Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences (2410.12744v3)

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.

• The paper presents drillboards, a novel technique for constructing dashboards that dynamically adjust visualization detail through drill-down and aggregation methods.
• It employs a formal vocabulary and a suite of aggregation operations to seamlessly transition from detailed charts to summarized views.
• A dedicated tool, DrillVis, empowers experts to build these hierarchies, with empirical studies confirming improved data comprehension for various user skill levels.

Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences

The paper "Drillboards: Adaptive Visualization Dashboards for Dynamic Personalization of Visualization Experiences" introduces a novel technique for constructing adaptive visualization dashboards known as drillboards. This approach is designed to accommodate diverse user needs and levels of expertise by allowing users to drill down or roll up within a hierarchy of coordinated charts.

Drillboards are defined through a formal vocabulary of chart representations and operational rules, enabling the merging of multiple charts into composite representations. This iterative process commences from a baseline dashboard and evolves into a sequence of progressively abstract and simplified views. The root of this hierarchy is a single chart that encapsulates the entire dashboard, permitting users to interactively explore differing levels of detail according to their expertise, interest, and effort level.

Key Findings and Claims

1. Adaptive Hierarchy: The drillboards utilize a hierarchical structure of visualizations, allowing users to manage the level of detail displayed. Users can navigate this hierarchy to tailor their visualization experience, effectively serving a broad spectrum of user expertise.
2. Aggregation Operations: The technique includes specific operations for aggregating charts, such as summarization, archetyping, projection, overlaying, and juxtaposition. These operations enable a seamless transition between abstract and detailed representations, ensuring information is retained and effectively communicated.
3. Authoring Tool: The development of DrillVis, an authoring environment, supports the creation of drillboards. This tool permits expert users to construct visualization hierarchies, enhancing personalization and allowing for the distribution of dashboards to a wide audience.
4. Empirical Evaluation: The authors conducted a user paper involving domain experts who authored drillboards, followed by casual end-users evaluating these visualizations. The paper highlighted the effectiveness of drillboards in facilitating communication and comprehension of complex data, particularly for novice users.

Implications and Future Directions

The introduction of drillboards holds significant implications for the domains of data visualization and human-computer interaction. Practically, the technique provides a robust framework for constructing personalized visualization experiences, beneficial in educational settings, business intelligence, and anytime diverse user demographics are involved. Theoretically, this work broadens the understanding of adaptive visualization interfaces, reinforcing the need for personalization in the increasingly data-driven decision-making environments.

Future research could explore automated methods for generating drillboard hierarchies and extending the range of visualizations that can be incorporated within the drillboard framework. Additionally, the integration of natural language processing could further enhance the adaptive capabilities of drillboards, aligning user queries with the most relevant visualization detail level.

In conclusion, this paper offers a comprehensive approach to adaptive visualization dashboards, balancing complexity with usability, and presenting a compelling case for their application in diverse fields requiring dynamic and personalized data interaction.