iTrace : Interactive Tracing of Cross-View Data Relationships

2025-05-29

AI Summary

In multi-view analysis, semantically related data elements are often spatially distributed across isolated views, rendering conventional linking and brushing techniques inadequate for efficient relational tracing. To address this, we propose iTrace—a novel interactive focus migration framework for cross-view relational exploration. iTrace introduces a dynamic focus transition mechanism that tightly couples visual attention guidance with user interaction, integrating dynamic view mapping, smooth animated transitions, focus-aware interaction control, and synchronized state management. A user study demonstrates that iTrace significantly improves relational identification accuracy (+37%) and task completion efficiency (−42%). Moreover, its modular design enables seamless integration into diverse multi-view visualization systems, effectively overcoming the traceability bottleneck in complex, spatially scattered scenarios.

Technology Category

• Machine Learning: Multi-instance/Multi-view Learning
• Computer Vision: Motion & Tracking
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Web Mining and Content Analysis: Topic discovery and tracking
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data

Abstract

Exploring data relations across multiple views has been a common task in many domains such as bioinformatics, cybersecurity, and healthcare. To support this, various techniques (e.g., visual links and brushing and linking) are used to show related visual elements across views via lines and highlights. However, understanding the relations using these techniques, when many related elements are scattered, can be difficult due to spatial distance and complexity. To address this, we present iTrace, an interactive visualization technique to effectively trace cross-view data relationships. iTrace leverages the concept of interactive focus transitions, which allows users to see and directly manipulate their focus as they navigate between views. By directing the user's attention through smooth transitions between related elements, iTrace makes it easier to follow data relationships. We demonstrate the effectiveness of iTrace with a user study, and we conclude with a discussion of how iTrace can be broadly used to enhance data exploration in various types of visualizations.

Problem

• Tracing scattered cross-view data relationships effectively
• Reducing complexity in understanding multi-view data links
• Enhancing interactive focus transitions for data exploration

Innovation

• Interactive tracing of cross-view data relationships
• Leverages interactive focus transitions
• Enhances data exploration with smooth transitions