Sequence Synopsis: Optimize Visual Summary of Temporal Event Data.

Area of Science:

• Data Visualization
• Machine Learning
• Pattern Recognition

Background:

• Event sequence data is prevalent in domains like customer behavior, healthcare, and diagnostics.
• Real-world event sequences are often noisy and complex, posing challenges for concise data overviews.
• Existing methods struggle to balance information content with reduced visual clutter.

Purpose of the Study:

• To propose a novel visualization technique for event sequence data analysis.
• To address the trade-off between reducing visual clutter and increasing information content.
• To enable simultaneous sequence clustering and pattern extraction from noisy data.

Main Methods:

• Utilized the minimum description length (MDL) principle for data overview construction.
• Developed a visualization technique tolerant to noise (missing/additional events).
• Proposed a multi-level-of-detail visual analytics framework for interactive exploration.

Main Results:

• Successfully constructed coarse-level overviews of event sequence data.
• Achieved simultaneous sequence clustering and pattern extraction.
• Demonstrated effectiveness and usability on real-world datasets, including vehicle fault analysis.

Conclusions:

• The proposed MDL-based visualization technique effectively handles noisy event sequence data.
• The visual analytics framework facilitates interactive exploration and pattern discovery.
• The method shows promise for predictive maintenance through fault development path analysis.