DSpace Repository :: Browsing by Author "Keim, Daniel A."

Browsing by Author "Keim, Daniel A."

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Augmenting Digital Sheet Music through Visual Analytics
(© 2022 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd, 2022) Miller, Matthias; Fürst, Daniel; Hauptmann, Hanna; Keim, Daniel A.; El‐Assady, Mennatallah; Hauser, Helwig and Alliez, Pierre
Music analysis tasks, such as structure identification and modulation detection, are tedious when performed manually due to the complexity of the common music notation (CMN). Fully automated analysis instead misses human intuition about relevance. Existing approaches use abstract data‐driven visualizations to assist music analysis but lack a suitable connection to the CMN. Therefore, music analysts often prefer to remain in their familiar context. Our approach enhances the traditional analysis workflow by complementing CMN with interactive visualization entities as minimally intrusive augmentations. Gradual step‐wise transitions empower analysts to retrace and comprehend the relationship between the CMN and abstract data representations. We leverage glyph‐based visualizations for harmony, rhythm and melody to demonstrate our technique's applicability. Design‐driven visual query filters enable analysts to investigate statistical and semantic patterns on various abstraction levels. We conducted pair analytics sessions with 16 participants of different proficiency levels to gather qualitative feedback about the intuitiveness, traceability and understandability of our approach. The results show that MusicVis supports music analysts in getting new insights about feature characteristics while increasing their engagement and willingness to explore.
CommAID: Visual Analytics for Communication Analysis through Interactive Dynamics Modeling
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Fischer, Maximilian T.; Seebacher, Daniel; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
Communication consists of both meta-information as well as content. Currently, the automated analysis of such data often focuses either on the network aspects via social network analysis or on the content, utilizing methods from text-mining. However, the first category of approaches does not leverage the rich content information, while the latter ignores the conversation environment and the temporal evolution, as evident in the meta-information. In contradiction to communication research, which stresses the importance of a holistic approach, both aspects are rarely applied simultaneously, and consequently, their combination has not yet received enough attention in automated analysis systems. In this work, we aim to address this challenge by discussing the difficulties and design decisions of such a path as well as contribute CommAID, a blueprint for a holistic strategy to communication analysis. It features an integrated visual analytics design to analyze communication networks through dynamics modeling, semantic pattern retrieval, and a user-adaptable and problem-specific machine learning-based retrieval system. An interactive multi-level matrix-based visualization facilitates a focused analysis of both network and content using inline visuals supporting cross-checks and reducing context switches. We evaluate our approach in both a case study and through formative evaluation with eight law enforcement experts using a real-world communication corpus. Results show that our solution surpasses existing techniques in terms of integration level and applicability. With this contribution, we aim to pave the path for a more holistic approach to communication analysis.
Comparative Analysis with Heightmaps in Virtual Reality Environments
(The Eurographics Association, 2019) Kraus, Matthias; Buchmüller, Juri; Schweitzer, Daniel; Keim, Daniel A.; Fuchs, Johannes; Madeiras Pereira, João and Raidou, Renata Georgia
3D heightmaps can be considered as an extension of heatmaps using the third dimension to encode the respective value by height, often in addition to encoding it by color. In contrast to 2D heatmaps, 3D heightmaps allow a superposition without aggregation. However, they also have the general disadvantages of 3D visualizations, such as occlusion and perceptual distortion. Previous research has revealed various advantages of stereoscopic displays and virtual reality (VR) in the context of 3D visualizations, for example, concerning memorization, depth perception, and collaboration. In this paper, we present a novel technique to compare heightmaps in VR by introducing a multi-layer approach of stacked heightmaps. We demonstrate the applicability and usefulness of our method by means of a use case on comparative crime data analysis.
CorpusVis: Visual Analysis of Digital Sheet Music Collections
(The Eurographics Association and John Wiley & Sons Ltd., 2022) Miller, Matthias; Rauscher, Julius; Keim, Daniel A.; El-Assady, Mennatallah; Borgo, Rita; Marai, G. Elisabeta; Schreck, Tobias
Manually investigating sheet music collections is challenging for music analysts due to the magnitude and complexity of underlying features, structures, and contextual information. However, applying sophisticated algorithmic methods would require advanced technical expertise that analysts do not necessarily have. Bridging this gap, we contribute CorpusVis, an interactive visual workspace, enabling scalable and multi-faceted analysis. Our proposed visual analytics dashboard provides access to computational methods, generating varying perspectives on the same data. The proposed application uses metadata including composers, type, epoch, and low-level features, such as pitch, melody, and rhythm. To evaluate our approach, we conducted a pair-analytics study with nine participants. The qualitative results show that CorpusVis supports users in performing exploratory and confirmatory analysis, leading them to new insights and findings. In addition, based on three exemplary workflows, we demonstrate how to apply our approach to different tasks, such as exploring musical features or comparing composers.
Evaluating Reordering Strategies for Cluster Identification in Parallel Coordinates
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Blumenschein, Michael; Zhang, Xuan; Pomerenke, David; Keim, Daniel A.; Fuchs, Johannes; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
The ability to perceive patterns in parallel coordinates plots (PCPs) is heavily influenced by the ordering of the dimensions. While the community has proposed over 30 automatic ordering strategies, we still lack empirical guidance for choosing an appropriate strategy for a given task. In this paper, we first propose a classification of tasks and patterns and analyze which PCP reordering strategies help in detecting them. Based on our classification, we then conduct an empirical user study with 31 participants to evaluate reordering strategies for cluster identification tasks. We particularly measure time, identification quality, and the users' confidence for two different strategies using both synthetic and real-world datasets. Our results show that, somewhat unexpectedly, participants tend to focus on dissimilar rather than similar dimension pairs when detecting clusters, and are more confident in their answers. This is especially true when increasing the amount of clutter in the data. As a result of these findings, we propose a new reordering strategy based on the dissimilarity of neighboring dimension pairs.
Learning and Teaching in Co-Adaptive Guidance for Mixed-Initiative Visual Analytics
(The Eurographics Association, 2020) Sperrle, Fabian; Jeitler, Astrik; Bernard, Jürgen; Keim, Daniel A.; El-Assady, Mennatallah; Turkay, Cagatay and Vrotsou, Katerina
Guidance processes in visual analytics applications often lack adaptivity. In this position paper, we contribute the concept of co-adaptive guidance, building on the principles of initiation and adaptation. We argue that both the user and the system adapt their data-, task- and user/system-models over time. Based on these principles, we propose reasoning about the guidance design space through introducing the concepts of learning and teaching that complement the existing dimension of implicit and explicit guidance, thus, deriving the four guidance dynamics user-teaching, system-teaching, user-learning, and system-learning. Finally, we classify current guidance approaches according to the dynamics, demonstrating their applicability to co-adaptive guidance.
ModelSpeX: Model Specification Using Explainable Artificial Intelligence Methods
(The Eurographics Association, 2020) Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.; Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko
Explainable artificial intelligence (XAI) methods aim to reveal the non-transparent decision-making mechanisms of black-box models. The evaluation of insight generated by such XAI methods remains challenging as the applied techniques depend on many factors (e.g., parameters and human interpretation). We propose ModelSpeX, a visual analytics workflow to interactively extract human-centered rule-sets to generate model specifications from black-box models (e.g., neural networks). The workflow enables to reason about the underlying problem, to extract decision rule sets, and to evaluate the suitability of the model for a particular task. An exemplary usage scenario walks an analyst trough the steps of the workflow to show the applicability.
MotionGlyphs: Visual Abstraction of Spatio-Temporal Networks in Collective Animal Behavior
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Cakmak, Eren; Schäfer, Hanna; Buchmüller, Juri; Fuchs, Johannes; Schreck, Tobias; Jordan, Alex; Keim, Daniel A.; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Domain experts for collective animal behavior analyze relationships between single animal movers and groups of animals over time and space to detect emergent group properties. A common way to interpret this type of data is to visualize it as a spatio-temporal network. Collective behavior data sets are often large, and may hence result in dense and highly connected node-link diagrams, resulting in issues of node-overlap and edge clutter. In this design study, in an iterative design process, we developed glyphs as a design for seamlessly encoding relationships and movement characteristics of a single mover or clusters of movers. Based on these glyph designs, we developed a visual exploration prototype, MotionGlyphs, that supports domain experts in interactively filtering, clustering, and animating spatio-temporal networks for collective animal behavior analysis. By means of an expert evaluation, we show how MotionGlyphs supports important tasks and analysis goals of our domain experts, and we give evidence of the usefulness for analyzing spatio-temporal networks of collective animal behavior.
Moving Together: Towards a Formalization of Collective Movement
(The Eurographics Association, 2019) Buchmüller, Juri; Cakmak, Eren; Andrienko, Natalia; Andrienko, Gennady; Jolles, Jolle W.; Keim, Daniel A.; Landesberger, Tatiana von and Turkay, Cagatay
While conventional applications for spatiotemporal datasets mostly focus on the relation between movers and environment, research questions in the analysis of collective movement typically focus more on relationships and dynamics between the moving entities themselves. Instead of concentrating on origin, destination and the way in between, this inter-mover perspective on spatiotemporal data allows to explain how moving groups are coordinating. Yet, only few visualization and Visual Analytics approaches focus on the relationships between movers. To illuminate this research gap, we propose initial steps towards a comprehensive formalization of coordination in collective movement based on temporal autocorrelation of distance matrices derived from basic movement characteristics. We exemplify how patterns can be encoded using autocorrelation cubes and outline the next steps towards an exhaustive formalization of coordination patterns.
ParSetgnostics: Quality Metrics for Parallel Sets
(The Eurographics Association and John Wiley & Sons Ltd., 2021) Dennig, Frederik L.; Fischer, Maximilian T.; Blumenschein, Michael; Fuchs, Johannes; Keim, Daniel A.; Dimara, Evanthia; Borgo, Rita and Marai, G. Elisabeta and Landesberger, Tatiana von
While there are many visualization techniques for exploring numeric data, only a few work with categorical data. One prominent example is Parallel Sets, showing data frequencies instead of data points - analogous to parallel coordinates for numerical data. As nominal data does not have an intrinsic order, the design of Parallel Sets is sensitive to visual clutter due to overlaps, crossings, and subdivision of ribbons hindering readability and pattern detection. In this paper, we propose a set of quality metrics, called ParSetgnostics (Parallel Sets diagnostics), which aim to improve Parallel Sets by reducing clutter. These quality metrics quantify important properties of Parallel Sets such as overlap, orthogonality, ribbon width variance, and mutual information to optimize the category and dimension ordering. By conducting a systematic correlation analysis between the individual metrics, we ensure their distinctiveness. Further, we evaluate the clutter reduction effect of ParSetgnostics by reconstructing six datasets from previous publications using Parallel Sets measuring and comparing their respective properties. Our results show that ParSetgostics facilitates multi-dimensional analysis of categorical data by automatically providing optimized Parallel Set designs with a clutter reduction of up to 81% compared to the originally proposed Parallel Sets visualizations.
SpatialRugs: Enhancing Spatial Awareness of Movement in Dense Pixel Visualizations
(The Eurographics Association, 2020) Buchmüller, Juri F.; Schlegel, Udo; Cakmak, Eren; Keim, Daniel A.; Dimara, Evanthia; Turkay, Cagatay and Vrotsou, Katerina
Compact visual summaries of spatio-temporal movement data often strive to express accurate positions of movers. We present SpatialRugs, a technique to enhance the spatial awareness of movements in dense pixel visualizations. SpatialRugs apply 2D colormaps to visualize location mapped to a juxtaposed display. We explore the effect of various colormaps discussing perceptual limitations and introduce a custom color-smoothing method to mitigate distorted patterns of collective movement behavior.
SurgeryCuts: Embedding Additional Information in Maps without Occluding Features
(The Eurographics Association and John Wiley & Sons Ltd., 2019) Angelini, Marco; Buchmüller, Juri; Keim, Daniel A.; Meschenmoser, Philipp; Santucci, Giuseppe; Gleicher, Michael and Viola, Ivan and Leitte, Heike
Visualizing contextual information to a map often comes at the expense of overplotting issues. Especially for use cases with relevant map features in the immediate vicinity of an information to add, occlusion of the relevant map context should be avoided. We present SurgeryCuts, a map manipulation technique for the creation of additional canvas area for contextual visualizations on maps. SurgeryCuts is occlusion-free and does not shift, zoom or alter the map viewport. Instead, relevant parts of the map can be cut apart. The affected area is controlledly distorted using a parameterizable warping function fading out the map distortion depending on the distance to the cut. We define extended metrics for our approach and compare to related approaches. As well, we demonstrate the applicability of our approach at the example of tangible use cases and a comparative user study.
v-plots: Designing Hybrid Charts for the Comparative Analysis of Data Distributions
(The Eurographics Association and John Wiley & Sons Ltd., 2020) Blumenschein, Michael; Debbeler, Luka J.; Lages, Nadine C.; Renner, Britta; Keim, Daniel A.; El-Assady, Mennatallah; Viola, Ivan and Gleicher, Michael and Landesberger von Antburg, Tatiana
Comparing data distributions is a core focus in descriptive statistics, and part of most data analysis processes across disciplines. In particular, comparing distributions entails numerous tasks, ranging from identifying global distribution properties, comparing aggregated statistics (e.g., mean values), to the local inspection of single cases. While various specialized visualizations have been proposed (e.g., box plots, histograms, or violin plots), they are not usually designed to support more than a few tasks, unless they are combined. In this paper, we present the v-plot designer; a technique for authoring custom hybrid charts, combining mirrored bar charts, difference encodings, and violin-style plots. v-plots are customizable and enable the simultaneous comparison of data distributions on global, local, and aggregation levels. Our system design is grounded in an expert survey that compares and evaluates 20 common visualization techniques to derive guidelines for the task-driven selection of appropriate visualizations. This knowledge externalization step allowed us to develop a guiding wizard that can tailor v-plots to individual tasks and particular distribution properties. Finally, we confirm the usefulness of our system design and the userguiding process by measuring the fitness for purpose and applicability in a second study with four domain and statistic experts.
ViNNPruner: Visual Interactive Pruning for Deep Learning
(The Eurographics Association, 2022) Schlegel, Udo; Schiegg, Samuel; Keim, Daniel A.; Archambault, Daniel; Nabney, Ian; Peltonen, Jaakko
Neural networks grow vastly in size to tackle more sophisticated tasks. In many cases, such large networks are not deployable on particular hardware and need to be reduced in size. Pruning techniques help to shrink deep neural networks to smaller sizes by only decreasing their performance as little as possible. However, such pruning algorithms are often hard to understand by applying them and do not include domain knowledge which can potentially be bad for user goals. We propose ViNNPruner, a visual interactive pruning application that implements state-of-the-art pruning algorithms and the option for users to do manual pruning based on their knowledge. We show how the application facilitates gaining insights into automatic pruning algorithms and semi-automatically pruning oversized networks to make them more efficient using interactive visualizations.
Visual Analytics of Conversational Dynamics
(The Eurographics Association, 2019) Seebacher, Daniel; Fischer, Maximilian T.; Sevastjanova, Rita; Keim, Daniel A.; El-Assady, Mennatallah; Landesberger, Tatiana von and Turkay, Cagatay
Large-scale interaction networks of human communication are often modeled as complex graph structures, obscuring temporal patterns within individual conversations. To facilitate the understanding of such conversational dynamics, episodes with low or high communication activity as well as breaks in communication need to be detected to enable the identification of temporal interaction patterns. Traditional episode detection approaches are highly dependent on the choice of parameters, such as window-size or binning-resolution. In this paper, we present a novel technique for the identification of relevant episodes in bi-directional interaction sequences from abstract communication networks. We model communication as a continuous density function, allowing for a more robust segmentation into individual episodes and estimation of communication volume. Additionally, we define a tailored feature set to characterize conversational dynamics and enable a user-steered classification of communication behavior. We apply our technique to a real-world corpus of email data from a large European research institution. The results show that our technique allows users to effectively define, identify, and analyze relevant communication episodes.
Visual Exploration of Preference-based Routes in Ski Resorts
(The Eurographics Association, 2022) Rauscher, Julius; Miller, Matthias; Keim, Daniel A.; Krone, Michael; Lenti, Simone; Schmidt, Johanna
Ski resorts exhibit a variety of available pistes and lifts, to which every skier has intrinsic preferences. While novices tend to favor easy pistes, experts might opt for more advanced pistes. In large resorts, the vast possibilities render manual, optimized routing according to specific piste and lift preferences extremely tedious. So far, existing visualizations of ski resorts lack these routing capabilities.We present a visual analytics interface that allows the user to find an optimal route between arbitrary locations in a ski resort according to individual personal preferences. Furthermore, we encode steepness information along the pistes to expose segments that deviate from the difficulty classification and thus are incompatible with the given user preferences.
VMV 2022: Frontmatter
(The Eurographics Association, 2022) Bender, Jan; Botsch, Mario; Keim, Daniel A.; Bender, Jan; Botsch, Mario; Keim, Daniel A.

Browsing by Author "Keim, Daniel A."

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