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Idealized probability distributions, such as normal or other curves, lie at the root of confirmatory statistical tests. But how well do people understand these idealized curves? In practical terms, does the human visual system allow us to match sample data distributions with hypothesized population distributions from which those samples might have been drawn? And how do different visualization techniques impact this capability? This paper shares the results of a crowdsourced experiment that tested the ability of respondents to fit normal curves to four different data distribution visualizations: bar histograms, dotplot histograms, strip plots, and boxplots. We find that the crowd can estimate the center (mean) of a distribution with some success and little bias. We also find that people generally overestimate the standard deviation, which we dub the "umbrella effect" because people tend to want to cover the whole distribution using the curve, as if sheltering it from the heavens above, and that strip plots yield the best accuracy.

Sketches are much more than marks on paper; they play a key role for designers both in ideation and problem-solving as well as in communication with other designers. Thus, the act of sketching is often enriched with annotations, references, and physical actions, such as gestures or speech---all of which constitute meta-data about the designer's reasoning. Conventional paper-based design notebooks cannot capture this rich meta-data, but digital design notebooks can. To understand what data to capture, we conducted an observational study of design practitioners where they individually explore design solutions for a set of problems. We identified sketching and non-sketching actions that reflect their exploration of the design space. We then categorized the captured meta-data and mapped observed physical actions to design intent. These findings inform the creation of future digital design notebooks that can better capture designers' reasoning during sketching.

Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well., Databases, Information Visualization, Human Computer Interaction; Accepted at Information Visualization Journal

International audience; Animated transitions are popular in many visual applications but they can be difficult to follow, especially when many objects move at the same time. One informal design guideline for creating effective animated transitions has long been the use of slow-in/slow-out pacing, but no empirical data exist to support this practice. We remedy this by studying object tracking performance under different conditions of temporal distortion, i.e., constant speed transitions, slow-in/slow-out, fast-in/fast-out, and an adaptive technique that slows down the visually complex parts of the animation. Slow-in/slowout outperformed other techniques, but we saw technique differences depending on the type of visual transition.

International audience; Visualization applications routinely map quantitative attributes to color using color scales. Although color is an effective visualization channel, it is limited by both display hardware and the human visual system. We propose a new interaction technique that overcomes these limitations by dynamically optimizing color scales based on a set of sampling lenses. The technique inspects the lens contents in data space, optimizes the initial color scale, and then renders the contents of the lens to the screen using the modified color scale. We present two prototype implementations of this pipeline and describe several case studies involving both information visualization and image inspection applications. We validate our approach with two mutually linked and complementary user studies comparing the Color Lens with explicit contrast control for visual search.

Despite typically receiving little emphasis in visualization research, interaction in visualization is the catalyst for the user's dialogue with the data, and, ultimately, the user's actual understanding and insight into these data. There are many possible reasons for this skewed balance between the visual and interactive aspects of a visualization. One reason is that interaction is an intangible concept that is difficult to design, quantify, and evaluate. Unlike for visual design, there are few examples that show visualization practitioners and researchers how to design the interaction for a new visualization in the best manner. In this article, we attempt to address this issue by collecting examples of visualizations with ‘best-in-class’ interaction and using them to extract practical design guidelines for future designers and researchers. We call this concept fluid interaction, and we propose an operational definition in terms of the direct manipulation and embodied interaction paradigms, the psychological concept of ‘flow’, and Norman's gulfs of execution and evaluation.

Published online before print July 29, 2011; International audience; The conflux of two growing areas of technology---collaboration and visualization---into a new research direction, collaborative visualization, provides new research challenges. Technology now allows us to easily connect and collaborate with one another---in settings as diverse as over networked computers, across mobile devices, or using shared displays such as interactive walls and tabletop surfaces. Digital information is now regularly accessed by multiple people in order to share information, to view it together, to analyze it, or to form decisions. Visualizations are used to deal more effectively with large amounts of information while interactive visualizations allow users to explore the underlying data. While researchers face many challenges in collaboration and in visualization, the emergence of collaborative visualization poses additional challenges but is also an exciting opportunity to reach new audiences and applications for visualization tools and techniques. The purpose of this article is (1) to provide a definition, clear scope, and overview of the evolving field of collaborative visualization, (2) to help pinpoint the unique focus of collaborative visualization with its specific aspects, challenges, and requirements within the intersection of general computer-supported cooperative work (CSCW) and visualization research, and (3) to draw attention to important future research questions to be addressed by the community. We conclude by discussing a research agenda for future work on collaborative visualization and urge for a new generation of visualization tools that are designed with collaboration in mind from their very inception.

International audience; Social networks collected by historians or sociologists typically have a large number of actors and edge attributes. Applying social network analysis (SNA) algorithms to these networks produces additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of attributes is one of the main challenges of multivariate SNA. We present the design of GraphDice, a multivariate network visualization system for exploring the attribute space of edges and actors. GraphDice builds upon the ScatterDice system for its main multidimensional navigation paradigm, and extends it with novel mechanisms to support network exploration in general and SNA tasks in particular. Novel mechanisms include visualization of attributes of interval type and projection of numerical edge attributes to node attributes. We show how these extensions to the original ScatterDice system allow to support complex visual analysis tasks on networks with hundreds of actors and up to 30 attributes, while providing a simple and consistent interface for interacting with network data.

International audience; We present a model for building, visualizing, and interacting with multiscale representations of information visualization techniques using hierarchical aggregation. The motivation for this work is to make visual representations more visually scalable and less cluttered. The model allows for augmenting existing techniques with multiscale functionality, as well as for designing new visualization and interaction techniques that conform to this new class of visual representations. We give some examples of how to use the model for standard information visualization techniques such as scatterplots, parallel coordinates, and node-link diagrams, and discuss existing techniques that are based on hierarchical aggregation. This yields a set of design guidelines for aggregated visualizations. We also present a basic vocabulary of interaction techniques suitable for navigating these multiscale visualizations.

International audience; We present a novel method called motion-pointing for selecting a set of visual items such as push-buttons without actually pointing to them. Instead, each potential target displays a rhythmically animated point we call the driver. To select a specific item, the user only has to imitate the motion of its driver using the input device. Once the motion has been recognized by the system, the user can confirm the selection to trigger the action. We consider cyclic motions on an elliptic trajectory with a specific period, and study the most effective methods for real-time matching such a trajectory, as well as the range of parameters a human can reliably reproduce. We then show how to implement motion-pointing in real applications using an interaction technique we call move-and-stroke. Finally, we measure the throughput and error rate of move-and-stroke in a controlled experiment. We show that the selection time is linearly proportional to the number of input bits conveyed up to 6 bits, confirming that motion-pointing is a practical input method.

Best Paper Award; International audience; Scatterplots remain one of the most popular and widely-used visual representations for multidimensional data due to their simplicity, familiarity and visual clarity, even if they lack some of the flexibility and visual expressiveness of newer multidimensional visualization techniques. This paper presents new interactive methods to explore multidimensional data using scatterplots. This exploration is performed using a matrix of scatterplots that gives an overview of the possible configurations, thumbnails of the scatterplots, and support for interactive navigation in the multidimensional space. Transitions between scatterplots are performed as animated rotations in 3D space, somewhat akin to rolling dice. Users can iteratively build queries using bounding volumes in the dataset, sculpting the query from different viewpoints to become more and more refined. Furthermore, the dimensions in the navigation space can be reordered, manually or automatically, to highlight salient correlations and differences among them. An example scenario presents the interaction techniques supporting smooth and effortless visual exploration of multidimensional datasets.

International audience; Information Visualization needs longitudinal studies to assess the usefulness, usability, and, more generally, the value of its techniques. However, most of the longitudinal studies conducted so far have involved human resources to collect and analyze evidences. Automatic logging and session replay mechanisms can help answering questions while limiting human collection and analysis of data. In this article, we describe how to set up these mechanisms in InfoVis systems with a minimal effort.

International audience; We present the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring graphs at a scale of millions of nodes and edges. ZAME is based on an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from an overview to the most detailed views. Several components work together in the ZAME tool to make this possible. Efficient matrix ordering algorithms group related elements. Individual data cases are aggregated into higher-order meta-representations. Aggregates are arranged into a pyramid hierarchy that allows for on-demand paging to GPU shader programs to support smooth multiscale browsing. Using ZAME, we are able to explore the entire French Wikipedia—over 500,000 articles and 6,000,000 links—with interactive performance on standard consumer-level computer hardware.

This article presents the Zoomable Adjacency Matrix Explorer (ZAME), a visualization tool for exploring networks at a scale of millions of nodes and tens of millions of edges. ZAME presents an adjacency matrix graph representation aggregated at multiple scales. It allows analysts to explore a graph at many levels, zooming and panning with interactive performance from the most summary to the most detailed views. Several components work together to make this performance possible and the results meaningful. A ``pyramid'' of aggregated views paged on demand to OpenGL GPU shader programs supports smooth multiscale browsing in huge datasets. Efficient matrix ordering algorithms group related elements to make the views meaningful. Using ZAME, we can explore the entire French Wikipedia, over 500,000 articles and 6,000,000 links, with interactive performance on standard consumer-level computer hardware.

Extended abstract and poster; International audience; Our overall vision is to enable researchers to explore 3D datasets with as much immersion as possible, arising both from visuals as well as from interaction . We therefore explore ways to combine an immersive large view of the 3D data with means to intuitively control this view with touch input on a separate mobile monoscopic tablet. This combination has the potential to increase people's acceptance of stereoscopic environments for 3D data visualization since--through touch-based interaction--it puts them in control of their data. Moreover, the indirect manipulation of (stereoscopically displayed) 3D data on a personal touch device has been shown to have potentially more efficient and precise interaction than interaction directly on a large display.