Interactive Storyboard for Overall Time-Varying Data Visualization

Lu, Aidong; Shen, Han‐Wei

doi:10.1109/pacificvis.2008.4475470

Cited by 47 publications

(30 citation statements)

References 29 publications

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“…In addition, illustrative techniques have been used to depict processes of interest. Lu and Shen [20] propose interactive storyboards composed of volume renderings and descriptive geometric primitives. While most techniques mentioned above deal with volume data, numerous approaches have been presented for flow visualization (cf.…”

Section: Visualization Of Spatio-temporal Datamentioning

confidence: 99%

Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks

Frey

2017

Informatics

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Increasingly fast computing systems for simulations and high-accuracy measurement techniques drive the generation of time-dependent volumetric data sets with high resolution in both time and space. To gain insights from this spatio-temporal data, the computation and direct visualization of pairwise distances between time steps not only supports interactive user exploration, but also drives automatic analysis techniques like the generation of a meaningful static overview visualization, the identification of rare events, or the visual analysis of recurrent processes. However, the computation of pairwise differences between all time steps is prohibitively expensive for large-scale data not only due to the significant cost of computing expressive distance between high-resolution spatial data, but in particular owing to the large number of distance computations (O(|T| 2 )), with |T| being the number of time steps). Addressing this issue, we present and evaluate different strategies for the progressive computation of similarity information in a time series, as well as an approach for estimating distance information that has not been determined so far. In particular, we investigate and analyze the utility of using neural networks for estimating pairwise distances. On this basis, our approach automatically determines the sampling strategy yielding the best result in combination with trained networks for estimation. We evaluate our approach with a variety of time-dependent 2D and 3D data from simulations and measurements as well as artificially generated data, and compare it against an alternative technique. Finally, we discuss prospects and limitations, and discuss different directions for improvement in future work.

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Section: Visualization Of Spatio-temporal Datamentioning

confidence: 99%

Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks

Frey

2017

Informatics

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“…Jern et al [13] embed semi-guided interactive visualizations into standard HTML documents, thus letting the users analyze data and save the insights for sharing. Lu and Shen [16] generate interactive storyboards from sample volume renderings and descriptive geometric primitives gained through a data analysis process. Wohlfart and Hauser [34] suggest that, along with comprehensibility of a visualization message, credibility should be addressed as well.…”

Section: Related Workmentioning

confidence: 99%

Run Watchers: Automatic Simulation-Based Decision Support in Flood Management

Konev

Waser

Sadransky

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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In this paper, we introduce a simulation-based approach to design protection plans for flood events. Existing solutions require a lot of computation time for an exhaustive search, or demand for a time-consuming expert supervision and steering. We present a faster alternative based on the automated control of multiple parallel simulation runs. Run Watchers are dedicated system components authorized to monitor simulation runs, terminate them, and start new runs originating from existing ones according to domain-specific rules. This approach allows for a more efficient traversal of the search space and overall performance improvements due to a re-use of simulated states and early termination of failed runs. In the course of search, Run Watchers generate large and complex decision trees. We visualize the entire set of decisions made by Run Watchers using interactive, clustered timelines. In addition, we present visualizations to explain the resulting response plans. Run Watchers automatically generate storyboards to convey plan details and to justify the underlying decisions, including those which leave particular buildings unprotected. We evaluate our solution with domain experts.

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“…Illustrative rendering and visual abstraction have been used to convey motion, for example, adding cartoon-like motion lines to show the trajectory of moving objects [15], see also [13, 16, 23, 27]. These and other illustrative visualization techniques (e.g., [2]) effectively use visual bandwidth by simplifying unneeded detail while emphasizing important detail.…”

Section: Related Workmentioning

confidence: 99%

Trend-Centric Motion Visualization: Designing and Applying a New Strategy for Analyzing Scientific Motion Collections

Schroeder

Korsakov

Knipe

et al. 2014

IEEE Trans. Visual. Comput. Graphics

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In biomechanics studies, researchers collect, via experiments or simulations, datasets with hundreds or thousands of trials, each describing the same type of motion (e.g., a neck flexion-extension exercise) but under different conditions (e.g., different patients, different disease states, pre- and post-treatment). Analyzing similarities and differences across all of the trials in these collections is a major challenge. Visualizing a single trial at a time does not work, and the typical alternative of juxtaposing multiple trials in a single visual display leads to complex, difficult-to-interpret visualizations. We address this problem via a new strategy that organizes the analysis around motion trends rather than trials. This new strategy matches the cognitive approach that scientists would like to take when analyzing motion collections. We introduce several technical innovations making trend-centric motion visualization possible. First, an algorithm detects a motion collection’s trends via time-dependent clustering. Second, a 2D graphical technique visualizes how trials leave and join trends. Third, a 3D graphical technique, using a median 3D motion plus a visual variance indicator, visualizes the biomechanics of the set of trials within each trend. These innovations are combined to create an interactive exploratory visualization tool, which we designed through an iterative process in collaboration with both domain scientists and a traditionally-trained graphic designer. We report on insights generated during this design process and demonstrate the tool’s effectiveness via a validation study with synthetic data and feedback from expert musculoskeletal biomechanics researchers who used the tool to analyze the effects of disc degeneration on human spinal kinematics.

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Interactive Storyboard for Overall Time-Varying Data Visualization

Cited by 47 publications

References 29 publications

Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks

Sampling and Estimation of Pairwise Similarity in Spatio-Temporal Data Based on Neural Networks

Run Watchers: Automatic Simulation-Based Decision Support in Flood Management

Trend-Centric Motion Visualization: Designing and Applying a New Strategy for Analyzing Scientific Motion Collections

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