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Scaling01:26

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In designing and analyzing filters, resonant circuits, or circuit analysis at large, working with standard element values like 1 ohm, 1 henry, or 1 farad can be convenient before scaling these values to more realistic figures. This approach is widely utilized by not employing realistic element values in numerous examples and problems; it simplifies mastering circuit analysis through convenient component values. The complexity of calculations is thereby reduced, with the understanding that...
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
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The maximum size of aggregate is defined as the aperture of the sieve retaining 15 percent or more of the particles present in the aggregate sample. The aggregate's maximum size impacts the concrete's water requirement, workability, and strength. Larger aggregates reduce the surface area needing cement paste coverage, which can lower water needs, thereby allowing a decrease in the water-to-cement ratio when the desired workability and richness of the mix are to be maintained, which can...
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Scalability in Visualization.

Gaelle Richer, Alexis Pister, Moataz Abdelaal

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    |April 4, 2023
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    Summary
    This summary is machine-generated.

    This study introduces a conceptual model for visualization scalability, analyzing 120 publications. It proposes consistent terminology and an effort function to clarify scalability claims and improve research reproducibility.

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    Area of Science:

    • Computer Science
    • Information Visualization
    • Human-Computer Interaction

    Background:

    • Scalability is a critical aspect of visualization research, yet its definition and application lack consistency.
    • Existing literature shows varied interpretations of scalability, hindering clear communication and comparison of visualization techniques.
    • A need exists for a standardized framework to address scalability in visualization research.

    Purpose of the Study:

    • To introduce a conceptual model for scalability tailored to visualization research.
    • To systematically analyze and characterize the diverse notions of scalability in visualization publications.
    • To propose a consistent terminology and methodology for presenting scalability in visualization research.

    Main Methods:

    • Conducted a systematic analysis of over 120 visualization publications from 1990 to 2020.
    • Developed a conceptual model centered around an effort function with defined inputs (problem size, resources) and outputs (computational runtime, visual clutter).
    • Selected representative examples to illustrate various scalability concepts in visualization.

    Main Results:

    • Identified a significant lack of consistency in the use and definition of scalability within the visualization research community.
    • Characterized different notions of scalability present in the analyzed literature.
    • Demonstrated the utility of the proposed effort function for quantifying scalability.

    Conclusions:

    • The proposed conceptual model and consistent terminology enhance the characterization of scalability in visualization research.
    • The effort function provides a structured method for supporting scalability claims.
    • Recommendations are provided to foster clearer presentation, fair comparison, and improved reproducibility of visualization techniques.