Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

Moritz, Dominik; Wang, Chenglong; Nelson, Greg L.; Lin, Halden; Smith, Adam M.; Howe, Bill; Heer, Jeffrey

doi:10.1109/tvcg.2018.2865240

Cited by 249 publications

(222 citation statements)

References 51 publications

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“…As more work is done to explore and test new visualization designs, GEViT will incorporate these designs, potentially resulting in the addition of new typological terms. It will also be interesting to explore how GEViT might be used to suggest visualizations to researchers, as is currently done with common statistical charts in tools like Tableau's "Show Me" feature (Mackinlay et al, 2007), Google Sheets' "Chart Suggestions", or in novel systems like Draco (Moritz, 2018).…”

Section: Implications Of Our Findings For Visualization Designmentioning

confidence: 99%

A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT

Crisan

Gardy

Munzner

2018

Preprint

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Motivation: Data visualization is an important tool for exploring and communicating findings from genomic and healthcare datasets. Yet, without a systematic way of organizing and describing the design space of data visualizations, researchers may not be aware of the breadth of possible visualization design choices or how to distinguish between good and bad options. Results:We have developed a method that systematically surveys data visualizations using the analysis of both text and images. Our method supports the construction of a visualization design space that is explorable along two axes: why the visualization was created and how it was constructed. We applied our method to a corpus of scientific research articles from infectious disease genomic epidemiology and derived a Genomic Epidemiology Visualization Typology (GEViT) that describes how visualizations were created from a series of chart types, combinations, and enhancements. We have also implemented an online gallery that allows others to explore our resulting design space of visualizations. Our results have important implications for visualization design and for researchers intending to develop or use data visualization tools.Finally, the method that we introduce is extensible to constructing visualizations design spaces across other research areas.Availability: Our browsable gallery is available at http://gevit.net and all project code can be found at https://github.com/amcrisan/gevitAnalysisRelease

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Section: Implications Of Our Findings For Visualization Designmentioning

confidence: 99%

A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT

Crisan

Gardy

Munzner

2018

Preprint

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“…These efforts include both visualization recommendation systems as well as visualization exploration tools. Among these, visualization recommendation systems like Draco [Moritz et al 2019], CompassQL [Wongsuphasawat et al 2016a], and ShowMe [Mackinlay et al 2007] recommend top completions of an incomplete visualization program. On the other hand, visualization exploration tools, such as VisExamplar [Saket et al 2017b], Visualization-by-Sketching [Schroeder and Keefe 2016], Polaris [Stolte et al 2008], and Voyager [Wongsuphasawat et al 2016b, aim to generate diverse visualizations based on user demonstrations, which can include graphical sketches, manipulation trajectories, and constraints.…”

Section: Related Workmentioning

confidence: 99%

Visualization by example

Wang

Bodík

et al. 2019

Proc. ACM Program. Lang.

Self Cite

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While visualizations play a crucial role in gaining insights from data, generating useful visualizations from a complex dataset is far from an easy task. In particular, besides understanding the functionality provided by existing visualization libraries, generating the desired visualization also requires reshaping and aggregating the underlying data as well as composing different visual elements to achieve the intended visual narrative. This paper aims to simplify visualization tasks by automatically synthesizing the required program from simple visual sketches provided by the user. Specifically, given an input data set and a visual sketch that demonstrates how to visualize a very small subset of this data, our technique automatically generates a program that can be used to visualize the entire data set.From a program synthesis perspective, automating visualization tasks poses several challenges that are not addressed by prior techniques. First, because many visualization tasks require data wrangling in addition to generating plots from a given table, we need to decompose the end-to-end synthesis task into two separate sub-problems. Second, because the intermediate specification that results from the decomposition is necessarily imprecise, this makes the data wrangling task particularly challenging in our context. In this paper, we address these problems by developing a new compositional visualization-by-example technique that (a) decomposes the end-to-end task into two different synthesis problems over different DSLs and (b) leverages bi-directional program analysis to deal with the complexity that arises from having an imprecise intermediate specification.We have implemented our visualization-by-example approach in a tool called Viser and evaluate it on 83 visualization tasks collected from on-line forums and tutorials. Viser can solve 84% of these benchmarks within a 600 second time limit, and, for those tasks that can be solved, the desired visualization is among the top-5 generated by Viser in 70% of the cases. building programming systems like D3 [Bostock et al. 2011] and Vega-Lite [Satyanarayan et al. 2017] to further facilitate real-world visualization tasks.Despite all these recent efforts, data visualization still remains a challenging task that requires considerable expertise -in fact, so much so that some companies even have job titles like "data visualization expert" or "data visualization specialist." Generally speaking, there are three key reasons that make data visualization a challenging task. First, beyond having a good insight about how the data can be best visualized, one needs to have sufficient knowledge about how to use the relevant visualization libraries. Second, different visualization primitives typically require the data to be in different formats; so, in order to experiment with different types of visualizations, one needs to constantly reshape the data into different formats. Finally, generating the intended visualization typically requires modifications to the original dataset, incl...

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“…Voyager extends prior work by automatic generation of a diverse set of visualizations [38]. Draco supports the design of visualizations by encapsulating design knowledge as constraints [30]. Kim and Heer [24] consider analysis tasks to recommend effective visual encoding for automated visualization design.…”

Section: Visualization Recommendation Systemsmentioning

confidence: 99%

Task-Oriented Optimal Sequencing of Visualization Charts

Shi

et al. 2019

2019 IEEE Visualization in Data Science (VDS)

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Fig. 1. A case study based on the optimal task-oriented charts sequencing technique. This figure illustrates the results of an expert user exploring the Cars dataset starting from a bar chart (the first chart on the left) to find out the clusters of cars having the same number of cylinders. During the analysis, our technique recommends proper actions to guide the exploration process to approach the analysis goal. After a series of operations, the user finally found that cars are clustered in a subspace as shown in the last view, which indicates that fewer cylinders corresponds to smaller displacement and the lower horsepower.Abstract-A chart sequence is used to describe a series of visualization charts generated in the exploratory analysis by data analysts. It provides information details in each chart as well as a logical relationship among charts. While existing research targets on generating chart sequences that match human's perceptions, little attention has been paid to formulate task-oriented connections between charts in a chart design space. We present a novel chart sequencing method based on reinforcement learning to capture the connections between charts in the context of three major analysis tasks, including correlation analysis, anomaly detection, and cluster analysis. The proposed method formulates a chart sequencing procedure as an optimization problem, which seeks an optimal policy to sequencing charts for the specific analysis task. In our method, a novel reward function is introduced, which takes both the analysis task and the factor of human cognition into consideration. We conducted one case study and two user studies to evaluate the effectiveness of our method under the application scenarios of visualization demonstration, sequencing charts for reasoning analysis results, and making a chart design choice. The study results showed the power of our method.

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Formalizing Visualization Design Knowledge as Constraints: Actionable and Extensible Models in Draco

Cited by 249 publications

References 51 publications

A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT

A systematic method for surveying data visualizations and a resulting genomic epidemiology visualization typology: GEViT

Visualization by example

Task-Oriented Optimal Sequencing of Visualization Charts

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