DMiner: Dashboard Design Mining and Recommendation

Lin, Yanna; Li, Haotian; Wu, Aoyu; Wang, Yong; Qu, Huamin

doi:10.1109/tvcg.2023.3251344

Cited by 12 publications

(4 citation statements)

References 49 publications

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“…Specification for VA systems: In the extraction of the declarative grammar required in LEVA, we refer to previous work describing basic charts [30] and adding descriptions of the data table, user interaction, and coordination based on the goal of understanding data, view and insight recommendation. An abstractlevel description of data and functionality for VA specifications can further benefit various downstream tasks.…”

Section: Discussionmentioning

confidence: 99%

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Zhao

Zhang

Shang

et al. 2022

Lecture Notes in Computer Science

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Visual analytics supports data analysis tasks within complex domain problems. However, due to the richness of data types, visual designs, and interaction designs, users need to recall and process a significant amount of information when they visually analyze data. These challenges emphasize the need for more intelligent visual analytics methods. Large language models have demonstrated the ability to interpret various forms of textual data, offering the potential to facilitate intelligent support for visual analytics. We propose LEVA, a framework that uses large language models to enhance users' VA workflows at multiple stages: onboarding, exploration, and summarization. To support onboarding, we use large language models to interpret visualization designs and view relationships based on system specifications. For exploration, we use large language models to recommend insights based on the analysis of system status and data to facilitate mixed-initiative exploration. For summarization, we present a selective reporting strategy to retrace analysis history through a stream visualization and generate insight reports with the help of large language models. We demonstrate how LEVA can be integrated into existing visual analytics systems. Two usage scenarios and a user study suggest that LEVA effectively aids users in conducting visual analytics.

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Section: Discussionmentioning

confidence: 99%

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Zhao

Zhang

Shang

et al. 2022

Lecture Notes in Computer Science

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“…Considering the importance of interactions in visual analysis [66], PI 1 recommends interactive visualizations when users query data from databases [68]. Informed by the advantages of multi-view visualizations over single-view charts [30], PI 2 [8] extends PI 1 by generating multi-view interactive visualizations. Besides recommending visualizations, EDAssistant [29] and LodeStar [38] suggest code snippets for data exploration.…”

Section: Visual Data Analysis With Computational Notebooksmentioning

confidence: 99%

Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks

Lü

Zhang

et al. 2023

Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

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Computational notebooks are widely used for data analysis. Their interleaved displays of code and execution results (e.g., visualizations) are welcomed since they enable iterative analysis and preserve the exploration process. However, the communication of data findings remains challenging in computational notebooks. Users have to carefully identify useful findings from useless ones, document them with texts and visual embellishments, and then organize them in different tools. Such workflow greatly increases their workload, according to our interviews with practitioners. To address the challenge, we designed Notable to offer on-the-fly assistance for

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“…The numerous high-level books, guidelines, and principles around dashboard design are also relevant (e.g., [27,95,98]), as are frameworks of user goals or intents that may help to guide visualization design (e.g., [46,49]), and design tools considered to support cognition [93]. More recently, Lin et al [51] introduced a data-driven approach for identifying a set of dashboard design rules from dashboards mined from the web. The rules describe view-wise relationships in terms of data, encoding, layout, and interactions and subsequently develop a recommender for dashboard design.…”

Section: Heuristics In Hci and Visualizationmentioning

confidence: 99%

Heuristics for Supporting Cooperative Dashboard Design

Setlur,

Correll,

Satyanarayan

et al. 2023

IEEE Trans. Visual. Comput. Graphics

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Fig. 1: Successes and failures of cooperative dashboard design throughout the five analytic states of a conversation (a-e). Cooperative dashboards guide users through their data and, in contrast to static dashboards, provide bi-directional communication through interactivity to allow the user to change or refine their analytical goals, switch between topics of interest and levels of detail, correct or update the system if it provides irrelevant or incorrect information, and provide useful summaries of analytical actions. Note that these conversation states are not necessarily sequential and the analyst can move between these various states.

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DMiner: Dashboard Design Mining and Recommendation

Cited by 12 publications

References 49 publications

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Notable: On-the-fly Assistant for Data Storytelling in Computational Notebooks

Heuristics for Supporting Cooperative Dashboard Design

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