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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Time Series Analysis

    Background:

    • Analyzing multiple data sequences presents challenges in tracking individual changes and comparing sequences over time.
    • Understanding how sequence comparisons evolve temporally is crucial for data analysis.

    Purpose of the Study:

    • To present STBins, a novel visual technique for analyzing multiple data sequences.
    • To address questions regarding individual sequence changes and temporal comparisons of sequence similarity.

    Main Methods:

    • Development of the STBins visual technique for sequence tracking and comparison.
    • Conducting a perception study to evaluate alternative visual designs for sequence tracking and comparison tasks.
    • Presenting case studies using real-world datasets to demonstrate STBins' application.

    Main Results:

    • STBins effectively supports visual tracking of individual data sequences.
    • The technique facilitates the comparison of multiple sequences by visualizing their similarity within temporal windows.
    • A perception study validated the readability of STBins' visual designs.

    Conclusions:

    • STBins provides a robust solution for analyzing complex temporal data sequences.
    • The visual technique aids in understanding both individual sequence dynamics and inter-sequence relationships over time.
    • Case studies demonstrate the practical utility of STBins in real-world data analysis scenarios.