Towards a general-purpose query language for visualization recommendation

Wongsuphasawat, Kanit; Moritz, Dominik; Anand, Anushka; Mackinlay, Jock D.; Howe, Bill; Heer, Jeffrey

doi:10.1145/2939502.2939506

Cited by 98 publications

(80 citation statements)

References 17 publications

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“…Our training dataset is constructed from 4300 Vega-Lite visualizations examples, based on 11 distinct datasets. The examples were originally compiled by [52] where the authors use the CompassQL [76] recommendation engine within Voyager2 [77] to generate charts with 1-3 variables, filtered to remove problematic instances. These charts are generated based on heuristics and rules which enumerate, cluster and rank visualizations according to data properties and perceptual principles [77].…”

Section: Data and Preprocessingmentioning

confidence: 99%

Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks

Dibia

Demiralp²

2019

IEEE Comput. Grap. Appl.

135

120

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Rapidly creating effective visualizations using expressive grammars is challenging for users who have limited time and limited skills in statistics and data visualization. Even high-level, dedicated visualization tools often require users to manually select among data attributes, decide which transformations to apply, and specify mappings between visual encoding variables and raw or transformed attributes. In this paper we introduce Data2Vis, an end-to-end trainable neural translation model for automatically generating visualizations from given datasets. We formulate visualization generation as a language translation problem where data specifications are mapped to visualization specifications in a declarative language (Vega-Lite). To this end, we train a multilayered attention-based encoder-decoder network with long short-term memory (LSTM) units on a corpus of visualization specifications. Qualitative results show that our model learns the vocabulary and syntax for a valid visualization specification, appropriate transformations (count, bins, mean) and how to use common data selection patterns that occur within data visualizations. Data2Vis generates visualizations that are comparable to manually-created visualizations in a fraction of the time, with potential to learn more complex visualization strategies at scale.

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Section: Data and Preprocessingmentioning

confidence: 99%

Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks

Dibia

Demiralp²

2019

IEEE Comput. Grap. Appl.

135

120

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“…For example, Mackinlay [46] developed an application-independent tool that uses expressiveness and effectiveness principles to automatically build effective visualizations. Wongsuphasawat et al [84][85][86] created Voyager, a system that uses both manual and automated chart specification to support data analysis. Draco [54] uses a set of constraints derived from empirical studies in visualizations to formalize visualization design knowledge.…”

Section: Automated Design and Design Mining In Visualizationmentioning

confidence: 99%

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Smart

Szafir

2020

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Our approach uses design mining and unsupervised clustering techniques to produce automatically generated color ramps that capture designer practices. The four choropleth maps shown above utilize color ramps generated from our approach. Developers select a single guiding seed color, shown in the squares below each map, to generate a ramp. We then fit curves capturing structural patterns in designer practices in CIELAB (bottom) to these seed colors to generate ramps (middle, seed colors indicated by a black dot). We embody this technique in Color Crafter, a web-based tool that enables designers of all ability levels to generate high-quality custom color ramps.Abstract-Visualizations often encode numeric data using sequential and diverging color ramps. Effective ramps use colors that are sufficiently discriminable, align well with the data, and are aesthetically pleasing. Designers rely on years of experience to create high-quality color ramps. However, it is challenging for novice visualization developers that lack this experience to craft effective ramps as most guidelines for constructing ramps are loosely defined qualitative heuristics that are often difficult to apply. Our goal is to enable visualization developers to readily create effective color encodings using a single seed color. We do this using an algorithmic approach that models designer practices by analyzing patterns in the structure of designer-crafted color ramps. We construct these models from a corpus of 222 expert-designed color ramps, and use the results to automatically generate ramps that mimic designer practices. We evaluate our approach through an empirical study comparing the outputs of our approach with designer-crafted color ramps. Our models produce ramps that support accurate and aesthetically pleasing visualizations at least as well as designer ramps and that outperform conventional mathematical approaches.

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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.

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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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Towards a general-purpose query language for visualization recommendation

Cited by 98 publications

References 17 publications

Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks

Data2Vis: Automatic Generation of Data Visualizations Using Sequence-to-Sequence Recurrent Neural Networks

Color Crafting: Automating the Construction of Designer Quality Color Ramps

Visualization by example

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