Visualization by example

Abstract

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...

Integrated Visualization Editing via Parameterized Declarative Templates

“…Data manipulation tasks, for instance, might be better facilitated by the incorporation of ideas from datafow programming and spreadsheets, as well as programming-by-example techniques to help with data wrangling [17,36,38]. To more fully support presentation tasks, it would be fruitful to extend our combination of modalities to include visual builders [21]-which ofer a variety of direct manipulation features [71,79] for creating chartsand visualization by demonstration [72,73,91,102].…”

AI4VIS: Survey on Artificial Intelligence Approaches for Data Visualization

“…Related to anchor-based generation, design-based generation studies the problem of generating visualizations by injecting the target data to a reference design. This is referred to as style transfer in Harper and Agrawala's approach [37] or visualization-byexample [38]. Another recent example is Retrieve-Then-Adapt [1] that applies pre-defined design templates to user information.…”

StructGraphics: Flexible Visualization Design through Data-Agnostic and Reusable Graphical Structures

“…The approach supports visualization transformation tasks, rather than visualization authoring tasks, and is largely data-driven. Wang et al [52] have recently introduced a visualization-by-example technique that infers a visualization grammar from a partial visual sketch and a dataset. Unfortunately, the technique requires the pre-specification of inference rules for each visualization type (e.g., scatter plot, bar chart, and line chart) and further assumes that the input dataset is well formatted and labeled in accordance to the visualization example.…”