Living dashboards update views automatically based on user interactions
Living Dashboards: Automatically Self-Updating Visualization Dashboards
Human-Computer Interaction
Summary
Traditional data dashboards show fixed charts that users cannot easily change to explore new questions. The authors present living dashboards that automatically adjust which views are visible by watching how users interact and what questions they ask in natural language. Views that are ignored gently disappear, while those reused can come back automatically, reducing manual changes. They built a web prototype and found users performed better and found it easier to use than a smart but static dashboard.
What this means in practice
- •For business intelligence teams: Create self-updating dashboards that highlight relevant views based on how analysts explore data and ask questions.
- •For software developers building data tools: Implement adaptive visualization components that automatically manage view lifecycles in response to user interactions and queries.
Authors
Mingyu An, Heyon Jeon, Sungbok Shin, Jinwook Seo, Eduard Gröller, Niklas Elmqvist, Vaishali Dhanoa
Abstract
Visualization dashboards are widely used interactive tools, but a disconnect exists between the dynamic data they display and their static structure. End-users cannot modify the dashboard to answer new questions. We introduce Living Dashboards, whose views are born, wither, revive, and die in response to how they are used. Rather than requiring manual reconfiguration, a living dashboard observes interaction and natural-language queries to autonomously wither neglected views and revive those used again. More consequential decisions, such as adding or retiring views, are deferred to the user. We formalize the concept as a four-dimensional design space and implement it in Living Dashboard, a web-based prototype. We evaluate it in an exploratory between-subjects study (N = 12) against an AI-supported baseline on analytical tasks. Living Dashboard participants answered more tasks correctly, reported lower workload, and rated the system higher on usability, though the two conditions differed in more than adaptive behavior alone.