221 research outputs found
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MyBrush: Brushing and Linking with Personal Agency
We extend the popular brushing and linking technique by incorporating personal agency in the interaction. We map existing research related to brushing and linking into a design space that deconstructs the interaction technique into three components: source (what is being brushed), link (the expression of relationship between source and target), and target (what is revealed as related to the source). Using this design space, we created MyBrush, a unified interface that offers personal agency over brushing and linking by giving people the flexibility to configure the source, link, and target of multiple brushes. The results of three focus groups demonstrate that people with different backgrounds leveraged personal agency in different ways, including performing complex tasks and showing links explicitly. We reflect on these results, paving the way for future research on the role of personal agency in information visualization
Recommended from our members
MyBrush: Brushing and Linking with Personal Agency
We extend the popular brushing and linking technique by incorporating personal agency in the interaction. We map existing research related to brushing and linking into a design space that deconstructs the interaction technique into three components: source (what is being brushed), link (the expression of relationship between source and target), and target (what is revealed as related to the source). Using this design space, we created MyBrush, a unified interface that offers personal agency over brushing and linking by giving people the flexibility to configure the source, link, and target of multiple brushes. The results of three focus groups demonstrate that people with different backgrounds leveraged personal agency in different ways, including performing complex tasks and showing links explicitly. We reflect on these results, paving the way for future research on the role of personal agency in information visualization
Multivariate volume visualization through dynamic projections
pre-printWe propose a multivariate volume visualization framework that tightly couples dynamic projections with a high-dimensional transfer function design for interactive volume visualization. We assume that the complex, high-dimensional data in the attribute space can be well-represented through a collection of low-dimensional linear subspaces, and embed the data points in a variety of 2D views created as projections onto these subspaces. Through dynamic projections, we present animated transitions between different views to help the user navigate and explore the attribute space for effective transfer function design. Our framework not only provides a more intuitive understanding of the attribute space but also allows the design of the transfer function under multiple dynamic views, which is more flexible than being restricted to a single static view of the data. For large volumetric datasets, we maintain interactivity during the transfer function design via intelligent sampling and scalable clustering. Using examples in combustion and climate simulations, we demonstrate how our framework can be used to visualize interesting structures in the volumetric space
Transfer function design based on user selected samples for intuitive multivariate volume exploration
pre-printMultivariate volumetric datasets are important to both science and medicine. We propose a transfer function (TF) design approach based on user selected samples in the spatial domain to make multivariate volumetric data visualization more accessible for domain users. Specifically, the user starts the visualization by probing features of interest on slices and the data values are instantly queried by user selection. The queried sample values are then used to automatically and robustly generate high dimensional transfer functions (HDTFs) via kernel density estimation (KDE). Alternatively, 2D Gaussian TFs can be automatically generated in the dimensionality reduced space using these samples. With the extracted features rendered in the volume rendering view, the user can further refine these features using segmentation brushes. Interactivity is achieved in our system and different views are tightly linked. Use cases show that our system has been successfully applied for simulation and complicated seismic data sets
10241 Abstracts Collection -- Information Visualization
From 13.06.10 to 18.06.10, the Dagstuhl Seminar 10241 ``Information Visualization \u27\u27 was held in Schloss Dagstuhl~--~Leibniz Center for Informatics.
During the seminar, several participants presented their current
research, and ongoing work and open problems were discussed. Abstracts of
the presentations given during the seminar as well as abstracts of
seminar results and ideas are put together in this paper. The first section
describes the seminar topics and goals in general.
Links to extended abstracts or full papers are provided, if available
EXPLORATORY VISUALIZATION OF GRAPHS BASED ON COMMUNITY STRUCTURE
Communities, also called clusters or modules, are groups of nodes which probably share common properties and/or play similar roles within a graph. They widely exist in real networks such as biological, social, and information networks. Allowing users to interactively browse and explore the community structure, which is essential for understanding complex systems, is a challenging yet important research topic. My work has been focused on visualization approaches to exploring the community structure in graphs based on automatic community detection results.
In this dissertation, we first report a formal user study that investigated the essen- tial influence factors, benefits, and constraints of a community based graph visual- ization system in a background application of seeking information from text corpora. A general evaluation methodology for exploratory visualization systems has been proposed and practiced. The evaluation methodology integrates detailed cognitive load analysis and users’ prior knowledge evaluation with quantitative and qualitative measures, so that in-depth insights can be gained. The study revealed that visual exploration based on the community structure benefits the understanding of real net- works. A literature review and a set of interviews were then conducted to learn tasks facing such graph exploration and the state-of-the-arts. This work led to commu- nity related graph visualization task taxonomy. Our examination of existing graph visualization systems revealed that a large number of community related graph visualization tasks are poorly supported in existing approaches. To bridge the gap, several
novel visualization techniques are proposed. In these approaches, graph topology information is mapped to a multidimensional space where the relationships between the communities and the nodes can be explicitly explored. Several user studies and case studies have been conducted to demonstrate the usefulness of these systems in real-world applications
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