Learning to Recommend Visualizations from Data

Qian, X.; Rossi, Ryan A.; Du, Fan; Kim, Sung‐Chul; Koh, Eunyee; Malik, Sana; Lee, Tak Yeon; Chan, Joel

doi:10.1145/3447548.3467224

Cited by 21 publications

(8 citation statements)

References 32 publications

(32 reference statements)

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“…Various researchers have implemented the Likert scale to evaluate data in a visualization system Islam et al 97 The Likert scale is popular in survey research because it allows personality traits or perceptions to be operationalized quickly. For example, in 2020, Qian et al 64 proposed the first end-to-end graphical recommendation system based on ML. They formalized and described a generic learning framework to solve the problem of ML-based visualization recommendations and used trained models to automatically generate, and evaluate a list of recommended views for new data sets which are unknown to arbitrary users.…”

Section: Overview Of Evaluation Methods and Terminology: State-of-the...mentioning

confidence: 99%

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Strategies for evaluating visual analytics systems: A systematic review and new perspectives

Islam,

Akter,

Islam

et al. 2023

Information Visualization

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In recent times, visual analytics systems (VAS) have been used to solve various complex issues in diverse application domains. Nonetheless, an inherent drawback arises from the insufficient evaluation of VAS, resulting in occasional inaccuracies when it comes to analytical reasoning, information synthesis, and deriving insights from vast, ever-changing, ambiguous, and frequently contradictory data. Hence, the significance of implementing an appropriate evaluation methodology cannot be overstated, as it plays a pivotal role in enhancing the design and development of visualization systems. This paper assesses visualization systems by providing a systematic exploration of various evaluation strategies (ES). While several existing studies have examined some ES, the extent of comprehensive and systematic review for visualization research remains limited. In this work, we introduce seven state-of-the-art and widely recognized ES namely (1) dashboard comparison; (2) insight-based evaluation; (3) log data analysis; (4) Likert scales; (5) qualitative and quantitative analysis; (6) Nielsen’s heuristics; and (7) eye trackers. Moreover, it delves into their historical context and explores numerous applications where these ES have been employed, shedding light on the associated evaluation practices. Through our comprehensive review, we overview and analyze the predominant evaluation goals within the visualization community, elucidating their evolution and the inherent contrasts. Additionally, we identify the open challenges that arise with the emergence of new ES, while also highlighting the key themes gleaned from the existing literature that hold potential for further exploration in future studies.

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Section: Overview Of Evaluation Methods and Terminology: State-of-the...mentioning

confidence: 99%

“…Qian et al 64 Likert Scale 7 point Likert scale accurately identified the highest scoring ML-based visualization recommender system according to the human expert's ratings It failed to measure the actual attitudes of respondents as only a few are rated efficiently rather than all users.…”

Section: Advantages Disadvantagesmentioning

confidence: 99%

Strategies for evaluating visual analytics systems: A systematic review and new perspectives

Islam,

Akter,

Islam

et al. 2023

Information Visualization

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“…Visualization recommendation techniques focus on automatically generating proper and desired visualizations for data analysts to explore data and discover insights [67]. Many previous studies adopt rule-based [34,35,40,61] and learning-based [7,20,31,33,38,42] approaches to suggest visual encodings of specified data based on data attributes, tasks, and visual perception theory. Moreover, other work [5,26,32,43,57,60,62] builds interactive systems that utilize visualization recommendations to facilitate data exploration.…”

Section: Visualization Recommendation For Data Analysismentioning

confidence: 99%

Interactive Data Analysis with Next-step Natural Language Query Recommendation

Wang¹,

Cheng²,

Wang³

et al. 2022

Preprint

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“…A corpus of research has been proposed on generating single visualization [19], [37], [42] or multiple-view visualizations [11]. Typically, these studies deal with data table queries, outputting static visualizations.…”

Section: Insights Recommendation In Visual Analyticsmentioning

confidence: 99%

Hyperbolic Personalized Tag Recommendation

Zhao

Zhang

Shang

et al. 2022

Database Systems for Advanced Applications

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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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Learning to Recommend Visualizations from Data

Cited by 21 publications

References 32 publications

Strategies for evaluating visual analytics systems: A systematic review and new perspectives

Strategies for evaluating visual analytics systems: A systematic review and new perspectives

Interactive Data Analysis with Next-step Natural Language Query Recommendation

Hyperbolic Personalized Tag Recommendation

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