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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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    PC-Expo enhances high-dimensional data storytelling with parallel coordinate plots (PCPs) by enabling real-time, local detection of line patterns for custom axes reordering. This framework supports human-in-the-loop analysis, allowing users to tailor data narratives effectively.

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    Area of Science:

    • Visual Analytics
    • Information Visualization
    • Data Science

    Background:

    • Parallel Coordinate Plots (PCPs) are effective for high-dimensional (HD) data visualization but axis ordering significantly impacts data interpretation.
    • Existing methods for PCP axis reordering are often restrictive, failing to account for multiple or local data properties and lacking human-in-the-loop (HIL) support.

    Purpose of the Study:

    • To introduce PC-Expo, a real-time visual analytics framework for comprehensive PCP line pattern detection and axes reordering.
    • To enable users to customize data storytelling in PCPs by optimizing axes order based on selected and weighted data properties.
    • To integrate HIL capabilities for intuitive and adaptable PCP axes reordering.

    Main Methods:

    • Developed real-time, local detection schemes for 12 common analysis tasks (properties) within PCPs.
    • Implemented a user-driven optimization system allowing property ranking and weighted combination for custom axes reordering.
    • Integrated local-property visualization to support HIL exploration of granular data regions and axis pair interactions.

    Main Results:

    • PC-Expo successfully detects and visualizes common line patterns in PCPs in real-time.
    • The framework allows users to create custom axes reordering schemes by selecting, ranking, and weighting desired data properties.
    • Local-property visualization aids users in understanding multi-property relationships and refining axes order for specific analytical goals.

    Conclusions:

    • PC-Expo provides a flexible and powerful tool for data storytelling with PCPs, overcoming limitations of previous methods.
    • The framework's real-time, local detection and HIL support enhance the explainability and adaptability of HD data analysis.
    • Evaluations confirm PC-Expo's efficacy in improving data interpretation and facilitating user-defined insights through effective PCP axes reordering.