Browsing by Author "Burch, Michael"

Now showing 1 - 4 of 4
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    Clustering for Stacked Edge Splatting
    (The Eurographics Association, 2018) Abdelaal, Moataz; Hlawatsch, Marcel; Burch, Michael; Weiskopf, Daniel; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    We present a time-scalable approach for visualizing dynamic graphs. By adopting bipartite graph layouts known from parallel edge splatting, individual graphs are horizontally stacked by drawing partial edges, leading to stacked edge splatting. This allows us to uncover the temporal patterns together with achieving time-scalability. To preserve the graph structural information, we introduce the representative graph where edges are aggregated and drawn at full length. The representative graph is then placed on the top of the last graph in the (sub)sequence. This allows us to obtain detailed information about the partial edges by tracing them back to the representative graph. We apply sequential temporal clustering to obtain an overview of different temporal phases of the graph sequence together with the corresponding structure for each phase. We demonstrate the effectiveness of our approach by using real-world datasets.
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    Identifying Similar Eye Movement Patterns with t-SNE
    (The Eurographics Association, 2018) Burch, Michael; Beck, Fabian and Dachsbacher, Carsten and Sadlo, Filip
    In this paper we describe an approach based on the t-distributed stochastic neighbor embedding (t-SNE) focusing on projecting high-dimensional eye movement data to two dimensions. The lower-dimensional data is then represented as scatterplots reflecting the local structure of the high-dimensional eye movement data and hence, providing a strategy to identify similar eye movement patterns. The scatterplots can be used as means to interact with and to further annotate and analyze the data for additional properties focusing on space, time, or participants. Since t-SNE oftentimes produces groups of data points mapped to and overplotted in small scatterplot regions, we additionally support the modification of data point groups by a force-directed placement as a post processing in addition to t-SNE that can be run after the initial t-SNE algorithm is stopped. This spatial modification can be applied to each identified data point group independently which is difficult to integrate into a standard t-SNE approach. We illustrate the usefulness of our technique by applying it to formerly conducted eye tracking studies investigating the readability of public transport maps and map annotations.
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    A Taxonomy and Survey of Dynamic Graph Visualization
    (© 2017 The Eurographics Association and John Wiley & Sons Ltd., 2017) Beck, Fabian; Burch, Michael; Diehl, Stephan; Weiskopf, Daniel; Chen, Min and Zhang, Hao (Richard)
    Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between entities in readable, scalable and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publications. While static graph visualizations are often divided into node‐link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline‐based ones. A bibliographic analysis provides insights into the organization and development of the field and its community. Finally, we identify and discuss challenges for future research. We also provide feedback from experts, collected with a questionnaire, which gives a broad perspective of these challenges and the current state of the field.Dynamic graph visualization focuses on the challenge of representing the evolution of relationships between entities in readable, scalable and effective diagrams. This work surveys the growing number of approaches in this discipline. We derive a hierarchical taxonomy of techniques by systematically categorizing and tagging publications. While static graph visualizations are often divided into node‐link and matrix representations, we identify the representation of time as the major distinguishing feature for dynamic graph visualizations: either graphs are represented as animated diagrams or as static charts based on a timeline. Evaluations of animated approaches focus on dynamic stability for preserving the viewer's mental map or, in general, compare animated diagrams to timeline‐based ones.
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    Visually Abstracting Event Sequences as Double Trees Enriched with Category‐Based Comparison
    (© 2023 Eurographics ‐ The European Association for Computer Graphics and John Wiley & Sons Ltd., 2023) Krause, Cedric; Agarwal, Shivam; Burch, Michael; Beck, Fabian; Hauser, Helwig and Alliez, Pierre
    Event sequence visualization aids analysts in many domains to better understand and infer new insights from event data. Analysing behaviour before or after a certain event of interest is a common task in many scenarios. In this paper, we introduce, formally define, and position as a domain‐agnostic tree visualization approach for this task. The visualization shows the sequences that led to the event of interest as a tree on the left, and those that followed on the right. Moreover, our approach enables users to create selections based on event attributes to interactively compare the events and sequences along colour‐coded categories. We integrate the double tree and category‐based comparison into a user interface for event sequence analysis. In three application examples, we show a diverse set of scenarios, covering short and long time spans, non‐spatial and spatial events, human and artificial actors, to demonstrate the general applicability of the approach.