Interactive exploration of large-scale time-varying data using dynamic tracking graphs

Widanagamaachchi, Wathsala; Christensen, Chris; Bremer, Peer-Timo; Pascucci, Valerio

doi:10.1109/ldav.2012.6378962

Cited by 66 publications

(51 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bremer et al [5] use tracking graphs to visualize burning cells in large-scale combustion simulations where cells are defined as areas exceeding a fuel consumption rate threshold and are tracked via spatial overlaps. Building on Bremer et al [5], Widanagamaachchi et al [16] propose a dynamic version of tracking graphs capable of quick graph layout updates for varying thresholds.…”

Section: Related Workmentioning

confidence: 99%

Viscous Fingering: A Topological Visual Analytic Approach

Lukasczyk

Aldrich

Steptoe

et al. 2017

AMM

View full text Add to dashboard Cite

Abstract.We present a methodology to analyze and visualize an ensemble of finite pointset method (FPM) simulations that model the viscous fingering process of salt solutions inside water. In course of the simulations the solutions form structures with increased salt concentration called viscous fingers. These structures are of primary interest to domain scientists as it is not clear when and where viscous fingers appear and how they evolve. To explore the aleatoric uncertainty embedded in the simulations we analyze an ensemble of simulation runs which differ due to stochastic effects. To detect and track the viscous fingers we derive a voxel volume for each simulation where fingers are identified as subvolumes that satisfy geometrical and topological constraints. Properties and the evolution of fingers are illustrated through tracking graphs that visualize when fingers form, dissolve, merge, and split. We provide multiple linked views to compare, browse, and analyze the ensemble in real-time.

show abstract

Section: Related Workmentioning

confidence: 99%

Viscous Fingering: A Topological Visual Analytic Approach

Lukasczyk

Aldrich

Steptoe

et al. 2017

AMM

View full text Add to dashboard Cite

show abstract

“…Other approaches store the tracking in a graph that is then shown using graph layout techniques. Widanagamaachchi et al [30] present tracking graphs that update quickly when the user changes the isovalue of the tracked features. Because we compute tracking information for all features up front, we can layout the tracking information directly and show more feature properties, e.g.…”

Section: Related Workmentioning

confidence: 99%

“…To track features over time, existing methods commonly fix a threshold; changing this value requires expensive recomputation of the tracking information [30]. Sup-porting threshold changes over time or on a per-feature basis depends on specifying a large number of parameters a priori.…”

Section: Introductionmentioning

confidence: 99%

Computing and Visualizing Time-Varying Merge Trees for High-Dimensional Data

Oesterling¹,

Heine

Weber

et al. 2017

Mathematics and Visualization

View full text Add to dashboard Cite

We introduce a new method that identifies and tracks features in arbitrary dimensions using the merge tree-a structure for identifying topological features based on thresholding in scalar fields. This method analyzes the evolution of features of the function by tracking changes in the merge tree and relates features by matching subtrees between consecutive time steps. Using the time-varying merge tree, we present a structural visualization of the changing function that illustrates both features and their temporal evolution. We demonstrate the utility of our approach by applying it to temporal cluster analysis of high-dimensional point clouds.

show abstract

“…They then derive entropy-based importance curves that characterize the local temporal behavior of each block, and classify them via k-means clustering. Widanagamaachchi et al [9] employ feature tracking graphs. Lee and Shen [10] visualize time-varying features and their motion on the basis of time activity curves (TAC) that contain each voxel's time series.…”

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

View full text Add to dashboard Cite

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.

show abstract

Interactive exploration of large-scale time-varying data using dynamic tracking graphs

Cited by 66 publications

References 42 publications

Viscous Fingering: A Topological Visual Analytic Approach

Viscous Fingering: A Topological Visual Analytic Approach

Computing and Visualizing Time-Varying Merge Trees for High-Dimensional Data

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

Contact Info

Product

Resources

About