Beyond Heuristics: Learning Visualization Design

Saket, Bahador; Moritz, Dominik; Lin, Halden; Dibia, Victor; Demiralp, Çağatay; Heer, Jeffrey

doi:10.48550/arxiv.1807.06641

Cited by 11 publications

(14 citation statements)

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“…Rather, there appears to be the need for novel approaches which occupy a middle ground between direct demonstration and visual programming interfaces (e.g., Inter-State [40]). Even with selection-based interactions, future work should consider how to go beyond heuristics [50] and utilize recommendation methods including ranked enumeration [32,36,59] and learned models [19]. A key challenge here is that these alternate approaches are grounded in empirically-validated principles for effective visual encoding, and similar results do not yet exist for interaction design.…”

Section: Discussionmentioning

confidence: 99%

Lyra 2: Designing Interactive Visualizations by Demonstration

Zong

Barnwal

Neogy

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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Section: Discussionmentioning

confidence: 99%

Lyra 2: Designing Interactive Visualizations by Demonstration

Zong

Barnwal

Neogy

et al. 2021

IEEE Trans. Visual. Comput. Graphics

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“…This initial set of papers has nine papers [1]- [9]. We further augment these starting points with papers covered in related surveys [20]- [23]. If a paper is selected to be included, we traverse both it references and citations.…”

Section: Methods and Corpusmentioning

confidence: 99%

“…Several surveys review techniques for automating the creation of visualizations. Saket et al [20] discussed the prospect of learning visualization design and classified automated visualization design systems into knowledge-based (i.e., rule-based), data-driven (i.e., machine-learning), and hybrid approaches. This classification was systematically reviewed in a recent survey about visualization and infographics recommendation [21].…”

Section: Related Surveysmentioning

confidence: 99%

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Wang

Shu

et al. 2021

Preprint

Self Cite

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Visualizations themselves have become a data format. Akin to other data formats such as text and images, visualizations are increasingly created, stored, shared, and (re-)used with artificial intelligence (AI) techniques. In this survey, we probe the underlying vision of formalizing visualizations as an emerging data format and review the recent advance in applying AI techniques to visualization data. We define visualization data as the digital representations of visualizations in computers and focus on visualizations in information visualization and visual analytic. We build our survey upon a corpus spanning ten different fields in computer science with an eye toward identifying important common interests. Our resulting taxonomy is organized around WHAT is visualization data and its representation, WHY and HOW to apply AI to visualization data. We highlight a set of common tasks that researchers apply to the visualization data and present a detailed discussion of AI approaches developed to accomplish those tasks. Drawing upon our literature review, we discuss several important research questions surrounding the management and exploitation of visualization data, as well as the role of AI in support of those processes. We make the list of surveyed papers and related material available online at ai4vis.github.io.

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“…Some of these findings have been integrated into the development of visualization authoring tools, e.g., ShowMe [32]. Following Mackinlay's work, there has been an increasing trend of using data-driven models for automated visualization design [42]. Most of these studies aim to learn an optimal mapping from inputs of data attributes and tasks to outputs of visualizations.…”

Section: Related Workmentioning

confidence: 99%

Composition and Configuration Patterns in Multiple-View Visualizations

Chen¹,

Zeng²,

Lin³

et al. 2020

Preprint

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Multiple-view visualization (MV) is a layout design technique often employed to help users see a large number of data attributes and values in a single cohesive representation. Because of its generalizability, the MV design has been widely adopted by the visualization community to help users examine and interact with large, complex, and high-dimensional data. However, although ubiquitous, there has been little work to categorize and analyze MVs in order to better understand its design space. As a result, there has been little to no guideline in how to use the MV design effectively. In this paper, we present an in-depth study of how MVs are designed in practice. We focus on two fundamental measures of multiple-view patterns: composition, which quantifies what view types and how many are there; and configuration, which characterizes spatial arrangement of view layouts in the display space. We build a new dataset containing 360 images of MVs collected from IEEE VIS, EuroVis, and PacificVis publications 2011 to 2019, and make fine-grained annotations of view types and layouts for these visualization images. From this data we conduct composition and configuration analyses using quantitative metrics of term frequency and layout topology. We identify common practices around MVs, including relationship of view types, popular view layouts, and correlation between view types and layouts. We combine the findings into a MV recommendation system, providing interactive tools to explore the design space, and support example-based design.

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Beyond Heuristics: Learning Visualization Design

Cited by 11 publications

References 0 publications

Lyra 2: Designing Interactive Visualizations by Demonstration

Lyra 2: Designing Interactive Visualizations by Demonstration

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

Composition and Configuration Patterns in Multiple-View Visualizations

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