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

Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a problem,...
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
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...
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures from...
Dimensional Analysis02:19

Dimensional Analysis

The concept of dimension is important because every mathematical equation linking physical quantities must be dimensionally consistent, implying that mathematical equations must meet the following two rules. The first rule is that, in an equation, the expressions on each side of the equal sign must have the same dimensions. This is fairly intuitive since we can only add or subtract quantities of the same type (dimension). The second rule states that, in an equation, the arguments of any of the...
Dimensional Analysis03:40

Dimensional Analysis

Dimensional analysis, also known as the factor label method, is a versatile approach for mathematical operations. The main principle behind this approach is: the units of quantities must be subjected to the same mathematical operations as their associated numbers. This method can be applied to computations ranging from simple unit conversions to more complex and multi-step calculations involving several different quantities and their units.
Conversion Factors and Dimensional Analysis
The unit...

You might also read

Related Articles

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

Sort by
Same author

Informing Future Risks of Record-Level Rainfall in the United States.

Geophysical research letters·2019
Same author

Query2Question: Translating Visualization Interaction into Natural Language.

IEEE transactions on visualization and computer graphics·2015
Same author

Conservation: Pool resources for protected areas.

Nature·2014
Same author

Visual analysis of higher-order conjunctive relationships in multidimensional data using a hypergraph query system.

IEEE transactions on visualization and computer graphics·2013
Same author

Variation in estimated ozone-related health impacts of climate change due to modeling choices and assumptions.

Environmental health perspectives·2012
Same author

Message from the paper chairs and guest editors. Conference proceedings.

IEEE transactions on visualization and computer graphics·2011

Related Experiment Video

Updated: Jun 17, 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

Cross-filtered views for multidimensional visual analysis.

Chris Weaver1

  • 1School of Computer Science, University of Oklahoma, Norman, OK 73072-7807, USA. weaver@cs.ou.edu

IEEE Transactions on Visualization and Computer Graphics
|January 16, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces cross-filtering, a method for interactively querying multidimensional data across linked visualizations. It simplifies complex data analysis by enabling users to explore relationships across dimensions effectively.

More Related Videos

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

Related Experiment Videos

Last Updated: Jun 17, 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

Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects
08:39

Longitudinal Micro-Computed Tomography Image Analysis for User-Defined Region of Interest in Critical-Sized Bone Defects

Published on: June 24, 2025

Area of Science:

  • Information Visualization
  • Human-Computer Interaction
  • Data Analysis

Background:

  • Multidimensional data analysis necessitates examining relationships across various dimensions.
  • Coordinated multiple view (CMV) approaches are common in visual analysis tools for complex queries.
  • Developing CMV tools is challenging due to mapping data structures to visual abstractions for pattern discovery.

Purpose of the Study:

  • To present a method for interactively expressing multidimensional set queries via cross-filtering.
  • To outline design strategies for constructing CMV interfaces for cross-filtered visual analysis.
  • To demonstrate the utility and modularity of cross-filtering as a design pattern.

Main Methods:

  • Interactive cross-filtering of data values across pairs of views.
  • Developing design strategies for CMV interfaces tailored for cross-filtered analysis.
  • Applying cross-filtering to diverse datasets and domains for evaluation.

Main Results:

  • Cross-filtering enables interactive expression of multidimensional set queries.
  • Design strategies facilitate modular and reusable cross-filtering implementations.
  • Cross-filtering can be customized for different data types and integrated into broader CMV designs.

Conclusions:

  • Cross-filtering is a suitable and effective design pattern for visual analysis tools.
  • The approach enhances the exploration of cross-dimensional relationships in multidimensional data.
  • Further research should address identified limitations of the cross-filtering method.