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.

Purpose of the Study:

• To develop a novel method for efficient interactive analysis of numerous time series.
• To enable clutter-free visual representations and low-latency interactions for large time-series datasets.

Main Methods:

• A novel line-segment-based KD-tree method for efficient time-series querying.
• A line splatting technique for rapid density field computation and representative line selection.
• Development of KD-Box, an interactive system with features like timebox and attribute filtering.

Main Results:

• The proposed method facilitates fast queries within selected regions of interest.
• Efficient computation of density fields and selection of representative lines are achieved.
• Quantitative comparisons demonstrate the effectiveness of KD-Box for line query and density field computation.

Conclusions:

• KD-Box provides an effective solution for interactive visual analysis of large-scale time-series data.
• The novel KD-tree approach enhances efficiency in querying and density estimation.
• The system proves useful for real-world datasets in various domains.