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About this paper
Leonardo Ferreira, Gustavo Moreira, Fabio Miranda
Researchers at the University of Illinois Chicago developed VA-Blueprint, a multi-level hierarchical knowledge base and semi-automated LLM-driven methodology for systematically extracting and organizing the fundamental components and their dependencies from 101 urban Visual Analytics (VA) systems, providing a structured resource for design and development.
Key insights
- VA systems exhibit common architectural patterns, with Visualization (39.7% of granular blocks) and Data Processing (25.5%) being the most prevalent component categories.
- The structural complexity of VA systems has increased over time, with average granular components rising from 13.5 to 15.8 and edges from 21.8 to 30.4 per system between 2007-2024.
- Large Language Models (LLMs) can significantly reduce VA component annotation time from over 18 hours to less than two hours per paper, while achieving an 86% block label match rate after human refinement.
Summary by alphaXiv.
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