Interactive Exploration of Large-scale Streamlines of Vector Fields via a Curve Segment Neighborhood Graph

2026-04-15

AI Summary

This work addresses the lack of a rigorous definition for coherent structures in large-scale streamline data, which hinders efficient interactive exploration. The authors propose a web-based interactive system whose core innovation lies in constructing a Curve Segment Neighborhood Graph (CSNG) to encode adjacency relationships among streamline segments. By integrating rapid community detection with an enhanced force-directed layout, the system enables multi-scale structural discovery. Leveraging adjacency matrix compression and parallel processing techniques, it achieves real-time, in-browser interactivity on datasets comprising hundreds of thousands of streamline segments, effectively revealing spatial clusters and coherent patterns within complex vector fields.

Technology Category

• Data Mining & Knowledge Management: Data Stream Mining
• Computer Vision: Segmentation
• Machine Learning: Learning with Manifolds

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Web data visualization
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

Abstract

Streamlines have been widely used to represent and analyze various steady vector fields. To sufficiently represent important features in complex vector fields (like flow), a large number of streamlines are required. Due to the lack of a rigorous definition of features or patterns in streamlines, user interaction and exploration are required to achieve effective interpretation. Existing approaches based on clustering or pattern search, while valuable for specific analysis tasks, often face challenges in supporting interactive and level-of-detail exploration of large-scale curve-based data, particularly when real-time parameter adjustment and iterative refinement are needed. To address this, we design and implement an interactive web-based system. Our system utilizes a Curve Segment Neighborhood Graph (CSNG) to encode the neighboring relationships between curve segments. CSNG enables us to adapt a fast community detection algorithm to identify coherent flow structures and spatial groupings in the streamlines interactively. CSNG also supports a multi-level exploration through an enhanced force-directed layout. Furthermore, our system integrates an adjacency matrix representation to reveal detailed inter-relations among segments. To achieve real-time performance within a web browser, our system employs matrix compression for memory-efficient CSNG storage and parallel processing. We have applied our system to analyze and interpret complex patterns in several streamline datasets. Our experiments show that we achieve real-time performance on datasets with hundreds of thousands of segments.

Problem

• streamlines
• interactive exploration
• large-scale data
• vector fields
• feature interpretation

Innovation

• Curve Segment Neighborhood Graph
• interactive exploration
• community detection
• matrix compression
• force-directed layout