On a visualization, the position of the marks encoding data is the most expressive and effective visual channel. It conveys order and quantity without impairing the perception of other visual channels. In the field of Information Visualization, position is often restricted to two dimensions, because using the third dimension, depth, usually affects the perception of size, which is also one of the most effective visual channels.

Extending a standard desktop workstation (i.e. a screen, a mouse, a keyboard) with virtual scenes displayed on an Augmented Reality Head-Mounted Display (AR HMD) offers many identified advantages including limited physical space requirements, very large and flexible display spaces, and 3D stereoscopic views. While the technologies become more mainstream, the remaining open question is how to interact with such hybrid workstations that combine 2D views displayed on a physical monitor and 3D views displayed on a HoloLens. For a selection task, we compared mouse-based interaction (standard for 2D desktop workstations) and direct touch interaction in mid-air (standard for 3D AR) while considering different positions of the 3D scene according to a physical monitor. To extend mouse-based selection to 3D views, we experimentally explored different interaction metaphors where the mouse cursor moves either on a horizontal or a vertical plane in a 3D virtual scene. To check for ecological validity of our results, we conducted an additional study focusing on interaction with a 2D/3D Gapminder dataset visualization. The results show 1) that the mouse-based interaction, as compared to direct touch interaction in mid-air, is easy and efficient, 2) that using a vertical plane placed in front of the 3D virtual scene to mimic the double screen metaphor outperforms other interaction techniques and 3) that flexibility is required to allow users to choose the selection techniques and to position the 3D virtual scene relative to the physical monitor. Based on these results, we derive interaction design guidelines for hybrid workstations.

In the information visualization reference model, visual mapping is the most crucial step in producing a visualization from a data set. The conventional visual mapping maps each data attribute onto a single visual channel (e.g. the year of production of a car to the position on the horizontal axis). In this work, we investigate composite visual mapping: mapping single data attributes onto several visual channels, each one representing one aspect of the data attribute (e.g. its order of magnitude, or its trend component). We first propose a table which allows us to explore the design space of composite mappings by offering a systematic overview of channel combinations. We expect that using more than one visual channel for communicating a data attribute increases the bandwidth of information presentation by displaying separable information on different aspects of data. In order to evaluate this point, we compare horizon graph, an existing technique which successfully adopts a composite visual mapping, with a selection of alternative composite mappings. We show that some of those mappings perform as well as -and in some cases even better than- horizon graph in terms of accuracy and speed. Our results confirm that the benefits of composite visual mapping are not limited to horizon graph. We thus recommend the use of composite visual mapping when users are simultaneously interested in several aspects of data attributes.

Clustering is often a first step when trying to make sense of a large data set. A wide family of cluster analysis algorithms, namely hierarchical clustering algorithms, does not provide a partition of the data set but a hierarchy of clusters organized in a binary tree, known as a dendrogram. The dendrogram has a classical node-link representation used by experts for various tasks like: to decide which subtrees are actual clusters (e.g., by cutting the dendrogram at a given depth); to give those clusters a name by inspecting their content; etc. We present Dendrogramix, a hybrid tree-matrix interactive visualization of dendrograms that superimposes the relationship between individual objects on to the hierarchy of clusters. Dendrogramix enables users to do tasks which involve both clusters and individual objects that are impracticable with the classical representation, like: to explain why a particular objects belongs to a particular cluster; to elicit and understand uncommon patterns (e.g., objects that could have been classified in a totally different cluster); etc. Those sensemaking tasks are supported by a consistent set of interaction techniques that facilitates the exploration of large clustering results.

In this paper, we introduce a new Focus+Context visualization technique, named "Stacked Trees", allowing us to explore large dendrograms produced by hierarchical clustering. This approach displays up to fifty thousands nodes on a standard-sized screen.

The classical representation of a binary tree generated by a hierarchical clustering is a node-link-based visualization denoted as a dendrogram. It allows users to explore in a simple way the clusters and the relationships between instances. However, exploration of large dendrograms is known to be difficult due to the graphical and cognitive information overload involved. Here, we discuss the current approaches and we introduce Stacked Trees, a new Focus+Context visualization technique that allows the exploration of the hierarchical clustering of up to fifty thousands nodes on a standard-sized screen.

Fitts' pointing paradigm is widely used to conduct controlled experiments and to evaluate new interaction techniques enhancing target acquisition. Many of them change the behavior of the cursor according to various inputs, most notably the positions of potential targets. We propose to extend Fitts' paradigm in order to challenge those techniques with distractors (i.e., potential targets which are not the goal of the user) in a controlled manner. To reduce variability, we add a single new factor to the paradigm, the distractor density. We specify a distractors distribution, fully determined by this factor together with those of Fitts' task, aimed at reducing bias toward a specific technique. We also propose a preliminary extension of Fitts' law to take account of the sensitivity to the density of distractors as well as of the task difficulty. In an experiment, we compare five existing pointing techniques, and show that this extended protocol enables contrasted comparisons between them.

A large variety of gestural interaction techniques based on accelerometers is now available. In this article, we propose a new taxonomic space as a systematic structure for supporting the comparative analysis of these techniques as well as for designing new ones. An interaction technique is plotted as a point in a space where the vertical axis denotes the semantic coverage of the techniques, and the horizontal axis expresses the physical actions users are engaged in, i.e. the lexicon. In addition, syntactic modifiers are used to express the interpretation process of input tokens into semantics, as well as pragmatic modifiers to make explicit the level of indirection between users' actions and system responses. To demonstrate the coverage of the taxonomy, we have classified 25 interaction techniques based on accelerometers. The analysis of the design space per se reveals directions for future research.

We investigate the use of two concurrent input channels to perform a pointing task. The first channel is the traditional mouse input device whereas the second one is the gaze position. The rake cursor interaction technique combines a grid of cursors controlled by the mouse and the selection of the active cursor by the gaze. A controlled experiment shows that rake cursor pointing drastically outperforms mouse-only pointing and also significantly outperforms the state of the art of pointing techniques mixing gaze and mouse input. A theory explaining the improvement is proposed: the global difficulty of a task is split between those two channels, and the sub-tasks could partly be performed concurrently.

Treemaps provide an interesting solution for representing hierarchical data. However, most studies have mainly focused on layout algorithms and paid limited attention to the interaction with treemaps. This makes it difficult to explore large data sets and to get access to details, especially to those related to the leaves of the trees. We propose the notion of zoomable treemaps (ZTMs), an hybridization between treemaps and zoomable user interfaces that facilitates the navigation in large hierarchical data sets. By providing a consistent set of interaction techniques, ZTMs make it possible for users to browse through very large data sets (e.g., 700,000 nodes dispatched amongst 13 levels). These techniques use the structure of the displayed data to guide the interaction and provide a way to improve interactive navigation in treemaps.

Structured graphics models such as Scalable Vector Graphics (SVG) enable designers to create visually rich graphics for user interfaces. Unfortunately current programming tools make it difficult to implement advanced interaction techniques for these interfaces. This paper presents the Hierarchical State Machine Toolkit (HsmTk), a toolkit targeting the development of rich interactions. The key aspect of the toolkit is to consider interactions as first-class objects and to specify them with hierarchical state machines. This approach makes the resulting behaviors self-contained, easy to reuse and easy to modify. Interactions can be attached to graphical elements without knowing their detailed structure, supporting the parallel refinement of the graphics and the interaction.

We introduce semantic pointing, a novel interaction technique that improves target acquisition in graphical user interfaces (GUIs). Semantic pointing uses two independent sizes for each potential target presented to the user: one size in motor space adapted to its importance for the manipulation, and one size in visual space adapted to the amount of information it conveys. This decoupling between visual and motor size is achieved by changing the control-to-display ratio according to cursor distance to nearby targets. We present a controlled experiment supporting our hypothesis that the performance of semantic pointing is given by Fitts' index of difficulty in motor rather than visual space. We apply semantic pointing to the redesign of traditional GUI widgets by taking advantage of the independent manipulation of motor and visual widget sizes.