monitoring dashboard development

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.

This work addresses the lack of effective evaluation mechanisms for large language models (LLMs) in generating interactive analytical dashboards, which hinders the assessment of their analytical reasoning and interaction quality. The authors propose the first open-ended benchmark for interactive dashboard generation, requiring models to produce both a functional dashboard and a replayable interaction trace. Evaluation is automated through a browser-based executor and a vision-language model (VLM), with the interaction trace serving as a novel basis for judgment. A lightweight, open-source judge model, DashJudge-8B, combined with Bradley-Terry aggregation, enables scalable leaderboard construction. Experiments demonstrate that DashJudge-8B reliably replicates human judgments, that interaction evidence substantially improves evaluation consistency, and that state-of-the-art models still exhibit significant shortcomings in rendering fidelity, analytical depth, and interactive behavior.

This work addresses the labor-intensive nature of presentation tasks—such as formatting and layout—in dashboard authoring, which currently lack support for partial reuse. Through a systematic user study, we characterize the needs and challenges associated with cross-source reuse of visual presentation elements. Building on these insights, we propose a novel paradigm that enables partial reuse of styles and layouts from multiple existing dashboards. We design and implement ReDash, a prototype system embodying this approach, and demonstrate through proof-of-concept experiments that our mechanism effectively overcomes key barriers in common reuse scenarios. The results show a significant improvement in authoring efficiency, confirming the feasibility and practical potential of partial reuse in real-world dashboard creation.

This study addresses the mismatch between assumed and actual user reading behaviors in dashboard design, where existing approaches often presume a fixed component viewing order. Through a mixed-methods investigation involving 18 designers and 16 users, the work systematically identifies and quantifies six key factors influencing interaction sequences: layout, visual salience, semantics, functional role, interactivity, and user context. Integrating interviews, behavioral logs, and sequential analysis, the research uncovers both consistent patterns and diverse strategies in how users navigate dashboards, revealing representative navigation pathways. These findings provide an empirical foundation and actionable design insights for computational modeling of dashboard comprehension and the development of intelligent guidance systems that adapt to real user behavior.

Existing visualization research predominantly focuses on *how to use* interactive features, neglecting the critical question of *how to construct* them. Method: We propose the first three-layer decoupled interaction authoring task model—intent–technique–component—derived from empirical coding and abstraction of 592 interaction units across 47 real-world applications. Contribution/Results: This model provides descriptive, evaluative, and generative capabilities, enabling the first unified formalization of interaction authoring intent, technical implementation, and component instantiation. It yields a reusable, theory-grounded classification framework that supports critical evaluation of existing visualization tools and informs the design and validation of next-generation low-code interaction authoring systems.

Contemporary dashboards suffer from complex interactions and tightly coupled views, necessitating labor-intensive authoring and maintenance of guided tutorials—resulting in high costs and poor synchronization with dashboard updates. To address this, we propose DIANA, the first multimodal dashboard assistant integrating speech, text, and mouse gaze inputs. Built upon large language models (LLMs), DIANA implements a context-aware, real-time interactive system that enables users to issue queries via any combination of modalities and receive immediate visual feedback—including interface element highlighting—and semantically grounded explanations. Its key innovation lies in transcending the conventional unimodal text-based LLM paradigm by enabling synergistic, tri-modal-driven dynamic guidance. DIANA establishes, for the first time in visualization analytics, a closed-loop pipeline spanning multimodal input, interface response, and semantic output. A user study demonstrates that DIANA significantly improves users’ comprehension efficiency of dashboard structure and functionality while markedly reducing reliance on manual guidance.

Existing approaches struggle to reconstruct interactive data dashboards that support functionalities such as clicking and filtering. This work introduces Dashboard2Code, a novel task requiring models to actively explore interactive dashboards and integrate user interaction feedback to generate code that faithfully reproduces the target dashboard. To facilitate research in this direction, we present DashboardMimic, the first benchmark dataset built on Plotly+Dash, along with an automated evaluation framework that combines semantic analysis and dynamic interaction testing. Experimental results on 180 high-quality dashboard–code pairs demonstrate that current models exhibit limited performance on highly complex dashboards, with closed-source models significantly outperforming their open-source counterparts.

Magic: The Gathering—Commander players lack effective tools for match data analysis. Method: This study employs user task analysis, iterative visualization design (including heatmaps and line charts), and structured usability testing to derive dashboard design principles centered on contextual relevance, outcome orientation, and progressive disclosure. It prioritizes adaptability, customizability, and accuracy equally—departing from conventional generic dashboard paradigms. Contribution/Results: Empirical evaluation demonstrates that heatmaps and line charts significantly improve players’ comprehension efficiency of key metrics such as win rate and play tempo. Players strongly prefer localized views, context-driven metrics, and personalized configurations. The study culminates in a domain-specific visualization design guideline for trading card games (TCGs), offering both methodological foundations and empirical validation for context-aware, domain-adapted game analytics tool design.

This work addresses the unreliability of developer productivity dashboards, which often stems from ad hoc scripts that introduce undetected silent data gaps, eroding organizational trust. To resolve this, we propose a robust ELT pipeline grounded in DAG-based orchestration and the Medallion architecture, decoupling data extraction from transformation to preserve the immutability of raw data. Our approach introduces a state-driven dependency scheduling mechanism and, for the first time, treats metric pipelines as production-grade distributed systems. We emphasize the critical role of immutable raw history in enabling reliable metric redefinition. This methodology significantly enhances data reliability and freshness while effectively eliminating silent failures, thereby restoring organizational confidence in DevOps metrics.

This work addresses the state desynchronization between natural language queries and dashboard interactions in multi-step business intelligence (BI) analysis by proposing the first agent-based digital twin framework. The approach couples large language model (LLM) agents with executable dashboard states and reconstructs a shared analytical context through unified interaction logs, thereby ensuring consistency across dialogue, user actions, semantic alignment, and provenance tracking. Innovatively leveraging a digital twin mechanism, the framework enables state-aware analytical summarization and traceable context management. Experimental results demonstrate a 20.0% absolute improvement in exact-match accuracy (from 43.3% to 63.3%), a partial-match accuracy of 70.8%, and a reduced timeout rate of 10.0%. User studies further confirm high task accuracy and a positive interactive experience.