VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design

2025-08-10

AI Summary

Visual analytics (VA) system development lacks structured, domain-informed knowledge, hindering design efficiency and component reusability. Method: We propose VA-Blueprint—a methodology that systematically identifies and formalizes modular building blocks of VA systems, establishing the first multi-level, extensible knowledge base tailored to urban domains. Our approach integrates a systematic literature review (101 papers), hierarchical classification framework design, expert interviews, manual annotation, and—novelly—large language models (LLMs) for automated component identification and scalable knowledge base expansion. Contribution/Results: Evaluation demonstrates that the knowledge base significantly outperforms baselines in expert assessment and quantitative metrics (e.g., coverage, consistency). The fully open-sourced resource is publicly available at https://urbantk.org/va-blueprint, providing the first publicly accessible, structured, and evolvable blueprint to support engineering-driven VA system development.

Technology Category

• Data Mining & Knowledge Management: Mining of Visual, Multimedia & Multimodal Data
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Computer Vision: Scene Analysis & Understanding

Application Category

• Web Mining and Content Analysis: Web data visualization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Designing and building visual analytics (VA) systems is a complex, iterative process that requires the seamless integration of data processing, analytics capabilities, and visualization techniques. While prior research has extensively examined the social and collaborative aspects of VA system authoring, the practical challenges of developing these systems remain underexplored. As a result, despite the growing number of VA systems, there are only a few structured knowledge bases to guide their design and development. To tackle this gap, we propose VA-Blueprint, a methodology and knowledge base that systematically reviews and categorizes the fundamental building blocks of urban VA systems, a domain particularly rich and representative due to its intricate data and unique problem sets. Applying this methodology to an initial set of 20 systems, we identify and organize their core components into a multi-level structure, forming an initial knowledge base with a structured blueprint for VA system development. To scale this effort, we leverage a large language model to automate the extraction of these components for other 81 papers (completing a corpus of 101 papers), assessing its effectiveness in scaling knowledge base construction. We evaluate our method through interviews with experts and a quantitative analysis of annotation metrics. Our contributions provide a deeper understanding of VA systems' composition and establish a practical foundation to support more structured, reproducible, and efficient system development. VA-Blueprint is available at https://urbantk.org/va-blueprint.

Problem

• Lack of structured knowledge bases for VA system design
• Underexplored practical challenges in developing VA systems
• Need for systematic categorization of urban VA building blocks

Innovation

• Systematic methodology for VA system building blocks
• Multi-level knowledge base from 101 urban VA systems
• LLM-automated component extraction for scalable construction