Jianu, RaduHutchinson, MaeveAndrienko, NataliaAndrienko, GennadyElshehaly, MaiSlingsby, AidanSheng, YunSlingsby, Aidan2025-09-092025-09-092025978-3-03868-293-6https://doi.org/10.2312/cgvc.20251221https://diglib.eg.org/handle/10.2312/cgvc20251221We explore how large language models (LLMs) can support real-time visual mapping of data analysis workflows. Building on an earlier vision, we investigate if and how LLMs can decompose analytic dialogues into ''analysis maps'' that capture key semantic units such as questions, datasets, tasks, and findings. Using two exemplar analyses, we test both post-hoc and interactive strategies for generating these maps and experiment with prompting techniques for structuring and updating them. Results, documented in Observable notebooks, suggest that LLMs can scaffold analysis-as-network meaningfully-laying the groundwork for user-facing systems and moving beyond purely textual forms of LLM-mediated analysis.Attribution 4.0 International LicenseCCS Concepts: Computing methodologies → Collision detection; Hardware → Sensors and actuators; PCB design and layoutComputing methodologies → Collision detectionHardware → Sensors and actuatorsPCB design and layout10.2312/cgvc.202512214 pages