← 研踪 PaperTrailarXiv:2611.11542 · v1
Dashboard Remixing: Goal-oriented Organization and Enrichment of Visualizations and Insights
首次提交 2026-11-09 02:29
版本更新 2026-11-09 02:29
雷达观测 2026-11-10 13:19
摘要
Dashboard creation requires iteratively authoring multiple views and organizing them into a structured format. However, creators often struggle to step back and assess their own work for organization and refinement due to cognitive limitations. In this work, we propose a pipeline to provide computational support for self-reflection during the design process. Our pipeline automatically infers analytical objectives, discovers implicit patterns, and structures those patterns to surface coherent analytical pathways. It further enriches existing dashboard designs by generating complementary visualizations and aggregating natural language insights based on relationships across data entities (e.g., contrasting, drill-down, and relatedness) to reveal chart connections. Our rule-based pipeline provides objective, verifiable feedback, offering an external perspective while enabling creators to tell their own data stories. Four case studies and six expert interviews demonstrate how this approach helps creators reflect on organizational alternatives and surface hidden analytical opportunities in a principled way, reducing the cognitive burden while encouraging deeper analytical exploration.
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v1 · 2026-11-09 02:29 · 最新观测版本
Dashboard Remixing: Goal-oriented Organization and Enrichment of Visualizations and Insights
Dashboard creation requires iteratively authoring multiple views and organizing them into a structured format. However, creators often struggle to step back and assess their own work for organization and refinement due to cognitive limitations. In this work, we propose a pipeline to provide computational support for self-reflection during the design process. Our pipeline automatically infers analytical objectives, discovers implicit patterns, and structures those patterns to surface coherent analytical pathways. It further enriches existing dashboard designs by generating complementary visualizations and aggregating natural language insights based on relationships across data entities (e.g., contrasting, drill-down, and relatedness) to reveal chart connections. Our rule-based pipeline provides objective, verifiable feedback, offering an external perspective while enabling creators to tell their own data stories. Four case studies and six expert interviews demonstrate how this approach helps creators reflect on organizational alternatives and surface hidden analytical opportunities in a principled way, reducing the cognitive burden while encouraging deeper analytical exploration.
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