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Updated: May 24, 2025

Facilitating the Analysis of Immunological Data with Visual Analytic Techniques
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Visual Analytics Meets Process Mining: Challenges and Opportunities.

Silvia Miksch, Claudio Di Ciccio, Pnina Soffer

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    Summary
    This summary is machine-generated.

    Visual analytics (VA) and process mining (PM) combine human insight with computer power for knowledge discovery. Integrating these methods enhances understanding of complex event data and process improvement.

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    Area of Science:

    • Integrates computer science, information science, and cognitive science.
    • Focuses on the synergy between human cognition and computational power for data analysis.

    Background:

    • Visual analytics (VA) leverages human visual exploration with computer processing for knowledge discovery.
    • Process mining (PM) extracts process insights from event logs for discovery, monitoring, and improvement.
    • The combined potential of VA and PM remains largely underexplored.

    Purpose of the Study:

    • To illustrate the concepts of Visual Analytics and Process Mining.
    • To explore the synergistic benefits of combining VA and PM for complex event data analysis.
    • To identify challenges and opportunities in applying VA to process data and enhancing VA with PM.

    Main Methods:

    • Conceptual illustration of Visual Analytics (VA) principles and interactive interfaces.
    • Explanation of Process Mining (PM) algorithms for event log analysis.
    • Discussion on the integration strategies for VA and PM techniques.

    Main Results:

    • The combination of VA and PM can enhance the comprehensibility of complex information structures.
    • Integrating VA with PM facilitates deeper insights into process data.
    • This integration offers new avenues for process discovery, monitoring, and improvement.

    Conclusions:

    • Combining Visual Analytics and Process Mining offers significant potential for advanced data analysis.
    • Further research is needed to fully explore the challenges and opportunities in this interdisciplinary area.
    • This integration can lead to more effective knowledge discovery and process optimization.