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    This study introduces methods for user interaction during computational algorithms, moving beyond "black box" usage in data visualization tools. It enables real-time feedback and control for better interactive data exploration.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Human-Computer Interaction

    Background:

    • Interactive visualization tools often integrate with computational software like R and MATLAB.
    • Algorithms are frequently used as isolated 'black boxes', hindering interactive data exploration.
    • There is a need for user involvement in ongoing computations for effective data analysis.

    Purpose of the Study:

    • To structure, formalize, and discuss strategies for user involvement in ongoing computations.
    • To enable intermediate feedback and control during algorithm execution.
    • To provide guidelines for designing algorithmic APIs for better integration.

    Main Methods:

    • Characterizing user needs for intermediate feedback and control.
    • Formalizing and comparing strategies for user involvement based on algorithm characteristics.
    • Analyzing algorithm implementations in R for guideline adherence.

    Main Results:

    • A formalization and comparison of strategies for user involvement in algorithms.
    • Considerations for implementing user involvement within visualization tools or algorithms.
    • Identification of requirements and guidelines for algorithmic APIs.
    • Survey results indicate varying adherence to proposed guidelines in R packages.

    Conclusions:

    • Pragmatic options exist for enabling user involvement in ongoing computations.
    • User involvement can be implemented on both the visualization and algorithm sides.
    • Calls for analysis modules supporting direct data exploration are echoed, with practical solutions presented.