Time maps: A tool for visualizing many discrete events across multiple timescales

Watson, Max

doi:10.1109/bigdata.2015.7363824

Cited by 5 publications

(2 citation statements)

References 9 publications

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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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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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show abstract

“…For example, a recent study in bidirectional movement used charts with contour maps and multi-layer heat maps to efficiently analyze behaviors based on big movement data [16], [17], [22]. Furthermore, an additional layer of challenge is presented when visualizing many discrete events in a dataset over long timelines, as it is possible for the viewer to get overwhelmed with information or even miss critical data [23].…”

Section: Introductionmentioning

confidence: 99%

Visualizing Effects of COVID-19 Social Isolation with Residential Activity Big Data Sensor Data

Rajkumar

Wallace

Ault

et al. 2020

2020 IEEE International Conference on Big Data (Big Data)

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The ability to understand and visualize big data sets is of increasing interest to caregivers and clinicians as ambient home sensing can provide massive amounts of data related to the activities of residents. However, this data is only useful if it can be effectively and simply visualized for review and analysis. This paper presents the visualization of longitudinal data sets from ambient well-being sensors deployed in 3 residences that have a spousal pair dyad where 1 resident has been diagnosed with Mild Cognitive Impairment or Dementia and the spousal partner is acting as a caregiver. The paper presents the differences in activity and behaviour that can be observed in the 3 residences by comparing two 30-day periods prior to and one 30-day period during COVID-19 social isolation precautions. The work shows the potential for this circle plot based visualization technique to summarize resident activity and also to convey external factors such as the variation in solar day that can itself influence behaviour.

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EmotionMap: Visual Analysis of Video Emotional Content on a Map

Song

Zhu

et al. 2020

J. Comput. Sci. Technol.

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Time maps: A tool for visualizing many discrete events across multiple timescales

Cited by 5 publications

References 9 publications

Visualization of Large Molecular Trajectories

Visualization of Large Molecular Trajectories

Visualizing Effects of COVID-19 Social Isolation with Residential Activity Big Data Sensor Data

EmotionMap: Visual Analysis of Video Emotional Content on a Map

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