Recent work has shown that when both the chart and caption emphasize the same aspects of the data... more Recent work has shown that when both the chart and caption emphasize the same aspects of the data, readers tend to remember the doubly-emphasized features as takeaways; when there is a mismatch, readers rely on the chart to form takeaways and can miss information in the caption text. Through a survey of 280 chart-caption pairs in real-world sources (e.g., news media, poll reports, government reports, academic articles, and Tableau Public), we find that captions often do not emphasize the same information in practice, which could limit how effectively readers take away the authors' intended messages. Motivated by the survey findings, we present EMPHASISCHECKER, an interactive tool that highlights visually prominent chart features as well as the features emphasized by the caption text along with any mismatches in the emphasis. The tool implements a time-series prominent feature detector based on the Ramer-Douglas-Peucker algorithm and a text reference extractor that identifies time references and data descriptions in the caption and matches them with chart data. This information enables authors to compare features emphasized by these two modalities, quickly see mismatches, and make necessary revisions. A user study confirms that our tool is both useful and easy to use when authoring charts and captions.
Fig. 1: Successes and failures of cooperative dashboard design throughout the five analytic state... more Fig. 1: Successes and failures of cooperative dashboard design throughout the five analytic states of a conversation (a-e). Cooperative dashboards guide users through their data and, in contrast to static dashboards, provide bi-directional communication through interactivity to allow the user to change or refine their analytical goals, switch between topics of interest and levels of detail, correct or update the system if it provides irrelevant or incorrect information, and provide useful summaries of analytical actions. Note that these conversation states are not necessarily sequential and the analyst can move between these various states.
Figure 1: Screenshots of Sentifiers showing the interpretation of vague intent modifiers using se... more Figure 1: Screenshots of Sentifiers showing the interpretation of vague intent modifiers using sentiment analysis and word cooccurrence. Interactive text is displayed with the ability to adjust the ranges using slider widgets. (a) For a dataset of earthquakes in the US [38], the system associates the vague modifier "unsafe" with the data attribute magnitude. Similar negative sentiment polarities (shown in red) result in a top N filter of magnitude 6 and higher to be applied. (b) A dataset showing the health and wealth of nations [20]. Here, the modifier "struggling" has a negative sentiment, while the incomePerCapita and lifeExpectancy attributes have positive sentiments shown in blue. The diverging sentiment polarities result in Bottom N filters applied.
Users often engage in tasks that span multiple personal devices. Although many current solutions ... more Users often engage in tasks that span multiple personal devices. Although many current solutions exist to provide ubiquitous access to one's data, users continue to struggle with cross-device tasks. These solutions often require them to plan ahead for their information needs. In this paper, we present Myngle, a device-agnostic system that lets users quickly find the information they are looking
Figure 1: Different explicit link representations for GraphTiles. (a) text: nodes with the same n... more Figure 1: Different explicit link representations for GraphTiles. (a) text: nodes with the same name are linked. (b) color: nodes with the same color are linked. (c) connectedness: nodes with lines between them are linked. (d) proximity: nodes at/containing the same vertical position are linked. (e) texture: nodes with/containing the same image are linked.
IEEE Transactions on Visualization and Computer Graphics
Fig. 1: Successes and failures of cooperative dashboard design throughout the five analytic state... more Fig. 1: Successes and failures of cooperative dashboard design throughout the five analytic states of a conversation (a-e). Cooperative dashboards guide users through their data and, in contrast to static dashboards, provide bi-directional communication through interactivity to allow the user to change or refine their analytical goals, switch between topics of interest and levels of detail, correct or update the system if it provides irrelevant or incorrect information, and provide useful summaries of analytical actions. Note that these conversation states are not necessarily sequential and the analyst can move between these various states.
Dashboards remain ubiquitous artifacts for presenting or reasoning with data across different dom... more Dashboards remain ubiquitous artifacts for presenting or reasoning with data across different domains. Yet, there has been little work that provides a quantifiable, systematic, and descriptive overview of dashboard designs at scale. We propose a schematic representation of dashboard designs as node-link graphs to better understand their spatial and interactive structures. We apply our approach to a dataset of 25,620 dashboards curated from Tableau Public to provide a descriptive overview of the core building blocks of dashboards in the wild and derive common dashboard design patterns. To guide future research, we make our dashboard corpus publicly available and discuss its application toward the development of dashboard design tools. CCS Concepts: • Human-centered computing → Visualization systems and tools; Visual analytics.
Fig. 1. MEDLEY's user interface. (A) Data attribute and intent input panel, (B) collection recomm... more Fig. 1. MEDLEY's user interface. (A) Data attribute and intent input panel, (B) collection recommendation zone, and (C) dashboard canvas. Views and widgets added to the canvas are faded out in the recommendation zone.
Tableau Research (a) Default binning scheme. (b) Binning scheme recommended by OSCAR Figure 1: Vi... more Tableau Research (a) Default binning scheme. (b) Binning scheme recommended by OSCAR Figure 1: Visualizations showing comparisons of bins for data on per-country life expectancy (left) and per-U.S. county obesity rates (right). The top-row bins are computed based on statistical properties, while the bottom-row bins are computed by OSCAR. Semantic bins have benefits for legibility, reducing the number of bins (i.e., the visual complexity of the map or histogram), and taking advantage of non-uniformity to either highlight areas of interest or compress long tails of the distribution into single bins.
Figure 1: Eviza's interface showing a map of earthquake data in the United States. The interface ... more Figure 1: Eviza's interface showing a map of earthquake data in the United States. The interface supports natural language analytical questions in the context of a visualization. Here, the map shows marks selected in response to the user's query "find large earthquakes near California." The system semantically associates the sized descriptor 'large' to an attribute in the dataset called 'magnitude' that can be associated with size. Eviza finds two ambiguities in the query: 'large' and 'near,' which are fuzzy terms for size and distance. The system sets 'large' to be of magnitude 5 and greater, while 'near' is a 100 mile radius around the border of California. Two ambiguity widgets are added to the interface to allow the user to modify these settings.
Figure 1: Examples of utterance recommendations in Snowy. (A) To assist with the "cold start" pro... more Figure 1: Examples of utterance recommendations in Snowy. (A) To assist with the "cold start" problem during data analysis, Snowy infers potentially interesting patterns from the underlying dataset and suggests analytic inquiries one may want to begin exploring the data with. (B) Upon executing a NL utterance, Snowy suggests follow-up utterances to drill down into specific data subsets or adjust the current view. (C) As marks are selected on the view through direct manipulation, Snowy recommends deictic utterances to perform popular calculations using the selected marks.
IEEE Transactions on Visualization and Computer Graphics
Fig. 1. Four comparison utterances from our design space with varying cardinalities for the compa... more Fig. 1. Four comparison utterances from our design space with varying cardinalities for the comparison entities (1-1, 1-n, n-m, n) and different levels of concreteness (explicit and implicit). Each of these comparison utterances was included in our online survey in which participants ranked their preference for the different visualization types; the most preferred visualization(s) have a colored border.
Interactive visual data analysis is most productive when users can focus on answering the questio... more Interactive visual data analysis is most productive when users can focus on answering the questions they have about their data, rather than focusing on how to operate the interface to the analysis tool. One viable approach to engaging users in interactive conversations with their data is a natural language interface to visualizations. These interfaces have the potential to be both more expressive and more accessible than many other interaction paradigms. In this paper, we focus on supporting a natural flow in data conversations by considering pragmatics, or the ways in which context in a conversation influences meaning. We explore the requirements of a pragmatics component in a natural language system for visualizations and the research challenges that arise in understanding the context of data-related conversations. We then summarize how many of these challenges are generalizable to other settings and contexts involving natural language interfaces. Flow and Visual Analytics ‘Flow’ ...
IEEE Transactions on Visualization and Computer Graphics
Fig. 1. Information displays with varying amounts of visuals and text. (a) Chart presented with n... more Fig. 1. Information displays with varying amounts of visuals and text. (a) Chart presented with no text (beyond axes and ticks), (b) Chart with a title and a single annotation, (c) Chart which displays a narrative or story around the data, annotated through text, and (d) A text-only version of the data, with the same story as displayed in (c).
IEEE Transactions on Visualization and Computer Graphics
Fig. 1. MEDLEY's user interface. (A) Data attribute and intent input panel, (B) collection recomm... more Fig. 1. MEDLEY's user interface. (A) Data attribute and intent input panel, (B) collection recommendation zone, and (C) dashboard canvas. Views and widgets added to the canvas are faded out in the recommendation zone.
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Papers by Vidya Setlur