Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Time-Series Graph00:54

Time-Series Graph

A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
Bar Graph01:07

Bar Graph

A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
Graphs of Functions01:30

Graphs of Functions

Graphs of functions provide a visual representation of how output values change in response to varying inputs. Each point on the graph corresponds to an ordered pair, where the x-coordinate (independent variable) determines the horizontal position and the y-coordinate (dependent variable) determines the vertical position. Linear functions like y = x give a straight line, indicating a constant rate of change.Nonlinear functions display more complex behaviors. Even power functions generate...
Multiple Bar Graph01:07

Multiple Bar Graph

As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
Interpreting R Charts01:22

Interpreting R Charts

R chart, or range chart, is a fundamental tool in statistical process control used to monitor the variability within a process. It complements the X-bar (x̄) chart by focusing on the range of the data, rather than individual values, providing a clear picture of the process dispersion over time.
An R chart plots the range of subsets of measurements collected from a process. Each point on the chart represents the range—defined as the difference between the maximum and minimum values—of a sample...
Relative Frequency Histogram01:14

Relative Frequency Histogram

The relative frequency depicts the proportion of data points that have each value. The frequency tells the number of data points that have each value. Like the histogram, a relative frequency histogram also has the same shape with a horizontal scale (the x-axis), but the vertical scale (the y-axis) is marked with relative frequencies (percentages of the whole) instead of actual frequencies. A relative frequency histogram is a graphical representation of a frequency distribution where the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Smartphone Detection of Fetal Movements Using Artificial Intelligence.

Obstetrics and gynecology·2026
Same author

CATOM: Causal Topology Map for Spatiotemporal Traffic Analysis With Granger Causality in Urban Areas.

IEEE transactions on visualization and computer graphics·2024
Same author

Two-Level Transfer Functions Using t-SNE for Data Segmentation in Direct Volume Rendering.

IEEE transactions on visualization and computer graphics·2024
Same author

Development of Polycistronic Baculovirus Surface Display Vectors to Simultaneously Express Viral Proteins of Porcine Reproductive and Respiratory Syndrome and Analysis of Their Immunogenicity in Swine.

Vaccines·2023
Same author

Optimal mode of delivery in pregnancy: Individualized predictions using national vital statistics data.

PLOS digital health·2023
Same author

Corrections to "Visual Analytics for Decision-Making During Pandemics".

Computing in science & engineering·2022
Same journal

MesoSplats: Texture Synthesis with Gaussian Splatting.

IEEE transactions on visualization and computer graphics·2026
Same journal

GLLA: A Unified Force-Directed Graph Layout Framework Supporting Local Adjustments.

IEEE transactions on visualization and computer graphics·2026
Same journal

Multi-Perception Crowd: Learning to combine entity and implicit perception for diverse crowd simulation.

IEEE transactions on visualization and computer graphics·2026
Same journal

Hiding in Plain Sight: Camouflaging Real-world Objects.

IEEE transactions on visualization and computer graphics·2026
Same journal

RTF2Mesh: Restricted Tangent Face Based Mesh Compression With Neural Displacement Fields.

IEEE transactions on visualization and computer graphics·2026
Same journal

Practical Occluder Generation for Mobile Games.

IEEE transactions on visualization and computer graphics·2026
See all related articles

Related Experiment Video

Updated: Jun 3, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Time-varying data visualization using functional representations.

Yun Jang1, David S Ebert, Kelly Gaither

  • 1Department of Computer Science, ETH Zurich, Switzerland. jjangyn@gmail.com

IEEE Transactions on Visualization and Computer Graphics
|March 9, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient method for visualizing complex time series data by using functional representations and temporal similarity. The approach enhances data encoding, reduces storage needs, and enables interactive visualization for scientific simulations.

More Related Videos

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions
05:41

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions

Published on: February 9, 2024

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Related Experiment Videos

Last Updated: Jun 3, 2026

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
10:58

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques

Published on: January 2, 2011

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions
05:41

Synchronous Triplanar Reconstruction Integrated with Color Doppler Mapping for Precise and Rapid Localization of Thyroid Lesions

Published on: February 9, 2024

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
08:36

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms

Published on: March 21, 2019

Area of Science:

  • Scientific Visualization
  • Data Analysis
  • Computer Graphics

Background:

  • Analyzing temporal variations in scientific simulations is crucial but challenging due to large, often irregular time series data volumes.
  • Interactive visualization of scattered time series data remains a significant hurdle.
  • Previous research suggested functional representations with basis functions for visualizing scattered data.

Purpose of the Study:

  • To develop an efficient encoding technique for time-varying data sets using functional representations.
  • To enhance the interactive visualization of time series data, particularly scattered and irregular datasets.
  • To improve data storage efficiency and rendering performance for time-varying scientific data.

Main Methods:

  • Utilized functional representation with basis functions for time-varying datasets.
  • Developed an efficient encoding technique leveraging temporal similarity between time steps.
  • Implemented a graduated approach with three methods of increasing time complexity.
  • Employed binary space partitioning (BSP) tree textures for efficient rendering.

Main Results:

  • Achieved enhanced encoding performance for time-varying datasets.
  • Reduced data storage requirements by saving only modified or new basis functions.
  • Enabled interactive visualization of time-varying encoding results.
  • Increased rendering performance through BSP tree textures.

Conclusions:

  • The proposed functional representation and encoding technique effectively addresses challenges in visualizing time-varying scientific data.
  • The system offers significant improvements in data encoding, storage, and interactive visualization performance.
  • The integration of BSP tree textures further optimizes rendering efficiency for complex datasets.