OSCAR: A Semantic-based Data Binning Approach

Binning is applied to categorize data values or to see distributions of data. Existing binning algorithms often rely on statistical properties of data. However, there are semantic considerations for selecting appropriate binning schemes. Surveys, for instance, gather respondent data for demographic-related questions such as age, salary, number of employees, etc., that are bucketed into defined semantic categories. In this paper, we leverage common semantic categories from survey data and Tableau Public visualizations to identify a set of semantic binning categories. We employ these semantic binning categories in OSCAR: a method for automatically selecting bins based on the inferred semantic type of the field. We conducted a crowdsourced study with 120 participants to better understand user preferences for bins generated by OSCAR vs. binning provided in Tableau. We find that maps and histograms using binned values generated by OSCAR are preferred by users as compared to binning schemes based purely on the statistical properties of the data.Comment: 5 pages (4 pages text + 1 page references), 3 figure

Striking a Balance: Reader Takeaways and Preferences when Integrating Text and Charts

While visualizations are an effective way to represent insights about information, they rarely stand alone. When designing a visualization, text is often added to provide additional context and guidance for the reader. However, there is little experimental evidence to guide designers as to what is the right amount of text to show within a chart, what its qualitative properties should be, and where it should be placed. Prior work also shows variation in personal preferences for charts versus textual representations. In this paper, we explore several research questions about the relative value of textual components of visualizations. 302 participants ranked univariate line charts containing varying amounts of text, ranging from no text (except for the axes) to a written paragraph with no visuals. Participants also described what information they could take away from line charts containing text with varying semantic content. We find that heavily annotated charts were not penalized. In fact, participants preferred the charts with the largest number of textual annotations over charts with fewer annotations or text alone. We also find effects of semantic content. For instance, the text that describes statistical or relational components of a chart leads to more takeaways referring to statistics or relational comparisons than text describing elemental or encoded components. Finally, we find different effects for the semantic levels based on the placement of the text on the chart; some kinds of information are best placed in the title, while others should be placed closer to the data. We compile these results into four chart design guidelines and discuss future implications for the combination of text and charts.Comment: 11 pages, 4 tables, 6 figures, accepted to IEEE Transaction on Visualization and Graphic

Toward a Scalable Census of Dashboard Designs in the Wild: A Case Study with Tableau Public

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.Comment: *J. Purich and A. Srinivasan contributed equally to the wor

More: A Mobile Open Rich Media Environment

‘Rich media ’ is a term that implies the integration of all of the ad-vances we have made in the mobile space delivering music, speech, text, graphics and video. This is true, but it is more than the sum of its parts. Rich media is the ability to deliver these modalities, to interact with these modalities, and to do it in a way that allows for the construction, delivery and use of compelling mobile services in an effective and economic manner. In this paper, we introduce a sys-tem called Mobile Open Rich-media Environment (‘MORE’) that helps realize such mobile rich media services, combining various technologies of W3C, OMA, 3GPP and IETF standards. The differ-ent components of the system include formatting, packaging, trans-porting, rendering and interacting with rich media files and streams. 1

Is that a smile?: gaze dependent facial expressions

Figure 1: a) A low spatial frequency filter reveals a more prominent smile. b) A more neutral expression is seen under high spatial frequency

Auto(mobile): Mobile Visual Interfaces for the Road

The increased prevalence of mobile touch screen interfaces in cars provides for new challenges in terms of optimizing safety, usability and affective response. While touch screens have certain usability benefits, the interfaces present significant visual attention demands from the driver. Suppose that you are traveling to an unfamiliar destination in your city to visit a friend. You know that she lives close to a popular landmark (mall, tourist attraction) and have visited that landmark several times. If you get directions from your GPS to visit your friend, it will most likely provide a shortest or fastest route, none of which will take into account the fact that you have visited the popular landmark several times. Additionally, the amount of navigational details that you would need to get to the popular landmark would be far fewer than the assistance you would need when you are driving in an unfamiliar region. By using context from the phone and car, more informed visual navigation applications can be created for a better user experience