MultiStream: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series

Cuenca, Erick; Sallaberry, Arnaud; Wang, Florence Y.; Poncelet, Pascal

doi:10.1109/tvcg.2018.2796591

Cited by 33 publications

(27 citation statements)

References 32 publications

(58 reference statements)

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“…RankExplorer (Shi et al 2012) uses a stacked graph to support exploration of rank changes over time in sets of time series. MultiStream (Cuenca et al 2018) adapts ThemeRiver to support interactive exploration of hierarchies of multiple time series using a nonlinear time axis. Our work also uses a stacked graph metaphor augmented with additional cue labels representing sentiment and stance data, as discussed in Sect.…”

Section: Visualization Of Time-varying Datamentioning

confidence: 99%

StanceVis Prime: visual analysis of sentiment and stance in social media texts

Kucher

Martins

Paradis

et al. 2020

J Vis

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Text visualization and visual text analytics methods have been successfully applied for various tasks related to the analysis of individual text documents and large document collections such as summarization of main topics or identification of events in discourse. Visualization of sentiments and emotions detected in textual data has also become an important topic of interest, especially with regard to the data originating from social media. Despite the growing interest in this topic, the research problem related to detecting and visualizing various stances, such as rudeness or uncertainty, has not been adequately addressed by the existing approaches. The challenges associated with this problem include the development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this paper, we describe our work on a visual analytics platform, called StanceVis Prime, which has been designed for the analysis of sentiment and stance in temporal text data from various social media data sources. The use case scenarios intended for StanceVis Prime include social media monitoring and research in sociolinguistics. The design was motivated by the requirements of collaborating domain experts in linguistics as part of a larger research project on stance analysis. Our approach involves consuming documents from several text stream sources and applying sentiment and stance classification, resulting in multiple data series associated with source texts. StanceVis Prime provides the end users with an overview of similarities between the data series based on dynamic time warping analysis, as well as detailed visualizations of data series values. Users can also retrieve and conduct both distant and close reading of the documents corresponding to the data series. We demonstrate our approach with case studies involving political targets of interest and several social media data sources and report preliminary user feedback received from a domain expert. Graphic abstract

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Section: Visualization Of Time-varying Datamentioning

confidence: 99%

StanceVis Prime: visual analysis of sentiment and stance in social media texts

Kucher

Martins

Paradis

et al. 2020

J Vis

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“…Detailed discussions of the amount of "wiggles" in such visualizations have been made by Byron and Wattenberg [11] as well as Bartolomeo and Hu [2]. A number of works [3,14,40] incorporate hierarchical information into stacked graphs. Hierarchy is conveyed through color encoding [14,40], joint displaying of a tree [14,40], or showing hierarchy layers separately [3] and facilitates interactive exploration of the data [3,40].…”

Section: Visualization Approaches For Time-dependent Treesmentioning

confidence: 99%

“…A number of works [3,14,40] incorporate hierarchical information into stacked graphs. Hierarchy is conveyed through color encoding [14,40], joint displaying of a tree [14,40], or showing hierarchy layers separately [3] and facilitates interactive exploration of the data [3,40]. In contrast to our approach, the topology of the tree remains static and nodes do not appear or disappear.…”

Section: Visualization Approaches For Time-dependent Treesmentioning

confidence: 99%

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Temporal Treemaps: Static Visualization of Evolving Trees

Köpp

Weinkauf

2019

IEEE Trans. Visual. Comput. Graphics

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We consider temporally evolving trees with changing topology and data: tree nodes may persist for a time range, merge or split, and the associated data may change. Essentially, one can think of this as a time series of trees with a node correspondence per hierarchy level between consecutive time steps. Existing visualization approaches for such data include animated 2D treemaps, where the dynamically changing layout makes it difficult to observe the data in its entirety. We present a method to visualize this dynamic data in a static, nested, and space-filling visualization. This is based on two major contributions: First, the layout constitutes a graph drawing problem. We approach it for the entire time span at once using a combination of a heuristic and simulated annealing. Second, we propose a rendering that emphasizes the hierarchy through an adaption of the classic cushion treemaps. We showcase the wide range of applicability using data from feature tracking in time-dependent scalar fields, evolution of file system hierarchies, and world population.

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“…The complexity of the data may originate from the variation or its volume [11]. To visualize the whole dataset, several approaches use the aggregation of data in different ways (e.g., [6,[41][42][43]), and progressive exploration [9,38,40]. The focus may be placed on finding the abstract representations or finding patterns, such as repeated sequences of events, and highlight [25] or abstract them, like Malik et al [30] do with patient data for cohort comparison.…”

Section: Visualization Of Large Chartsmentioning

confidence: 99%

Visualization of Large Molecular Trajectories

Duran

Hermosilla²,

Ropinski³

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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The analysis of protein-ligand interactions is a time-intensive task. Researchers have to analyze multiple physico-chemical properties of the protein at once and combine them to derive conclusions about the protein-ligand interplay. Typically, several charts are inspected, and 3D animations can be played side-by-side to obtain a deeper understanding of the data. With the advances in simulation techniques, larger and larger datasets are available, with up to hundreds of thousands of steps. Unfortunately, such large trajectories are very difficult to investigate with traditional approaches. Therefore, the need for special tools that facilitate inspection of these large trajectories becomes substantial. In this paper, we present a novel system for visual exploration of very large trajectories in an interactive and user-friendly way. Several visualization motifs are automatically derived from the data to give the user the information about interactions between protein and ligand. Our system offers specialized widgets to ease and accelerate data inspection and navigation to interesting parts of the simulation. The system is suitable also for simulations where multiple ligands are involved. We have tested the usefulness of our tool on a set of datasets obtained from protein engineers, and we describe the expert feedback.

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MultiStream: A Multiresolution Streamgraph Approach to Explore Hierarchical Time Series

Cited by 33 publications

References 32 publications

StanceVis Prime: visual analysis of sentiment and stance in social media texts

StanceVis Prime: visual analysis of sentiment and stance in social media texts

Temporal Treemaps: Static Visualization of Evolving Trees

Visualization of Large Molecular Trajectories

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