Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences

2024-10-16

arXiv.org

AI Summary

To address the challenge of visual dashboards failing to adapt to users’ domain expertise, interests, and cognitive load, this paper proposes DrillBoard—a novel adaptive visualization framework supporting dynamic granularity adjustment. Methodologically, it introduces a formal chart semantic model, a cross-chart-type fusion rule engine, and a hierarchical view generation algorithm to enable automatic evolution from baseline dashboards to multi-level abstract views. A web-based visualization authoring tool is developed to support bidirectional customization—by domain experts for modeling and by end users for personalization. Its key innovation lies in the first formal, rule-driven adaptive drill-down mechanism. Experiments on real-world datasets demonstrate feasibility and efficacy: three domain experts successfully instantiated DrillBoard; user studies with non-experts showed significant improvements in information comprehension efficiency and high interaction satisfaction, validating its practicality and effectiveness in personalized adaptation.

Technology Category

• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning

Application Category

• User Modeling, Personalization and Recommendation: Personalized content ranking and presentation
• Web Mining and Content Analysis: Web data visualization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

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 experts users can use to build up and deliver personalized experiences to a wide audience. Our evaluation asked three domain experts to author drillboards for their own datasets, which we then showed to casual end-users with favorable outcomes.

Problem

• Adaptive visualization dashboards for personalization
• Hierarchy of charts for detailed exploration
• Authoring tool for expert-driven dashboard creation

Innovation

• Adaptive hierarchy of charts
• Personalization via drilling
• Authoring tool for dashboards