Representing Visualization Insights as a Dense Insight Network
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910795421450240 |
|---|---|
| author | Hoffswell, Jane Bursztyn, Victor Soares Guo, Shunan Martinez, Jesse Koh, Eunyee |
| author_facet | Hoffswell, Jane Bursztyn, Victor Soares Guo, Shunan Martinez, Jesse Koh, Eunyee |
| contents | We propose a dense insight network framework to encode the relationships between automatically generated insights from a complex dashboard based on their shared characteristics. Our insight network framework includes five high-level categories of relationships (e.g., type, topic, value, metadata, and compound scores). The goal of this insight network framework is to provide a foundation for implementing new insight interpretation and exploration strategies, including both user-driven and automated approaches. To illustrate the complexity and flexibility of our framework, we first describe a visualization playground to directly visualize key network characteristics; this playground also demonstrates potential interactive capabilities for decomposing the dense insight network. Then, we discuss a case study application for ranking insights based on the underlying network characteristics captured by our framework, before prompting a large language model to generate a concise, natural language summary. Finally, we reflect on next steps for leveraging our insight network framework to design and evaluate new systems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_13309 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Representing Visualization Insights as a Dense Insight Network Hoffswell, Jane Bursztyn, Victor Soares Guo, Shunan Martinez, Jesse Koh, Eunyee Human-Computer Interaction We propose a dense insight network framework to encode the relationships between automatically generated insights from a complex dashboard based on their shared characteristics. Our insight network framework includes five high-level categories of relationships (e.g., type, topic, value, metadata, and compound scores). The goal of this insight network framework is to provide a foundation for implementing new insight interpretation and exploration strategies, including both user-driven and automated approaches. To illustrate the complexity and flexibility of our framework, we first describe a visualization playground to directly visualize key network characteristics; this playground also demonstrates potential interactive capabilities for decomposing the dense insight network. Then, we discuss a case study application for ranking insights based on the underlying network characteristics captured by our framework, before prompting a large language model to generate a concise, natural language summary. Finally, we reflect on next steps for leveraging our insight network framework to design and evaluate new systems. |
| title | Representing Visualization Insights as a Dense Insight Network |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2501.13309 |