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EventThread: Visual Summarization and Stage Analysis of Event Sequence Data.

Shunan Guo, Ke Xu, Rongwen Zhao

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    |September 4, 2017
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    EventThread visualizes complex event sequence data by clustering similar sequences into threads. This novel system reveals latent patterns and evolution, overcoming limitations of existing methods for analyzing records like health or service histories.

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    Area of Science:

    • Computer Science
    • Data Visualization
    • Machine Learning

    Background:

    • Event sequence data, such as electronic health records or service histories, are ordered series of events over time.
    • Analyzing these sequences can reveal important patterns, but visualizing large datasets with many event types is challenging.
    • Current methods often miss similar but not identical event sequence evolutions, focusing only on explicit pattern matching.

    Purpose of the Study:

    • To introduce EventThread, a novel visualization system for analyzing large collections of event sequences.
    • To address the limitations of existing methods in capturing latent clusters and evolutionary patterns within event sequence data.
    • To enable interactive exploration and discovery of commonalities and differences in event sequence evolutions.

    Main Methods:

    • Developed EventThread, a system that clusters event sequences into "threads" using tensor analysis.
    • Visualizes latent stage categories and evolution patterns by interactively grouping similar threads.
    • Employs similarity grouping to create time-specific clusters for enhanced analysis.

    Main Results:

    • EventThread effectively clusters event sequences based on latent similarities, revealing hidden patterns.
    • The system visualizes the evolution of event sequences, highlighting common and distinct pathways.
    • Demonstrated effectiveness across three diverse application domains: healthcare, academic records, and automotive service.

    Conclusions:

    • EventThread offers a powerful new approach for visually exploring and understanding complex event sequence data.
    • The system overcomes limitations of traditional methods by capturing nuanced, latent patterns in sequence evolution.
    • Provides a valuable tool for researchers and practitioners across various fields dealing with temporal event data.