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  1. Article ; Online: What Features Can Tell Us about Shape.

    Schreck, Tobias

    IEEE computer graphics and applications

    2017  Volume 37, Issue 3, Page(s) 82–87

    Abstract: 3D shape representations are essential when storing shape information for natural and manmade objects. To make use of shape information, many applications require shape-processing functionality, such as for search, annotation, classification, modeling, ... ...

    Abstract 3D shape representations are essential when storing shape information for natural and manmade objects. To make use of shape information, many applications require shape-processing functionality, such as for search, annotation, classification, modeling, restoration, or collection exploration. This article discusses feature-based approaches and how they can support such functionality.
    Language English
    Publishing date 2017-07-31
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1558-1756
    ISSN (online) 1558-1756
    DOI 10.1109/MCG.2017.41
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Visual Exploration and Analysis of Simulation and Testing Data in Motor Engineering.

    Louis, Patrick / Cibulski, Lena / Suschnigg, Josef / Marth, Edmund / Mitterhofer, Hubert / Kohlhammer, Jorn / Schreck, Tobias / Mutlu, Belgin

    IEEE computer graphics and applications

    2024  Volume PP

    Abstract: End-of-line tests and defect detection are vital for ensuring the reliability of electric motors. However, automated defect detection methods, e.g., data-driven approaches, face challenges due to the limited availability of real data from failed motors. ... ...

    Abstract End-of-line tests and defect detection are vital for ensuring the reliability of electric motors. However, automated defect detection methods, e.g., data-driven approaches, face challenges due to the limited availability of real data from failed motors. Simulated data, though beneficial, lacks the complexity of real motors, impacting the performance of these methods when applied to actual observations. To tackle this challenge, we introduce a visual analysis tool designed to facilitate the analysis of measured and simulated data, presented in the form of time series data. This tool helps identify domain-invariant features and evaluate simulation data accuracy, assisting in selecting training data for reliable automated defect detection in real-world scenarios. The main contribution of this work is a design proposal based on visual design principles, specifically tailored to address the unique requirements of electric motor professionals. The visual design is validated by findings from a think-aloud study with specialized engineers.
    Language English
    Publishing date 2024-04-24
    Publishing country United States
    Document type Journal Article
    ISSN 1558-1756
    ISSN (online) 1558-1756
    DOI 10.1109/MCG.2024.3392969
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  3. Article ; Online: ManuKnowVis: How to Support Different User Groups in Contextualizing and Leveraging Knowledge Repositories.

    Eirich, Joscha / Jackle, Dominik / Sedlmair, Michael / Wehner, Christoph / Schmid, Ute / Bernard, Jurgen / Schreck, Tobias

    IEEE transactions on visualization and computer graphics

    2023  Volume 29, Issue 8, Page(s) 3441–3457

    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 ...

    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.
    Language English
    Publishing date 2023-06-29
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2023.3279857
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  4. Article ; Online: Large-Scale Evaluation of Topic Models and Dimensionality Reduction Methods for 2D Text Spatialization.

    Atzberger, Daniel / Cech, Tim / Trapp, Matthias / Richter, Rico / Scheibel, Willy / Dollner, Jurgen / Schreck, Tobias

    IEEE transactions on visualization and computer graphics

    2023  Volume 30, Issue 1, Page(s) 902–912

    Abstract: Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora ... ...

    Abstract Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corpora as two-dimensional scatter plots, reflecting semantic similarity between the documents and supporting corpus analysis. Although the choice of the topic model, the dimensionality reduction, and their underlying hyperparameters significantly impact the resulting layout, it is unknown which particular combinations result in high-quality layouts with respect to accuracy and perception metrics. To investigate the effectiveness of topic models and dimensionality reduction methods for the spatialization of corpora as two-dimensional scatter plots (or basis for landscape-type visualizations), we present a large-scale, benchmark-based computational evaluation. Our evaluation consists of (1) a set of corpora, (2) a set of layout algorithms that are combinations of topic models and dimensionality reductions, and (3) quality metrics for quantifying the resulting layout. The corpora are given as document-term matrices, and each document is assigned to a thematic class. The chosen metrics quantify the preservation of local and global properties and the perceptual effectiveness of the two-dimensional scatter plots. By evaluating the benchmark on a computing cluster, we derived a multivariate dataset with over 45 000 individual layouts and corresponding quality metrics. Based on the results, we propose guidelines for the effective design of text spatializations that are based on topic models and dimensionality reductions. As a main result, we show that interpretable topic models are beneficial for capturing the structure of text corpora. We furthermore recommend the use of t-SNE as a subsequent dimensionality reduction.
    Language English
    Publishing date 2023-12-25
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2023.3326569
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  5. Article ; Online: Investigating the Sketchplan: A Novel Way of Identifying Tactical Behavior in Massive Soccer Datasets.

    Seebacher, Daniel / Polk, Tom / Janetzko, Halldor / Keim, Daniel A / Schreck, Tobias / Stein, Manuel

    IEEE transactions on visualization and computer graphics

    2023  Volume 29, Issue 4, Page(s) 1920–1936

    Abstract: Coaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. Existing approaches in this domain typically rely on manual video analysis and formation ... ...

    Abstract Coaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. Existing approaches in this domain typically rely on manual video analysis and formation discussion using whiteboards; or expert systems that rely on state-of-the-art video and trajectory visualization techniques and advanced user interaction. We bridge the gap between these approaches by contributing a light-weight, simplified interaction and visualization system, which we conceptualized in an iterative design study with the coaching team of a European first league soccer team. Our approach is walk-up usable by all domain stakeholders, and at the same time, can leverage advanced data retrieval and analysis techniques: a virtual magnetic tactic-board. Users place and move digital magnets on a virtual tactic-board, and these interactions get translated to spatio-temporal queries, used to retrieve relevant situations from massive team movement data. Despite such seemingly imprecise query input, our approach is highly usable, supports quick user exploration, and retrieval of relevant results via query relaxation. Appropriate simplified result visualization supports in-depth analyses to explore team behavior, such as formation detection, movement analysis, and what-if analysis. We evaluated our approach with several experts from European first league soccer clubs. The results show that our approach makes the complex analytical processes needed for the identification of tactical behavior directly accessible to domain experts for the first time, demonstrating our support of coaches in preparation for future encounters.
    MeSH term(s) Soccer ; Athletic Performance ; Computer Graphics ; Movement ; Walking
    Language English
    Publishing date 2023-02-28
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2021.3134814
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  6. Article ; Online: Multiscale Snapshots: Visual Analysis of Temporal Summaries in Dynamic Graphs.

    Cakmak, Eren / Schlegel, Udo / Jackle, Dominik / Keim, Daniel / Schreck, Tobias

    IEEE transactions on visualization and computer graphics

    2021  Volume 27, Issue 2, Page(s) 517–527

    Abstract: The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively ... ...

    Abstract The overview-driven visual analysis of large-scale dynamic graphs poses a major challenge. We propose Multiscale Snapshots, a visual analytics approach to analyze temporal summaries of dynamic graphs at multiple temporal scales. First, we recursively generate temporal summaries to abstract overlapping sequences of graphs into compact snapshots. Second, we apply graph embeddings to the snapshots to learn low-dimensional representations of each sequence of graphs to speed up specific analytical tasks (e.g., similarity search). Third, we visualize the evolving data from a coarse to fine-granular snapshots to semi-automatically analyze temporal states, trends, and outliers. The approach enables us to discover similar temporal summaries (e.g., reoccurring states), reduces the temporal data to speed up automatic analysis, and to explore both structural and temporal properties of a dynamic graph. We demonstrate the usefulness of our approach by a quantitative evaluation and the application to a real-world dataset.
    Language English
    Publishing date 2021-01-28
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2020.3030398
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  7. Article ; Online: ManEx: The Visual Analysis of Measurements for the Assessment of Errors in Electrical Engines.

    Eirich, Joscha / Koutroulis, Georgios / Mutlu, Belgin / Jackle, Dominik / Kern, Roman / Schreck, Tobias / Bernard, Jurgen

    IEEE computer graphics and applications

    2022  Volume 42, Issue 2, Page(s) 68–80

    Abstract: Electrical engines are a key technology all automotive manufacturers must master to stay competitive. Engineers need to analyze an overwhelming number of engine measurements to improve the manufacturing for this technology. They are hindered in the task ... ...

    Abstract Electrical engines are a key technology all automotive manufacturers must master to stay competitive. Engineers need to analyze an overwhelming number of engine measurements to improve the manufacturing for this technology. They are hindered in the task of analyzing large numbers of engines, however, by the following challenges: 1) Engines comprise a complex hierarchical structure of subcomponents. 2) Locating the cause of errors along manufacturing processes is a difficult procedure. 3) Large numbers of heterogeneous measurements impair the ability to explain errors in engines. We address these challenges in a design study with automotive engineers and by developing the visual analytics system Manufacturing Explorer (ManEx), which provides interactive interfaces to analyze measurements of engines across the manufacturing process. ManEx was validated by five experts. Our results suggest high usability and usefulness scores and the improvement of a real-world manufacturing process. Specifically, with ManEx, experts reduced scraped parts by over 3%.
    Language English
    Publishing date 2022-04-13
    Publishing country United States
    Document type Journal Article
    ISSN 1558-1756
    ISSN (online) 1558-1756
    DOI 10.1109/MCG.2022.3155306
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  8. Article ; Online: HetVis: A Visual Analysis Approach for Identifying Data Heterogeneity in Horizontal Federated Learning.

    Wang, Xumeng / Chen, Wei / Xia, Jiazhi / Wen, Zhen / Zhu, Rongchen / Schreck, Tobias

    IEEE transactions on visualization and computer graphics

    2022  Volume 29, Issue 1, Page(s) 310–319

    Abstract: Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity among clients is one of the major concerns. However, due to the security ... ...

    Abstract Horizontal federated learning (HFL) enables distributed clients to train a shared model and keep their data privacy. In training high-quality HFL models, the data heterogeneity among clients is one of the major concerns. However, due to the security issue and the complexity of deep learning models, it is challenging to investigate data heterogeneity across different clients. To address this issue, based on a requirement analysis we developed a visual analytics tool, HetVis, for participating clients to explore data heterogeneity. We identify data heterogeneity through comparing prediction behaviors of the global federated model and the stand-alone model trained with local data. Then, a context-aware clustering of the inconsistent records is done, to provide a summary of data heterogeneity. Combining with the proposed comparison techniques, we develop a novel set of visualizations to identify heterogeneity issues in HFL. We designed three case studies to introduce how HetVis can assist client analysts in understanding different types of heterogeneity issues. Expert reviews and a comparative study demonstrate the effectiveness of HetVis.
    Language English
    Publishing date 2022-12-16
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2022.3209347
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  9. Article ; Online: Multiscale Visualization: A Structured Literature Analysis.

    Cakmak, Eren / Jackle, Dominik / Schreck, Tobias / Keim, Daniel A / Fuchs, Johannes

    IEEE transactions on visualization and computer graphics

    2022  Volume 28, Issue 12, Page(s) 4918–4929

    Abstract: Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations ... ...

    Abstract Multiscale visualizations are typically used to analyze multiscale processes and data in various application domains, such as the visual exploration of hierarchical genome structures in molecular biology. However, creating such multiscale visualizations remains challenging due to the plethora of existing work and the expression ambiguity in visualization research. Up to today, there has been little work to compare and categorize multiscale visualizations to understand their design practices. In this article, we present a structured literature analysis to provide an overview of common design practices in multiscale visualization research. We systematically reviewed and categorized 122 published journal or conference articles between 1995 and 2020. We organized the reviewed articles in a taxonomy that reveals common design factors. Researchers and practitioners can use our taxonomy to explore existing work to create new multiscale navigation and visualization techniques. Based on the reviewed articles, we examine research trends and highlight open research challenges.
    MeSH term(s) Computer Graphics
    Language English
    Publishing date 2022-10-26
    Publishing country United States
    Document type Systematic Review ; Journal Article ; Research Support, Non-U.S. Gov't
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2021.3109387
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  10. Article ; Online: Visualizing Large-Scale Spatial Time Series with GeoChron.

    Deng, Zikun / Chen, Shifu / Schreck, Tobias / Deng, Dazhen / Tang, Tan / Xu, Mingliang / Weng, Di / Wu, Yingcai

    IEEE transactions on visualization and computer graphics

    2023  Volume 30, Issue 1, Page(s) 1194–1204

    Abstract: In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series ... ...

    Abstract In geo-related fields such as urban informatics, atmospheric science, and geography, large-scale spatial time (ST) series (i.e., geo-referred time series) are collected for monitoring and understanding important spatiotemporal phenomena. ST series visualization is an effective means of understanding the data and reviewing spatiotemporal phenomena, which is a prerequisite for in-depth data analysis. However, visualizing these series is challenging due to their large scales, inherent dynamics, and spatiotemporal nature. In this study, we introduce the notion of patterns of evolution in ST series. Each evolution pattern is characterized by 1) a set of ST series that are close in space and 2) a time period when the trends of these ST series are correlated. We then leverage Storyline techniques by considering an analogy between evolution patterns and sessions, and finally design a novel visualization called GeoChron, which is capable of visualizing large-scale ST series in an evolution pattern-aware and narrative-preserving manner. GeoChron includes a mining framework to extract evolution patterns and two-level visualizations to enhance its visual scalability. We evaluate GeoChron with two case studies, an informal user study, an ablation study, parameter analysis, and running time analysis.
    Language English
    Publishing date 2023-12-25
    Publishing country United States
    Document type Journal Article
    ISSN 1941-0506
    ISSN (online) 1941-0506
    DOI 10.1109/TVCG.2023.3327162
    Database MEDical Literature Analysis and Retrieval System OnLINE

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