Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences
Sungbok Shin, Inyoup Na, Niklas Elmqvist · 2025 · 3 citationsRead the paper
We present drillboards, a technique for adaptive visualization dashboards consisting of a hierarchy of coordinated charts that the user can drill down to reach a desired level of detail depending on their expertise, interest, and desired effort. This functionality allows different users to personalize the same dashboard to their specific needs and expertise. The technique is based on a formal vocabulary of chart representations and rules for merging multiple charts of different types and data into single composite representations. The drillboard hierarchy is created by iteratively applying these rules starting from a baseline dashboard, with each consecutive operation yielding a new dashboard with fewer charts and progressively more abstract and simplified views. We also present an authoring tool for building drillboards and show how it can be applied to an agricultural dataset with hundreds of expert users. Our evaluation asked three domain experts to author drillboards for their own datasets, which we then showed to casual end-users with favorable outcomes.
What the full text shows
Working prototype- Casual end-users adapted quickly to the interface and answered questions correctly in under 10 minutes. — < 10 minutes (10 casual end-users interacting with expert-authored drillboards)
- Domain experts successfully reduced complex dashboards into simplified hierarchies for novices. — P1: 96 leaf charts reduced to 8 (novice) and 26 (expert); P2: 32 leaf charts reduced to 6 (novice) and 11 (expert); P3: 9 leaf charts reduced to 3 (novice) and 9 (expert) (3 domain experts using the DrillVis authoring tool)
- End-users preferred 'novice mode' (higher abstraction) when dealing with unfamiliar or large datasets to better understand author intent. (10 casual end-users)
- Setup
- 3 domain experts (financial officer, government deputy director, statistics professor) and 10 casual end-users; datasets included financial assistance, workforce shortages, and regional household data.
- How
- The researchers developed DrillVis, an authoring tool based on a formal vocabulary of chart representations and merging rules (select, summarize, overlay, add, subtract, divide). They conducted a four-phase study where domain experts authored drillboards from their own datasets, which were then evaluated by casual end-users.
- Limitations
- Lack of visual stability during expansion/collapse operations can disrupt the user's mental map. · Aggregation models are not universally applicable across all chart types (e.g., treemaps or node-link diagrams) without modification.
Summarized by Gemma 4 from the open-access full text · 8 Oct 2026. Read it