ScagExplorer: Exploring Scatterplots by Their Scagnostics

Dang, Tommy; Wilkinson, Leland

doi:10.1109/pacificvis.2014.42

Cited by 45 publications

(5 citation statements)

References 25 publications

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“…A body of related work proposed techniques for sub-tasks in interactive visualization recommendation systems, such as improving expressiveness or perceptual effectiveness [24], matching user intents [10], displaying trends and outliers [20], etc. These sub-tasks can generally be divided into two categories [18,42]: whether the solution focuses on recommending data (what data to visualize), such as Discovery-driven Data Cubes [33], Scagnostics [39], Auto-Vis [40], AutoPartition [2], Foresight [6], SpotLight [11], ScagExplorer [5], VisPilot [19], VizDeck [28] and MuVE [9] or recommending encoding (how to design and visually encode the data), such as APT [24], ShowMe [25], BDVR [10], SeeDB [38], Draco-learn [26], and LQ 2 [44]. While some of those are ML-based [14,19,26,28], none recommends entire visualizations, and thus does not solve the visualization recommendation problem that lies at the heart of our work.…”

Section: Related Workmentioning

confidence: 99%

Learning to Recommend Visualizations from Data

Qian

Rossi

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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Visualization recommendation is important for exploratory analysis and making sense of the data quickly by automatically recommending relevant visualizations to the user. In this work, we propose the first end-to-end ML-based visualization recommendation system that leverages a large corpus of datasets and their relevant visualizations to learn a visualization recommendation model automatically. Then, given a new unseen dataset from an arbitrary user, the model automatically generates visualizations for that new dataset, derives scores for the visualizations, and outputs a list of recommended visualizations to the user ordered by effectiveness. We also describe an evaluation framework to quantitatively evaluate visualization recommendation models learned from a large corpus of visualizations and datasets. Through quantitative experiments, a user study, and qualitative analysis, we show that our end-to-end ML-based system recommends more effective and useful visualizations compared to existing state-of-the-art rule-based systems. CCS CONCEPTS• Information systems → Recommender systems; • Humancentered computing → Visualization systems and tools.

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Section: Related Workmentioning

confidence: 99%

Learning to Recommend Visualizations from Data

Qian

Rossi

et al. 2021

Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery &Amp; Data Mining

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show abstract

“…All of the existing rule-based [Hu et al 2018;Perry et al 2013;Vartak et al 2017;Wongsuphasawat et al 2015Wongsuphasawat et al , 2017, hybrid [Moritz et al 2018], and pure ML-based visualization recommendation [Qian et al 2020] approaches are unable to recommend personalized visualizations for specific users. These approaches do not model users, but focus entirely on learning or manually defining visualization rules that capture the notion of an effective visualization [Cui et al 2019;Dang and Wilkinson 2014;Demiralp et al 2017;Elzen and Wijk 2013;Key et al 2012;Lee et al 2019a;Mackinlay et al 2007b;Siddiqui et al 2016;Vartak et al 2015;Wilkinson and Wills 2008;Wills and Wilkinson 2010]. Therefore, no matter the user, the model always gives the same recommendations.…”

Section: Visualization Recommendationmentioning

confidence: 99%

Personalized Visualization Recommendation

Qian¹,

Rossi²,

Du³

et al. 2021

Preprint

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Visualization recommendation work has focused solely on scoring visualizations based on the underlying dataset, and not the actual user and their past visualization feedback. These systems recommend the same visualizations for every user, despite that the underlying user interests, intent, and visualization preferences are likely to be fundamentally different, yet vitally important. In this work, we formally introduce the problem of personalized visualization recommendation and present a generic learning framework for solving it. In particular, we focus on recommending visualizations personalized for each individual user based on their past visualization interactions (e.g., viewed, clicked, manually created) along with the data from those visualizations. More importantly, the framework can learn from visualizations relevant to other users, even if the visualizations are generated from completely different datasets. Experiments demonstrate the effectiveness of the approach as it leads to higher quality visualization recommendations tailored to the specific user intent and preferences. To support research on this new problem, we release our user-centric visualization corpus consisting of 17.4k users exploring 94k datasets with 2.3 million attributes and 32k user-generated visualizations.

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“…By scagnostics, it is possible to detect anomalies in the dataset through indices calculated over the visualization of big scatterplot matrices. For example, Dang and Wilkinson 19 worked with scagnostics to find anomalies and similar distribution among the scatterplots of a scatterplot matrix with more than 100 dimensions. However, scagnostics are focused on extraction and deduction of information about a dataset from a visualization, but not on the quantification of the characteristics of the visualization itself.…”

Section: Metrics For Scatterplotsmentioning

confidence: 99%

Prediction of data visibility in two-dimensional scatterplots

Urribarri

Castro

2016

Information Visualization

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The result of a visualization process depends on the user’s decisions along it. With the intention of accelerating this process and guaranteeing an appropriate visualization of the data, we are looking to semi-automatize the process to help the users with the decision-making along it. To contribute to this semi-automation, it is useful to have metrics that characterize different important aspects of the visualization techniques, such as data representation visibility. Besides, scatterplots are a widely used technique to visualize scalar datasets. In this context, this work presents a metric that evaluates data representation visibility considering glyph visibility in scatterplots. We defined a metric that estimates the proportion of glyphs that will be visible regardless of the drawing order, and it depends on the number of items in the dataset, the size of the window, and the size of the glyphs that will represent the data. To define and approximate the metric, we experimented with several random datasets for which both dimensions followed a normal distribution. This metric constitutes an alternative to characterize scatterplots and collaborates in the semi-automation of the user’s decisions along the visualization process.

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ScagExplorer: Exploring Scatterplots by Their Scagnostics

Cited by 45 publications

References 25 publications

Learning to Recommend Visualizations from Data

Learning to Recommend Visualizations from Data

Personalized Visualization Recommendation

Prediction of data visibility in two-dimensional scatterplots

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