KD-Box: Line-segment-based KD-tree for Interactive Exploration of Large-scale Time-Series Data

Area of Science:

• Data Visualization
• Computer Science
• Information Visualization

Background:

• Time-series data is crucial across finance, meteorology, health, and urban informatics.
• Interactive exploration of large-scale time-series data is challenging due to visual clutter and latency.
• Existing methods lack efficient support for analyzing numerous time series simultaneously.

Purpose of the Study:

• To develop a novel method for interactive visual analysis of large-scale time-series data.
• To enable clutter-free, low-latency interactions for exploring multiple time series.
• To present KD-Box, an interactive system facilitating efficient time-series data exploration.

Main Methods:

• A novel line-segment-based KD-tree method for efficient data indexing and querying.
• Line splatting technique for rapid density field computation and representative line selection.
• Development of the KD-Box interactive system with features like timebox, attribute filtering, and coordinated multiple views.

Main Results:

• The proposed method enables fast queries over time series in selected regions of interest.
• Line splatting facilitates efficient density field computation and selection of representative lines.
• KD-Box demonstrates effectiveness in supporting efficient line queries and density field computation.

Conclusions:

• The KD-Box system, powered by a line-segment KD-tree, significantly enhances interactive visual analysis of large-scale time-series data.
• The approach provides efficient querying, density estimation, and interactive features for complex datasets.
• Demonstrated usefulness across real-world datasets in finance, meteorology, health, and urban informatics.