aws quicksight

Existing visualization tools commonly embed data loading and transformation logic within visualization components, leading to redundant development efforts, high user learning costs, and difficulties in cross-tool data interoperability. Method: This paper proposes a modular architecture that systematically decouples data processing from visualization logic for the first time. It defines standardized data interfaces and a dynamic integration mechanism, implemented as a web-based prototype supporting bidirectional data flow and parallel collaboration across heterogeneous tools while preserving their autonomy. Contribution/Results: The architecture establishes a unified data interaction layer without compromising tool independence. Empirical evaluation demonstrates significant reductions in developers’ reimplementing overhead and users’ learning curves. Moreover, it enables scalable, open, and collaborative visualization ecosystems—introducing a novel, extensible paradigm for integrated visual analytics.

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.

To address the dual challenges of insufficient personalization and low efficiency in interactive exploration for automated insight discovery, this paper proposes InsightMap—a map-metaphor-based framework for insight visualization and hybrid discovery. Methodologically, it formalizes data insights as measurable, layout-aware data objects; introduces a similarity metric integrating semantic and statistical features; and establishes a hybrid paradigm that synergistically combines automated mining with interactive exploration. InsightMap enables seamless transitions from global overviews to localized deep-dive analysis. Through multiple case studies and user experiments, InsightMap reduces average task completion time by 37% and achieves a user satisfaction score of 4.8/5.0, demonstrating significant improvements in both insight discovery efficiency and personalized adaptability.

To address the challenge of maintaining semantic consistency while enabling dynamic schema evolution in silver-layer modeling within data lakehouse environments, this paper proposes Hub-Star generalized modeling—a framework that abstracts the star schema paradigm into a unified, source-agnostic silver-layer modeling approach for heterogeneous, multi-source data. It supports incremental development and continuous schema evolution. Built on the Databricks platform, the method integrates Delta Lake’s ACID transaction capabilities, SQL-based declarative modeling, and metadata-driven automated transformations to ensure end-to-end reproducible modeling. Evaluated on a retail-org dataset, Hub-Star improves modeling efficiency by 40% and reduces schema change response time to the hour level. The implementation is open-sourced.

This work addresses the challenge that existing tools struggle to automatically generate insight videos from raw tabular data that simultaneously exhibit strong narrative structure, engaging animation, and high data fidelity. The authors propose an end-to-end interactive system that transforms tabular data and natural language queries into narrated videos featuring dynamic visualizations, spoken explanations, and synchronized animations. Key innovations include a declarative specification language, DVSpec, to ensure data faithfulness; a generate-and-orchestrate multi-agent architecture to manage the combinatorial explosion of the design space; and a structured provenance mechanism enabling exploratory, interactive question answering. Evaluation on 109 real-world examples demonstrates that the system efficiently produces high-quality, interactive, and data-accurate narrative videos.

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.

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 study addresses the cognitive bias in scatterplots where “data-induced grouping”—arising from the interplay between data values and visual encoding—leads users to misinterpret spatial arrangements as meaningful patterns. Through two user studies, the authors systematically demonstrate the prevalence of this phenomenon, develop the first perceptual model capable of predicting whether users perceive a given set of points as a coherent group, and propose a visualization intervention strategy that integrates user perception with data reordering. Notably, the model effectively captures users’ tendency to group points based on trends even in nominal data contexts. Applied to visualization diagnosis and optimization, this approach significantly enhances the accuracy and reliability of graphical representations.

Contemporary BI dashboards lack a structured, iterative optimization framework, hindering their evolution from exploratory tools to robust decision-support systems. Method: This study proposes a feedback-driven, gap-analysis–informed four-stage iterative methodology, integrating a six-element data narrative framework—encompassing goals, context, insights, evidence, actions, and impact—and implements it in Power BI via DAX metric optimization and collaborative peer review. Contribution/Results: The framework demonstrably enhances narrative coherence and explanatory power. Empirical application uncovered critical issues: significantly lower gross margin for furniture (6.94% vs. 13.99% for technology), profitability erosion beyond a 20% discount threshold, and $1.35M in unrecovered freight costs—substantially improving decision accuracy. This work makes the first contribution of embedding structured narrative design directly into the BI dashboard iteration lifecycle, yielding a reusable, methodologically grounded framework.

To address systemic inefficiencies—including resource idleness, redundant data transfers, and violations of data locality—arising from misaligned coordination between the PanDA workflow system and the Rucio data management system in the ATLAS experiment, this paper proposes an end-to-end co-optimization framework. We introduce a novel file-level metadata matching algorithm to precisely associate computing tasks with datasets, and integrate log-based tracing, spatiotemporal imbalance analysis, and anomaly pattern detection to construct a fine-grained, holistic view of data access and movement. Our approach is the first to identify, in production, the root causes of cross-system scheduling mismatches, delivering interpretable performance insights. Empirical validation confirms tangible improvements in resource utilization and system resilience, demonstrating the feasibility and effectiveness of the proposed co-design strategies.