm the usefulness of our technique based on three use cases, namely: (1) the automatic comparative evaluation of topic models, (2) the visual exploration of topic modeling differences, and (3) the optimization of topic modeling results through combining matches.}, author = {El-Assady, Mennatallah and Sperrle, Fabian and Sevastjanova, Rita and Sedlmair, Michael and Keim, Daniel A.}, booktitle = {Proceedings of the International Symposium on Big Data Visual and Immersive Analytics (BDVA)}, crossref = {conf/bdva/2018}, doi = {10.1109/BDVA.2018.8534018}, ee = {https://doi.org/10.1109/BDVA.2018.8534018}, isbn = {978-1-5386-9194-6}, pages = {1-10}, publisher = {IEEE}, title = {LTMA: Layered Topic Matching for the Comparative Exploration, Evaluation, and Refinement of Topic Modeling Results}, url = {https://ieeexplore.ieee.org/document/8534018}, year = 2018 }
  Link
  https://ieeexplore.ieee.org/document/8534018
3. T. Torsney-Weir, S. Afroozeh, M. Sedlmair, and T. Möller, “Risk Fixers and Sweet Spotters: a Study of the Different Approaches to Using Visual Sensitivity Analysis in an Investment Scenario,” in Proceedings of the Eurographics Conference on Visualization (EuroVis), J. Johansson, F. Sadlo, and T. Schreck, Eds., in Proceedings of the Eurographics Conference on Visualization (EuroVis). Eurographics Association, 2018, pp. 119–123. doi: 10.2312/eurovisshort.20181089.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{conf/vissym/Torsney-WeirASM18, abstract = {We present an empirical study that illustrates how individual users' decision making preferences and biases influence visualization design choices. Twenty-three participants, in a lab study, were shown two interactive financial portfolio optimization interfaces which allowed them to adjust the return for the portfolio and view how the risk changes. One interface showed the sensitivity of the risk to changes in the return and one did not have this feature. Our study highlights two classes of users. One which preferred the interface with the sensitivity feature and one group that does not prefer the sensitivity feature. We named these two groups the "risk fixers" and the "sweet spotters" due to the analysis method they used. The "risk fixers" selected a level of risk which they were comfortable with while the "sweet spotters" tried to find a point right before the risk increased greatly. Our study shows that exposing the sensitivity of investment parameters will impact the investment decision process and increase confidence for these "sweet spotters." We also discuss the implications for design.}, author = {Torsney-Weir, Thomas and Afroozeh, Shahrzad and Sedlmair, Michael and Möller, Torsten}, booktitle = {Proceedings of the Eurographics Conference on Visualization (EuroVis)}, crossref = {conf/vissym/2018}, doi = {10.2312/eurovisshort.20181089}, editor = {Johansson, Jimmy and Sadlo, Filip and Schreck, Tobias}, ee = {https://doi.org/10.2312/eurovisshort.20181089}, pages = {119-123}, publisher = {Eurographics Association}, title = {Risk Fixers and Sweet Spotters: a Study of the Different Approaches to Using Visual Sensitivity Analysis in an Investment Scenario}, url = {https://dl.acm.org/doi/abs/10.5555/3290776.3290801}, year = 2018 }
  Link
  https://dl.acm.org/doi/abs/10.5555/3290776.3290801
4. A. C. Valdez, M. Ziefle, and M. Sedlmair, “Priming and Anchoring Effects in Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 1, Art. no. 1, 2018, doi: 10.1109/TVCG.2017.2744138.
  - BibTeX
  - Link
  BibTeX
  @article{journals/tvcg/ValdezZS18, abstract = {We investigate priming and anchoring effects on perceptual tasks in visualization. Priming or anchoring effects depict the phenomena that a stimulus might influence subsequent human judgments on a perceptual level, or on a cognitive level by providing a frame of reference. Using visual class separability in scatterplots as an example task, we performed a set of five studies to investigate the potential existence of priming and anchoring effects. Our findings show that - under certain circumstances - such effects indeed exist. In other words, humans judge class separability of the same scatterplot differently depending on the scatterplot(s) they have seen before. These findings inform future work on better understanding and more accurately modeling human perception of visual patterns.}, author = {Valdez, André Calero and Ziefle, Martina and Sedlmair, Michael}, doi = {10.1109/TVCG.2017.2744138}, ee = {https://www.wikidata.org/entity/Q47967395}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 1, pages = {584-594}, title = {Priming and Anchoring Effects in Visualization}, url = {https://ieeexplore.ieee.org/document/8022891}, volume = 24, year = 2018 }
  Link
  https://ieeexplore.ieee.org/document/8022891
5. Y. Wang et al., “A Perception-driven Approach to Supervised Dimensionality Reduction for Visualization,” IEEE Transactions on Visualization and Computer Graphics, vol. 24, no. 5, Art. no. 5, 2018, doi: 10.1109/TVCG.2017.2701829.
  - BibTeX
  - Link
  BibTeX
  @article{journals/tvcg/WangFCZFSYC18, abstract = {Dimensionality reduction (DR) is a common strategy for visual analysis of labeled high-dimensional data. Low-dimensional representations of the data help, for instance, to explore the class separability and the spatial distribution of the data. Widely-used unsupervised DR methods like PCA do not aim to maximize the class separation, while supervised DR methods like LDA often assume certain spatial distributions and do not take perceptual capabilities of humans into account. These issues make them ineffective for complicated class structures. Towards filling this gap, we present a perception-driven linear dimensionality reduction approach that maximizes the perceived class separation in projections. Our approach builds on recent developments in perception-based separation measures that have achieved good results in imitating human perception. We extend these measures to be density-aware and incorporate them into a customized simulated annealing algorithm, which can rapidly generate a near optimal DR projection. We demonstrate the effectiveness of our approach by comparing it to state-of-the-art DR methods on 93 datasets, using both quantitative measure and human judgments. We also provide case studies with class-imbalanced and unlabeled data.}, author = {Wang, Yunhai and Feng, Kang and Chu, Xiaowei and Zhang, Jian and Fu, Chi-Wing and Sedlmair, Michael and Yu, Xiaohui and Chen, Baoquan}, doi = {10.1109/TVCG.2017.2701829}, ee = {https://www.wikidata.org/entity/Q38794233}, journal = {IEEE Transactions on Visualization and Computer Graphics}, number = 5, pages = {1828-1840}, title = {A Perception-driven Approach to Supervised Dimensionality Reduction for Visualization}, url = {https://www.computer.org/csdl/journal/tg/2018/05/07920403/13rRUEgs2M7}, volume = 24, year = 2018 }
  Link
  https://www.computer.org/csdl/journal/tg/2018/05/07920403/13rRUEgs2M7
6. Y. Wang et al., “Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization.,” IEEE Trans. Vis. Comput. Graph., vol. 24, no. 1, Art. no. 1, 2018, doi: 10.1109/TVCG.2017.2745919.
  - BibTeX
  - Link
  BibTeX
  @article{journals/tvcg/WangWSZLFSDC18, abstract = {We present an improved stress majorization method that incorporates various constraints, including directional constraints without the necessity of solving a constraint optimization problem. This is achieved by reformulating the stress function to impose constraints on both the edge vectors and lengths instead of just on the edge lengths (node distances). This is a unified framework for both constrained and unconstrained graph visualizations, where we can model most existing layout constraints, as well as develop new ones such as the star shapes and cluster separation constraints within stress majorization. This improvement also allows us to parallelize computation with an efficient GPU conjugant gradient solver, which yields fast and stable solutions, even for large graphs. As a result, we allow the constraint-based exploration of large graphs with 10K nodes - an approach which previous methods cannot support.}, author = {Wang, Yunhai and Wang, Yanyan and Sun, Yinqi and Zhu, Lifeng and Lu, Kecheng and Fu, Chi-Wing and Sedlmair, Michael and Deussen, Oliver and Chen, Baoquan}, doi = {10.1109/TVCG.2017.2745919}, ee = {https://www.wikidata.org/entity/Q50230357}, journal = {IEEE Trans. Vis. Comput. Graph.}, number = 1, pages = {489-499}, title = {Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization.}, url = {https://ieeexplore.ieee.org/document/8017634}, volume = 24, year = 2018 }
  Link
  https://ieeexplore.ieee.org/document/8017634
2017
1. D. Sacha et al., “What You See Is What You Can Change: Human-Centered Machine Learning by Interactive Visualization,” Neurocomputing, vol. 268, pp. 164–175, 2017, doi: 10.1016/j.neucom.2017.01.105.
  - BibTeX
  BibTeX
  @article{sacha2017change, affiliation = {Sacha, D (Reprint Author), Univ Konstanz, D-78457 Constance, Germany. Sacha, Dominik, Univ Konstanz, D-78457 Constance, Germany. Keim, Daniel A., Univ Konstanz, Data Anal \& Visualizat Res Grp, Comp Sci Dept, D-78457 Constance, Germany. Sedlmair, Michael, Univ Vienna, A-1090 Vienna, Austria. Zhang, Leishi, Middlesex Univ, Data Intens Syst, London NW4 4BT, England. Lee, John A., Catholic Univ Louvain, B-1348 Louvain La Neuve, Belgium. Peltonen, Jaakko, Aalto Univ, FI-00076 Aalto, Finland. Peltonen, Jaakko, Univ Tampere, Tampere 33014, Finland. Weiskopf, Daniel, Univ Stuttgart, D-70174 Stuttgart, Germany. North, Stephen C., Infovisible, Oldwick, NJ 08858 USA.}, author = {Sacha, Dominik and Sedlmair, Michael and Zhang, Leishi and Lee, John A. and Peltonen, Jaakko and Weiskopf, Daniel and North, Stephen C. and Keim, Daniel A.}, da = {2018-08-07}, doi = {10.1016/j.neucom.2017.01.105}, eissn = {1872-8286}, issn = {0925-2312}, journal = {Neurocomputing}, language = {English}, pages = {164-175}, publisher = {Elsevier}, research-areas = {Computer Science}, title = {What You See Is What You Can Change: Human-Centered Machine Learning by Interactive Visualization}, type = {Article}, unique-id = {ISI:000411541000017}, volume = 268, year = 2017 }
2. D. Sacha et al., “Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis,” IEEE Transactions on Visualization and Computer Graphics, vol. 23, no. 1, Art. no. 1, 2017, doi: 10.1109/TVCG.2016.2598495.
  - BibTeX
  BibTeX
  @article{sacha2017visual, affiliation = {Sacha, D (Reprint Author), Univ Konstanz, Constance, Germany. Sacha, Dominik; Keim, Daniel A., Univ Konstanz, Constance, Germany. Zhang, Leishi, Middlesex Univ, London N17 8HR, England. Sedlmair, Michael, Univ Vienna, A-1010 Vienna, Austria. Lee, John A., Catholic Univ Louvain, ISSS, IREC, MIRO, Louvain, Belgium. Lee, John A., Belgian FRS FNRS, Brussels, Belgium. Peltonen, Jaakko, Aalto Univ, Helsinki Inst Informat Technol, Espoo, Finland. Peltonen, Jaakko, Univ Tampere, Tampere, Finland. Weiskopf, Daniel, Univ Stuttgart, VISUS, Stuttgart, Germany. North, Stephen C., Infovisible LLC, Oldwick, NJ USA.}, author = {Sacha, Dominik and Zhang, Leishi and Sedlmair, Michael and Lee, John A. and Peltonen, Jaakko and Weiskopf, Daniel and North, Stephen C. and Keim, Daniel A.}, da = {2018-08-07}, doi = {10.1109/TVCG.2016.2598495}, eissn = {1941-0506}, issn = {1077-2626}, journal = {IEEE Transactions on Visualization and Computer Graphics}, language = {English}, number = 1, pages = {241-250}, research-areas = {Computer Science}, title = {Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis}, unique-id = {ISI:000395537600027}, volume = 23, year = 2017 }
2016
1. M. Aupetit and M. Sedlmair, “SepMe: 2002 New Visual Separation Measures.,” in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis), C. Hansen, I. Viola, and X. Yuan, Eds., in Proceedings of the IEEE Pacific Visualization Symposium (PacificVis). IEEE, 2016, pp. 1–8. doi: 10.1109/PACIFICVIS.2016.7465244.
  - BibTeX
  - Link
  BibTeX
  @inproceedings{conf/apvis/AupetitS16, abstract = {Our goal is to accurately model human class separation judgements in color-coded scatterplots. Towards this goal, we propose a set of 2002 visual separation measures, by systematically combining 17 neighborhood graphs and 14 class purity functions, with different parameterizations. Using a Machine Learning framework, we evaluate these measures based on how well they predict human separation judgements. We found that more than 58% of the 2002 new measures outperform the best state-of-the-art Distance Consistency (DSC) measure. Among the 2002, the best measure is the average proportion of same-class neighbors among the 0.35-Observable Neighbors of each point of the target class (short GONG 0.35 DIR CPT), with a prediction accuracy of 92.9%, which is 11.7% better than DSC. We also discuss alternative, well-performing measures and give guidelines when to use which.}, author = {Aupetit, Michaël and Sedlmair, Michael}, booktitle = {Proceedings of the IEEE Pacific Visualization Symposium (PacificVis)}, crossref = {conf/apvis/2016}, doi = {10.1109/PACIFICVIS.2016.7465244}, editor = {Hansen, Chuck and Viola, Ivan and Yuan, Xiaoru}, ee = {http://doi.ieeecomputersociety.org/10.1109/PACIFICVIS.2016.7465244}, isbn = {978-1-5090-1451-4}, pages = {1-8}, publisher = {IEEE}, title = {SepMe: 2002 New Visual Separation Measures.}, url = {https://ieeexplore.ieee.org/abstract/document/7465244}, year = 2016 }
  Link
  https://ieeexplore.ieee.org/abstract/document/7465244
2. M. Hund et al., “Visual Analytics for Concept Exploration in Subspaces of Patient Groups,” Brain Informatics, vol. 3, no. 4, Art. no. 4, 2016, doi: 10.1007/s40708-016-0043-5.
  - BibTeX
  - Link
  BibTeX
  @article{journals/braininf/HundBSSSUKMH16, abstract = {Medical doctors and researchers in bio-medicine are increasingly confronted with complex patient data, posing new and difficult analysis challenges. These data are often comprising high-dimensional descriptions of patient conditions and measurements on the success of certain therapies. An important analysis question in such data is to compare and correlate patient conditions and therapy results along with combinations of dimensions. As the number of dimensions is often very large, one needs to map them to a smaller number of relevant dimensions to be more amenable for expert analysis. This is because irrelevant, redundant, and conflicting dimensions can negatively affect effectiveness and efficiency of the analytic process (the so-called curse of dimensionality). However, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We demonstrate the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we present SubVIS, an interactive tool to visually explore subspace clusters from different perspectives, introduce a novel analysis workflow, and discuss future directions for high-dimensional (medical) data analysis and its visual exploration. We apply the presented workflow to a real-world dataset from the medical domain and show its usefulness with a domain expert evaluation.}, author = {Hund, Michael and Böhm, Dominic and Sturm, Werner and Sedlmair, Michael and Schreck, Tobias and Ullrich, Torsten and Keim, Daniel A. and Majnaric, Ljiljana and Holzinger, Andreas}, doi = {10.1007/s40708-016-0043-5}, ee = {https://www.wikidata.org/entity/Q30828408}, journal = {Brain Informatics}, number = 4, pages = {233-247}, title = {Visual Analytics for Concept Exploration in Subspaces of Patient Groups}, url = {https://doi.org/10.1007/s40708-016-0043-5}, volume = 3, year = 2016 }
  Link
  https://doi.org/10.1007/s40708-016-0043-5
3. D. Sacha et al., “Human-Centered Machine Learning Through Interactive Visualization: Review and Open Challenges.,” in Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), in Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). 2016. [Online]. Available: http://dblp.uni-trier.de/db/conf/esann/esann2016.html#SachaSZLWNK16
  - BibTeX
  - Link
  BibTeX
  @inproceedings{conf/esann/SachaSZLWNK16, author = {Sacha, Dominik and Sedlmair, Michael and Zhang, Leishi and Lee, John Aldo and Weiskopf, Daniel and North, Stephen C. and Keim, Daniel A.}, booktitle = {Proceedings of the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)}, crossref = {conf/esann/2016}, ee = {http://www.elen.ucl.ac.be/Proceedings/esann/esannpdf/es2016-166.pdf}, title = {Human-Centered Machine Learning Through Interactive Visualization: Review and Open Challenges.}, url = {http://dblp.uni-trier.de/db/conf/esann/esann2016.html#SachaSZLWNK16}, year = 2016 }
  Link
  http://dblp.uni-trier.de/db/conf/esann/esann2016.html#SachaSZLWNK16
2015
1. M. Sedlmair and M. Aupetit, “Data-driven Evaluation of Visual Quality Measures,” Computer Graphics Forum, vol. 34, no. 3, Art. no. 3, 2015, doi: 10.1111/cgf.12632.
  - BibTeX
  - Link
  BibTeX
  @article{journals/cgf/SedlmairA15, abstract = {Visual quality measures seek to algorithmically imitate human judgments of patterns such as class separability, correlation, or outliers. In this paper, we propose a novel data-driven framework for evaluating such measures. The basic idea is to take a large set of visually encoded data, such as scatterplots, with reliable human "ground truth" judgements, and to use this human-labeled data to learn how well a measure would predict human judgements on previously unseen data. Measures can then be evaluated based on predictive performance-an approach that is crucial for generalizing across datasets but has gained little attention so far. To illustrate our framework, we use it to evaluate 15 state-of-the-art class separation measures, using human ground truth data from 828 class separation judgments on color-coded 2D scatterplots.}, author = {Sedlmair, Michael and Aupetit, Michaël}, doi = {10.1111/cgf.12632}, ee = {http://dx.doi.org/10.1111/cgf.12632}, journal = {Computer Graphics Forum}, number = 3, pages = {201-210}, title = {Data-driven Evaluation of Visual Quality Measures}, url = {https://dl.acm.org/doi/10.5555/2858877.2858899}, volume = 34, year = 2015 }
  Link
  https://dl.acm.org/doi/10.5555/2858877.2858899

Weitere Publikationen 2007—2018

2018

Leilani Battle, Marco Angelini, Carsten Binnig, Tiziana Catarci, Philipp Eichmann, Jean-Daniel Fekete, Giuseppe Santucci, Michael Sedlmair, Wesley Willett.
Evaluating Visual Data Analysis Systems: A Discussion Report.
In Proceedings of the Workshop on Human-In-the-Loop Data Analytics (HILDA'18), ACM, 2018.
pdf

Thomas Torsney-Weir, Torsten Möller, Michael Sedlmair, Mike Kirby.
Hypersliceplorer: Interactive Visualization of Shapes in Multiple Dimensions.
In Computer Graphics Forum (Proc. EuroVis 2018), 37(3): 229-240, 2018.
pdf, video

Jürgen Bernard, Marco Hutter, Markus Lehmann, Martin Müller, Matthias Zeppelzauer, Michael Sedlmair.
Learning from the Best – Visual Analysis of Quasi-Optimal Data Labeling Strategies.
EuroVis 2018 - short papers. 2018.
pdf, suppl. material (pdf)

Rene Cutura, Stefan Holzer, Michael Aupetit, Michael Sedlmair.
VisCoDeR: A Tool for Visually Comparing Dimensionality Reduction Algorithms.
In Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2018.
pdf, video, tool

Jürgen Bernard, Matthias Zeppelzauer, Michael Sedlmair, Wolfgang Aigner.
VIAL – A Unified Process for Visual-Interactive Labeling.
In The Visual Computer (Special Issue EuroVA), 34(9): 189-1207, 2018.
preprint

Elena Rudkowsky, Martin Haselmayer, Matthias Wastian, Marcelo Jenny, Stefan Emrich, Michael Sedlmair.
More than Bags of Words: Sentiment Analysis with Word Embeddings.
In Communication Methods and Measures, 12(2-3): 140-157, 2018.
preprint, final version on Tayolor & Francis

Yunhai Wang, Xiaowei Chu, Chen Bao, Lifeng Zhu, Oliver Deussen, Baoquan Chen, Michael Sedlmair.
EdWordle: Consistency-preserving Word Cloud Editing.
In IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis 2017), 24(1): 647-656, 2018.
pdf, video

Yunhai Wang, Yanyan Wang, Yinqi Sun, Lifeng Zhu, Kecheng Lu, Chi-Wing Fu, Michael Sedlmair, Oliver Deussen, Baoquan Chen.
Revisiting Stress Majorization as a Unified Framework for Interactive Constrained Graph Visualization.
In IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis 2017), 24(1): 489-499, 2018.
pdf, video

Jürgen Bernard, Marco Hutter, Matthias Zeppelzauer, Dieter Fellner, Michael Sedlmair.
Comparing Visual-Interactive Labeling with Active Learning: An Experimental Study.
In IEEE Transactions on Visualization and Computer Graphics (Proc. VAST 2017), 24(1): 298-308, 2018.
pdf, video, 30s video

2017

Michael Oppermann, Torsten Möller, Michael Sedlmair.
Bike Sharing Atlas: Visual Analysis of Bike-Sharing Networks.
In International Journal of Transportation, to appear.
pdf, video, bikesharingatlas.org

André Calero Valdez, Martina Ziefle, Michael Sedlmair.
A Framework for Studying Biases in Visualization Research.
IEEE VIS Workshop on Dealing with Cognitive Biases in Visualisations (DECISIVe 2017).
pdf

Elena Rudkowsky, Martin Haselmayer, Matthias Wastian, Marcelo Jenny, Stefan Emrich, Michael Sedlmair.
Supervised Sentiment Analysis of Parliamentary Speeches and News Reports.
67th Annual Conference of the International Communication Association (ICA), Panel on ‘ Automatic Sentiment Analysis’, 25-29 May 2017.
pdf

Marcelo Jenny, Martin Haselmayer, Elena Rudkowsky, Matthias Wastian, Stefan Emrich, Michael Sedlmair.
Incivility in Austrian parliamentary debates: A supervised sentiment analysis of parliamentary speeches.
Workshop Political Incivility in Parliament, ECPR Joint Sessions of Workshop, Nottingham, 25-30 April 2017.
pdf

Dominik Sacha, Michael Sedlmair, Leishi Zhang, John A. Lee, Jaakko Peltonen, Daniel Weiskopf, Stephen C. North, and Daniel A. Keim.
What You See Is What You Can Change: Human-Centered Machine Learning By Interactive Visualization.
Neurocomputing, 268: 164-175, 2017.
pdf, html version

Jüregen Bernard, Eduard Dobermann, Michael Sedlmair, and Dieter Fellner.
Combining Cluster and Outlier Analysis with Visual Analytics.
In Proc. EuroVis Workshop on Visual Analytics (EuroVA), 2017.
pdf

Jüregen Bernard, Matthias Zeppelzauer, Michael Sedlmair, and Wolfgang Aigner.
A Unified Process for Visual-Interactive Labeling.
In Proc. EuroVis Workshop on Visual Analytics (EuroVA), 2017.
pdf

Thomas Torsney-Weir, Michael Sedlmair, Torsten Möller.
Sliceplorer: 1D Slices for Multi-dimensional Continuous Functions.
In Computer Graphics Forum (Proc. EuroVis 2017), 36(3): 167-177, 2017.
pdf, evaluation page, youtube video

Petra Isenberg, Florian Heimerl, Steffen Koch, Tobias Isenberg, Panpan Xu, Chad Stolper, Michael Sedlmair, Jian Chen, Torsten Möller, John Stasko.
vispubdata.org: A Metadata Collection about IEEE Visualization (VIS) Publications.
In IEEE Transactions on Visualization and Computer Graphics (TVCG), 23(9): 2199-2206, 2017.
pdf

Dominik Sacha, Leishi Zhang, Michael Sedlmair, John Aldo Lee, Jaakko Peltonen, Daniel Weiskopf, Stephen North, and Daniel A. Keim.
Visual Interaction with Dimensionality Reduction: A Structured Literature Analysis.
In IEEE Transactions on Visualization and Computer Graphics (Proc. VAST 2016), 23(1): 241-250, 2017.
pdf

Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Jian Chen, Möller Torsten.
Visualization as seen through its research paper keywords.
In IEEE Transactions on Visualization and Computer Graphics (Proc. SciVis 2016), 23(1): 771-780, 2017.
pdf, suppl. material (pdf), suppl. material (zip)

2016

Michael Sedlmair.
Design study contributions come in different guises: Seven guiding scenarios.
In Proceedings of the IEEE VIS Workshop Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV). ACM Press, 2016.
pdf

Renate Motschnig, Michael Sedlmair, Svenja Schröder, Torsten Möller.
A team-approach to putting learner-centered principles to practice in a large course on Human-Computer Interaction.
In Proceedings of IEEE Frontiers in Education Conference (FIE), 2016.
pdf

Christina Niederer, Alexander Rind, Wolfgang Aigner, Julian Ausserhofer, Robert Gutounig, Michael Sedlmair.
Visual Exploration of Media Transparency for Data Journalists: Problem Characterization and Abstraction.
In Proceedings of Forschungforum der Östereichischen Fachhochschulen (FFH), 2016.
pdf

Dominik Sacha, Michael Sedlmair, Leishi Zhang, John Lee, Daniel Weiskopf, Stephen North, Daniel A. Keim.
Human-centered machine learning through interactive visualization: review and open challenges.
In Proceedings of European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2016.
pdf

Michaël Aupetit, Michael Sedlmair.
SepMe: 2002 New Visual Separation Measures.
In Proceedings of IEEE Pacific Visualization Symposium (PacificVis), 2016.
pdf

Hund, D. Böhm, W. Sturm, M. Sedlmair, T. Schreck, T. Ullrich, D. A. Keim, L. Majnaric, A. Holzinger.
Visual Analytics for Concept Exploration in Subspaces of Patient Groups: Making Sense of Complex Datasets with the Doctor-in-the-Loop.
Brain Informatics, 3(4): 233-247, 2016.
pdf, doi

Mohsen Kamalzadeh, Christoph Kralj, Torsten Möller, Michael Sedlmair.
TagFlip: Active Mobile Music Discovery with Social Tags.
In Proceedings of the 21st International Conference on Intelligent User Interfaces (IUI), 2016.
Best Student Paper Award.
pdf, supplemental material (zip), 30s video

2015

Thomas Torsney-Weir, Michael Sedlmair, Torsten Möller.
Decision making in uncertainty visualization.
In IEEE VIS Workshop on Visualization for Decision Making under Uncertainty (VDMU), 2015.
pdf

Michael Hund, Michael Behrisch, Ines Färber, Michael Sedlmair, Tobias Schreck, Thomas Seidl, Daniel A. Keim.
Subspace Nearest Neighbor Search - Problem Statement, Approaches, and Discussion.
In Proceedings of the Similarity Search and Applications Conference (SISAP).
pdf, bib

Michael Sedlmair, Michaël Aupetit
Data-driven Evaluation of Visual Quality Measures.
In Computer Graphics Forum (Proc. EuroVis 2015), 34(3): 201-210, 2015.
pdf

Svenja Simon, Sebastian Mittelstädt, Daniel A. Keim, Michael Sedlmair.
Bridging the Gap of Domain and Visualization Experts with a Liaison.
Proc. of Eurographics Conference on Visualization (EuroVis 2015, Short Paper), 2015.
pdf

2014

Matthew Brehmer, Michael Sedlmair, Stephen Ingram, Tamara Munzner
Visualizing Dimensionally-Reduced Data: Interviews with Analysts and a Characterization of Task Sequences.
In Proceedings of the VIS Workshop Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV), 2014.
pdf

Michael Sedlmair, Christoph Heinzl, Stefan Bruckner, Harald Piringer, Torsten Möller
Visual Parameter Space Analysis: A Conceptual Framework.
In IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis 2014), 20(12): 2161-2170, 2014.
pdf, supplemental material, 30s video

Thomas Mühlbacher, Harald Piringer, Samuel Gratzl, Michael Sedlmair, Marc Streit
Opening the Black Box: Strategies for Increased User Involvement in Existing Algorithm Implementations.
In IEEE Transactions on Visualization and Computer Graphics (Proc. VAST 2014), 20(12): 1643-1652, 2014.
pdf, 30s video

Petra Isenberg, Tobias Isenberg, Michael Sedlmair, Jian Chen, Torsten Möller
Toward a deeper understanding of Visualization through keyword analysis.
Technical Report, Inria Research Report Number 8580, August 2014.
pdf, TR page

2013

Miriah Meyer, Michael Sedlmair, P. Samuel Quinan, Tamara Munzner
The Nested Blocks and Guidelines Model.
In Journal of Information Visualization, Special Issue on Evaluation (BELIV), 14(3), 2015. Published online before print Dec, 2013.
pdf, paper page

Michael Sedlmair, Tamara Munzner, Melanie Tory
Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices.
In IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis 2013), 19(12): 2634-2643, 2013.
pdf, paper page

Tobias Isenberg, Petra Isenberg, Jian Chen, Michael Sedlmair, Torsten Möller
A Systematic Review on the Practice of Evaluating Visualization.
In IEEE Transactions on Visualization and Computer Graphics (Proc. SciVis 2013), 19(12): 2818-2827, 2013.
pdf, paper page

Steven Bergner, Michael Sedlmair, Torsten Möller, Sareh Nabi Abdolyousefi, Ahmed Saad
Paraglide: Interactive Parameter Space Partitioning for Computer Simulations
In IEEE Transactions on Visualization and Computer Graphics (TVCG), 19(9): 1499–1512, 2013.
pdf, bib

2012

Miriah Meyer, Michael Sedlmair, Tamara Munzner
The Four-Level Nested Model Revisited: Blocks and Guidelines
In Proceedings of the VisWeek Workshop Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV), 2012.
pdf, paper page

Michael Sedlmair, Miriah Meyer, Tamara Munzner
Design Study Methodology: Reflections from the Trenches and the Stacks
In IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis 2012), 18(12): 2431–2440, 2012.
Honorable Mention for Best Paper Award.
pdf, paper page

Michael Sedlmair, Annika Frank, Tamara Munzner, Andreas Butz
RelEx: Visualization for Actively Changing Overlay Network Specifications
In IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis 2012), 18(12): 2729–2738, 2012.
pdf, paper page

Michael Sedlmair, Matt Brehmer, Stephen Ingram, Tamara Munzner
Dimensionality Reduction in the Wild: Gaps and Guidance
UBC Computer Science Technical Report TR-2012-03, June 2012.
pdf, TR page

Michael Sedlmair, Andrada Tatu, Tamara Munzner, Melanie Tory
A Taxonomy of Visual Cluster Separation Factors
In Computer Graphics Forum (Proc. EuroVis 2012), 31(3): 1335-1344, 2012.
pdf, paper page

2011

Michael Sedlmair, Michael Schraut, Wolfgang Hintermaier
Visualisierung von Busdaten – Fehleranalyse im Forschungsprojekt AutobahnVis
In Journal of Automobil Elektronik, vol. 4: 20-23, 2011.
pdf, bib (german paper, online version available via www.all-electronics.de)

Michael Sedlmair, Petra Isenberg, Dominikus Baur, Andreas Butz
Information Visualization Evaluation in Large Companies: Challenges, Experiences and Recommendations
In Journal of Information Visualization, Special Issue on Evaluation (BELIV), 10(3): 248-266, 2011.
pdf, bib (pre-print, final version available via www.palgrave-journals.com)

Steven Bergner, Michael Sedlmair, Sareh Nabi Abdolyousefi, Ahmed Saad, Torsten Möller
Paraglide: Interactive Parameter Space Partitioning for Computer Simulations
ARXIV Technical Report arXiv:1110.5181, Oct 2011.
TR page

Michael Sedlmair, Petra Isenberg, Dominikus Baur, Michael Mauerer, Christian Pigorsch, Andreas Butz
Cardiogram: Visual Analytics for Automotive Engineers
In Proceedings of the 29th ACM International Conference on Human Factors in Computing Systems (CHI), 2011.
pdf, bib, video

2010

Michael Sedlmair
Visual Analysis of In-Car Communication Networks
PhD Thesis, Faculty of Mathematics, Computer Science and Statistics, University of Munich, 2010.
pdf, bib

Dominikus Baur, Frederik Seiffert, Michael Sedlmair, Sebastian Boring
The Streams of Our Lives: Visualizing Listening Histories in Context
In IEEE Transactions on Visualization and Computer Graphics (Proc. InfoVis 2010), 16(6): 1119-1128, 2010.
pdf, bib, video

Petra Isenberg, Michael Sedlmair, Dominikus Baur, Tobias Isenberg, Andreas Butz
Collaborative Visualization on Interactive Surfaces - CoVIS '09
Technical Report, LMU-MI-2010-2, April 2010, ISSN 1862-5207: Collaborative Visualization on Interactive Surface - CoVIS '09, Workshop at Visweek 2009
pdf, bib

Dominikus Baur, Michael Sedlmair, Raphael Wimmer, Yaxi Chen, Sara Streng, Sebastian Boring, Alexander De Luca, Andreas Butz
Trends in Information Visualization
Technical Report, LMU-MI-2010-1, April 2010, ISSN 1862-5207: Media Informatics Advanced Seminar, Winter Term 2008/2009.
pdf, bib

Michael Sedlmair, Petra Isenberg, Dominikus Baur, Andreas Butz
Evaluating Information Visualization in Large Companies: Challenges, Experiences and Recommendations
Proceedings of the CHI Workshop Beyond Time and Errors: Novel Evaluation Methods for Information Visualization (BELIV), 2010.
Best Paper Award.
pdf, bib

2009

Michael Sedlmair, Christian Berhold, Daniel Herrscher, Sebastian Boring, Andreas Butz
MostVis: An Interactive Visualization Supporting Automotive Engineers in MOST Catalog Exploration
In Proceedings of the 13th International Conference on Information Visualization (IV), 2009.
pdf, bib, video

Michael Sedlmair, Benjamin Kunze, Wolfgang Hintermaier, Andreas Butz
User-centered Development of a Visual Exploration System for In-Car Communication
In Proceedings of the 9th International Symposium on Smart Graphics (SG), 2009.
pdf, bib, video

Michael Sedlmair, Kerstin Ruhland, Fabian Hennecke, Andreas Butz, Susan Bioletti, Carol O'Sullivan
Towards the Big Picture: Enriching 3D Models with Information Visualisation and Vice Versa
In Proceedings of the 9th International Symposium on Smart Graphics (SG), 2009.
pdf, bib, video

Kerstin Ruhland, Michael Sedlmair, Susan Bioletti, Carol O'Sullivan
LibViz: Data Visualisation of the Old Library
In International Journal of Architectural Computing, 7(1): 177-192, 2009.
pdf, bib, video

2008

Michael Sedlmair, Dominikus Baur, Sebastian Boring, Petra Isenberg, Marko Jurmu, Andreas Butz
Requirements for a MDE System to Support Collaborative In-Car Communication Diagnostics
International CSCW Workshop Beyond the Laboratory: Supporting Authentic Collaboration with Multiple Displays, 2008.
pdf, bib

Kerstin Ruhland, Susan Bioletti, Michael Sedlmair, Carol O'Sullivan
LibViz: Data Visualisation of the old Library
In Proceedings of the 14th International Conference on Virtual Systems and Multimedia (VSMM), 2008.
Best Paper Award.
pdf, bib

Michael Sedlmair
MSCar: Enhancing Message Sequence Charts with Interactivity for Analysing (Automotive) Communication Sequences
In Proceedings of the 2nd International Workshop on the Layout of (Software) Engineering Diagrams (LED), 2008.
pdf, bib

Michael Sedlmair, Wolfgang Hintermaier, Konrad Stocker, Thortsen Büring, Andreas Butz
A Dual-View Visualization of In-Car Communication Processes
In Proceedings of the 12th International Conference on Information Visualization (IV), 2008.
pdf, bib, video

2007

Michael Sedlmair
Methoden der Informationsvisualisierung für Kommunikationsprozesse im Fahrzeug
Diplom Thesis, Faculty of Mathematics, Computer Science and Statistics, University of Munich, 2007.

Talks

Invited Talks, Capstones, Keynotes

Visualization: The human lens to data.
Invited talk at Qatar Computing Research Institute (QCRI), Doha, Qatar, Nov 2019.

Machine learning meets visualization.
Invited talk at 1st International Conference on Visualization and Computer-Human Interaction (VisCHI), Doha, Qatar, Nov2019.

Experiences from the trenches.
Keynote talk at 1st IEEE VIS workshop on EValuation of Interactive VisuAl Machine Learning systems (EVIVA-ML), Vancouver, Canada, Oct 2019.

Was ist KI?
Panel talk at Podiumsdiskussion zu “KI und Ethik” : Was darf Künstliche Intelligenz?, Stuttgart, Germany , Oct 2019.

Why visualization? On the relation of machine learning and visualization.
Keynote talk at Joint Workshop on Advances in Interpretable Machine Learning and
Artiﬁcial Intelligence / eXplainable Knowledge Discovery in Data Mining (AIMLAIXKDD), Würzburg, Germany, Sep 2019.

Why visualization? On the relation of machine learning and visualization.
Keynote talk at INFOS Summer Colloquium, Stuttgart, Germany, Jun 2019

Was ist KI?
Panel talk at Lange Nacht der KI, Stuttgart, Germany, Jul 2019.

Machine learning meets visualization.
Invited talk at University of Magdeburg, Germany, Jun2019.

Machine learning meets visualization.
Invited talk at INRIA, Saclay, France, May 2019.

On the relation of machine learning and visualization.
Invited talk at University of Tübingen, Germany, Apr 2019.

Machine learning and visualization.
A tag team for interactive data science Invited talk at TU Dortmund, Germany, Apr 2019.

Priming and anchoring eﬀects in visualization.
Paper talk at IEEE Information Visualization (InfoVis), Phoenix, AZ, USA, Oct 2017

Visualizing Data: Perception, Interaction, Computation.
Invited talk at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Science (CAS), Shenzhen, China, Jul 2017.
slides (pdf)

Visualization: The Human Lens to Data.
Invited talk at the Austrian Computer Science Day (ACSD), Vienna, Austria, Jun 2017.
slides (pdf), slides (keynote)

Visualization Design Studies.
Invited talk at Jacobs University, Bremen, Germany, May 2017.
slides (pdf), slides (keynote)

Human-centered Network Visualization.
Invited talk at Junge Statistik Austria, Vienna, Austria, Nov 2016.
slides (pdf)

Visualization: The Human Lens to Networks.
Invited talk at the LMU excellence workshop on "What's new in networks?", Center of Advanced Studies, Ludwig Maximilian University of Munich, Germany, Oct 2016.
slides (pdf)

Human-centered Methods in Visualization Research.
Keynote talk at the 3rd Workshop on Human-Oriented Formal Methods (HOFM), co-located with STAF 2016, Vienna, Austria, Jul 2016.
slides (keynote)

Human-centered Data Science
Invited talk at the Symposium on Selected Topics in Science and Technology, Technical University of Munich, Germany, Jan 2016.
slides (pdf), slides (keynote)

Visual Parameter Space Analysis.
Keynote talk at the Workshop for Machine Learning for Biomedicine, Graz University of Technology, Austria, Jan 2016.
slides (keynote)

Visualization: The Human Lens to Big Data.
Capstone talk at Predictive Analytics Conference, Vienna, Austria, Oct 2015.
slides (pdf)

On CS Undergrad Teaching and Visualization Research.
Invited talk at University of Konstanz, Germany, Aug 2015.
slides (pdf)

Visualization: The Human Lens to Big Data.
Invited talk at IDC Data Hub Conference, Vienna, Austria, May 2015.
slides (pdf)

Design Study Contributions: 7 contribution types for problem-driven visualization research.
Invited talk at the Data Analysis and Visualization group, University of Konstanz, Germany, Apr 2015.
slides (keynote)

Parameter Space Analysis: A different set of problems Visulization can help with.
Invited talk at the Data Analysis and Visualization group, University of Konstanz, Germany, Apr 2015.
slides (keynote)

Visual Tools for Understanding Multi-dimensional Parameter Spaces.
Invited talk at University of Leeds, Leeds, UK, Feb 2014.
slides (pdf)

Visual Data Analysis: Real Users, Real Data, Right Methods.
Invited talk at VRVis, Vienna, Austria, Oct 2013.
slides (pdf)

Visual Data Analysis: Real Users, Real Data, Right Methods.
Invited talk at City University, London, UK, Jul 2013.
slides (pdf)

A Taxonomy of Visual Cluster Separation Factors.
Invited talk at University of Konstanz, Konstanz, Germany, Jan 2012.
slides (pdf), slides (keynote)

Characterizing Dimensionally Reduced Data: Human Judgement vs. Quality Metrics.
Invited talk at University of British Columbia, Vancouver, Canada, Sept 2011.

Visual Analysis of In-Car Communication Networks.
Invited talk at Simon Fraser University, Burnaby, Canada, Oct 2010.

Methoden der Informationsvisualisierung für Kommunikationsnetze im Fahrzeug.
Invited talk at BMW Group, Forschungs-Kolloquium, Munich, Germany, Nov 2009.

Paper and Panel Talks

Priming and Anchoring Effects in Visualization.
Paper talk at IEEE Information Visualization (InfoVis), Phoenix, AZ, USA, Oct 2017.
slides (pdf), slides (keynote)

A Framework for Studying Biases in Visualization Research.
Paper talk at IEEE VIS Workshop on Dealing with Cognitive Biases in Visualisations (DECISIVe), Phoenix, AZ, USA, Oct 2017.
slides (pdf), slides (keynote)

Eye-tracking and Visualization Evaluation.
Panel talk at IEEE VIS Workshop on Eye Tracking and Visualization (ETVIS), Baltimore, MA, USA, Oct 2016.
slides (pdf), slides (keynote)

Design Study Contributions Come in Different Guises: Seven Guiding Scenarios.
Paper talk at IEEE VIS Workshop Beyond Time and Errors: Novel Evaluation Methods for Visualization (BELIV), Baltimore, MA, USA, Oct 2016.
slides (pdf), slides (keynote)

A Team-Approach to Putting Learner-Centered Principles to Practice in a Large Course on Human-Computer Interaction.
Paper talk at IEEE Frontiers in Education Conference (FIE), Erie, PA, USA, Oct 2016.
slides (pdf), slides (keynote)

Data-driven Evaluation of Visual Quality Measures.
Paper talk at Eurographics Conference on Visualization (EuroVis), Cagliari, Italy, May 2015.
slides (pdf), slides (keynote)

Visual Parameter Space Analysis: A Conceptual Framework.
Paper talk at IEEE Information Visualization (InfoVis), Paris, France, Nov 2014.
slides (pdf), slides (keynote/zip)

Empirical Guidance on Scatterplot and Dimension Reduction Technique Choices.
Paper talk at IEEE Information Visualization (InfoVis), Atlanta, GA, USA, Oct 2013.
paper page (with slides)

Design Study Methodology: Reflections from the Trenches and the Stacks.
Paper talk at IEEE Information Visualization (InfoVis), Seattle, WA, USA, Oct 2012.
paper page (with slides)

RelEx: A Design Study of Visual Network Analysis in the Automotive Domain.
Paper talk at IEEE Information Visualization (InfoVis), Seattle, WA, USA, Oct 2012.
paper page (with slides)

A Taxonomy of Visual Cluster Separation Factors.
Paper talk at Eurographics Conference on Visualization (EuroVis), Vienna, Austria, Jun 2012.
paper page (with slides)

Cardiogram: Visual Analytics for Automotive Engineers.
Paper talk at ACM Conference on Human Factors in Computing Systems (CHI), Vancouver, Canada, May 2011.
slides (pdf)

Evaluating Information Visualization in Large Companies: Challenges, Experiences and Recommendations.
Paper talk at CHI Workshop Beyond Time and Errors: Novel Evaluation Methods for Information Visualization (BELIV), Atlanta, GA, USA, Apr 2010.

MostVis: An Interactive Visualization Supporting Automotive Engineers in MOST Catalog Exploration.
Paper talk at the International Conference Information Visualisation (IV), Barcelona, Spain, Jul 2009.

User-centered Development of a Visual Exploration System for In-Car Communication.
Paper talk at Symposium on Smart Graphics, Salamanca, Spain, May 2009.

Towards the Big Picture: Enriching 3D Models with Information Visualisation and Vice Versa.
Paper talk at Symposium on Smart Graphics, Salamanca, Spain, May 2009.

Requirements for a MDE System to Support Collaborative In-car Communication Diagnostics.
Paper talk at CSCW Workshop Beyond the Laboratory: Supporting Authentic Collaboration with Multiple Displays, San Diego, CA, USA, Nov 2008.

MSCar: Enhancing Message Sequence Charts with Interactivity for Analysing (Automotive) Communication Sequences.
Paper talk at VL/HCC Workshop on the Layout of (Software) Engineering Diagrams, Herrsching, Germany, Sep 2008.

A Dual-view Visualization of In-car Communication Processes.
Paper talk at the International Conference Information Visualisation (IV), London, GB, Jul 2008.

Other

Usable Interfaces: How to deal with complexity?
Presentation at Improve Industry Workshop, Vienna, Austria, Apr 2017.
slides (pdf), slides (keynote)

Wir, das HCI-Team, danken den Organisatoren und ...
Presentation for Receiving the UNIVIE Teaching Award, Vienna, Austria, Jun 2016.
slides (pdf, in German)

Dissertation -- Visual Analysis of In-Car Communication Networks.
PhD Defensio, Munich, Germany, Oct 2010.
slides (pdf), slides (keynote)

Michael Sedlmair

Contact

Publications

2023

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

2022

BibTeX

BibTeX

BibTeX

Link

BibTeX

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

BibTeX

2021

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX

Link

BibTeX