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Related Concept Videos

Design Consideration01:22

Design Consideration

241
Designing a structure involves a series of considerations, primarily the material's ultimate strength, calculated through tests that measure changes under increased force until the material reaches its breaking point or limit. The ultimate load, where the material breaks, is divided by its original cross-sectional area, resulting in the ultimate normal stress or strength. The ultimate shearing stress is another significant factor taken into account.
The factor of safety is another key...
241
Design Example01:23

Design Example

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The innovation of touch-tone telephony revolutionized the telecommunications industry by replacing the traditional rotary dial with a dual-tone multi-frequency (DTMF) signaling system. This system uses a matrix-style keypad with buttons arranged in four rows and three columns, creating 12 distinct signals each assigned to a pair of frequencies. Each button press results in a simultaneous generation of two sinusoidal tones – one from a low-frequency group (697 to 941 Hz) and one from a...
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Group Design02:01

Group Design

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The most basic experimental design involves two groups: the experimental group and the control group. The two groups are designed to be the same except for one difference— experimental manipulation. The experimental group gets the experimental manipulation—that is, the treatment or variable being tested—and the control group does not. Since experimental manipulation is the only difference between the experimental and control groups, we can be sure that any differences between...
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Pareto Chart00:52

Pareto Chart

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A Pareto chart is a bar graph or a combination of both line and bar graphs. The bar lengths represent the individual values or the frequency, while the lines represent the cumulative total values. In this chart, the longest bars are arranged on the left and the shortest bars on the right, which makes it easier to read and interpret the data. It can also be called a Pareto diagram or Pareto analysis.
The Pareto chart is named after the Italian economist Vilfredo Pareto, who described the Pareto...
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Design Example: Automobile Ignition System01:14

Design Example: Automobile Ignition System

264
The automobile's ignition system plays a vital role by ensuring the timely ignition of the fuel-air mixture in each cylinder. This ignition is facilitated by a spark plug, which is composed of two electrodes separated by an air gap. A spark forms across this air gap when a substantial voltage is generated between the electrodes, leading to the ignition of the fuel.
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Experimental Designs01:16

Experimental Designs

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An experimental design is a systematic process that allows researchers to evaluate the relationship between dependent and independent variables. There are three widely used types of experimental design - pre-experimental design, true experimental design, and quasi-experimental design. In pre-experimental design, the researcher compares the data before and after some interventions or treatments. The true-experimental design has more than one purposefully created group, a commonly measured...
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DMiner: Dashboard Design Mining and Recommendation.

Yanna Lin, Haotian Li, Aoyu Wu

    IEEE Transactions on Visualization and Computer Graphics
    |April 7, 2023
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    This summary is machine-generated.

    This study introduces a data-driven method to automatically organize dashboard visualizations by mining design rules. The developed recommender system achieves human-level performance in dashboard arrangement and coordination.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Dashboards integrate multiple data views for simultaneous analysis.
    • Designing effective dashboards is complex due to the need for logical arrangement and coordination of visualizations.
    • Automating dashboard organization can significantly improve design efficiency.

    Purpose of the Study:

    • To propose a data-driven approach for mining design rules from existing dashboards.
    • To develop an automated system for dashboard organization, focusing on view arrangement and coordination.
    • To evaluate the effectiveness of the proposed design rules and recommender system.

    Main Methods:

    • Collected a dataset of 854 online dashboards.
    • Developed feature engineering for describing individual views and their relationships (data, encoding, layout, interactions).
    • Identified design rules and built a recommender system (DMiner) for dashboard design automation.

    Main Results:

    • Extracted design rules align with expert practices, confirmed by an expert study.
    • The recommender system demonstrates human-level performance in automating dashboard organization, shown in a comparative user study.
    • The approach successfully mines design principles from a large corpus of dashboards.

    Conclusions:

    • The data-driven approach offers a promising method for automating dashboard design.
    • The developed recommender system aids in creating effective and elegant dashboard layouts.
    • This work lays the foundation for design mining in visualization recommender systems.