Some Augmented Reality applications require tracking physical objects to anchor virtual elements to them. Despite significant progress in computer vision, achieving robust tracking of manipulated objects remains challenging, notably due to occlusions caused by the hands. Yet the hands carry valuable information: the way an object is grasped and moved is reflected in their shape and motion. We explore the following question: can we infer changes to an object’s position and orientation from the shape and movements of the hand manipulating it? We investigate this general approach, which we call Object-from-Hand (ObHa), building three probes, drawing on insights from experimental psychology research on grasping, on our own empirical studies, and on an analysis of the extensive HOT3D dataset of everyday object manipulations. We then discuss the approach’s potential either as a complement to vision-based pose tracking solutions or as a coarse standalone pose tracking solution.

International audience

Interaction in Augmented Reality primarily relies on raycast pointing and mid-air touch. An alternative consists of using the non-dominant hand as a touch-sensitive surface, enabling more comfortable, less fatiguing input. AR UI design guidelines have so far discouraged this alternative because of poor hand tracking performance when the hands overlap, favoring touchpads in the air near the hand, rather than on the hand. But significant improvements to the hand tracking capabilities of recent commodity headsets suggest that on-hand pads may now be feasible. We develop an on-hand touchpad prototype and conduct two studies that involve both discrete input and continuous control tasks. The first study compares such on-hand pads to baseline in-air and on-object pads, showing comparable performance despite some limitations in tracking accuracy. The second study quantifies the advantage of on-hand and in-air pads over on-object pads during transitions between touchpad input and other physical hand activities.

Composers use music notation programs throughout their creative process. Those programs are essentially elaborate structured document editors that enable composers to create high-quality scores by enforcing musical notation rules. They effectively support music engraving, but impede the more creative stages in the composition process because of their lack of flexibility. Composers thus often combine these desktop tools with other mediums such as paper. Interactive surfaces that support pen and touch input have the potential to address the tension between the contradicting needs for structure and flexibility. We interview nine professional composers. We report insights about their thought process and creative intentions, and rely on the “Cognitive Dimensions of Notations” framework to capture the frictions they experience when materializing those intentions on a score. We then discuss how interactive surfaces could increase flexibility by temporarily breaking the structure when manipulating the notation.

TriPad enables opportunistic touch interaction in Augmented Reality using hand tracking only. Users declare the surface they want to appropriate with a simple hand tap gesture. They can then use this surface at will for direct and indirect touch input. TriPad only involves analyzing hand movements and postures, without the need for additional instrumentation, scene understanding or machine learning. TriPad thus works on a variety of flat surfaces, including glass. It also ensures low computational overhead on devices that typically have a limited power budget. We describe the approach, and report on two user studies. The first study demonstrates the robustness of TriPad’s hand movement interpreter on different surface materials. The second study compares TriPad against direct mid-air AR input techniques on both discrete and continuous tasks and with different surface orientations. TriPad achieves a better speed-accuracy trade-off overall, improves comfort and minimizes fatigue.

Spreadsheet programs for interactive surfaces have limited manipulations capabilities and are often frustrating to use. One key reason is that the spreadsheet grid creates a layer that intercepts most user input events, making it difficult to reach the cell values that lie underneath. We conduct an analysis of commercial spreadsheet programs and an elicitation study to understand what users can do and what they would like to do with spreadsheets on interactive surfaces. Informed by these, we design interaction techniques that leverage the precision of the pen to mitigate friction between the different layers. These enable more operations by direct manipulation on and through the grid, targeting not only cells and groups of cells, but values and substrings within and across cells as well. We prototype these interaction techniques and conduct a qualitative study with information workers who perform a variety of spreadsheet operations on their own data.

Mobile devices such as smartphones and tablets have limited display size and input capabilities that make a variety of tasks challenging. Coupling the mobile device with Augmented Reality eyewear such as smartglasses can help address some of these challenges. In the specific context of digital content annotation tasks, this combination has the potential to enhance the user experience on two fronts. First, annotations can be offloaded into the air around the mobile device, freeing precious screen real-estate. Second, as smartglasses often come equipped with a variety of sensors including a camera, users can annotate documents with pictures or videos of their environment, captured on the spot, hands-free, and from the wearer's perspective. We present AnnotAR, a prototype that we use as a research probe to assess the viability of this approach to digital content annotation. We use AnnotAR to gather users' preliminary feedback in a laboratory setting, and to showcase how it could support real-world use cases.

We contribute a detailed investigation of AR-enhanced widgets for smartphones, where AR technology is not only used to offload widgets from the phone to the air around it, but to give users more control on input precision as well. Such widgets have the obvious benefit of freeing up screen real-estate on the phone, but their other potential benefits remain largely theoretical. Their limitations are not well understood, most particularly in terms of input performance. We compare different AR-enhanced widget designs against their state-of-the-art touch-only counterparts with a series of exploratory studies in which participants had to perform three tasks: command trigger, parameter value adjustment, and precise 2D selection. We then derive guidelines from our empirical observations.

Expressive design environments enable visualization designers not only to specify chart types and visual mappings, but also to customize individual graphical marks, as they would in a vector graphics drawing tool. Prior work has mainly investigated how to support the expressive design of a wide range of charts generated from tabular data: bar charts, scatterplots, maps, etc. We focus here on an expressive design environment for node-link diagrams generated from multivariate networks. Such data structures raise specific challenges and opportunities in terms of visual design and interactive authoring. We discuss those specificities and describe the user-centered design process that led to Graphies, a prototype environment for expressive node-link diagram authoring. We then report on a study in which participants successfully reproduced several expressive designs, and created their own designs as well.

Geographical propagation phenomena occur in multiple domains, such as in epidemiology and social media. Propagation dynamics are often complex, and visualizations play a key role in helping subject-matter experts understand and analyze them. However, there is little empirical data about the effectiveness of the various strategies used to visualize geographical propagation. To fill this gap, we conduct an experiment to evaluate the effectiveness of three strategies: an animated map, small-multiple maps, and a single map with glyphs. We compare them under five tasks that vary in one of the following dimensions: propagation scope, direction, speed, peaks, and spatial jumps. Our results show that small-multiple maps perform best overall, but that the effectiveness of each visualization varies depending on the task considered.

Observing the relationship between two or more variables over space and time is essential in many domains. For instance, looking, for different countries, at the evolution of both the life expectancy at birth and the fertility rate will give an overview of their demographics. The choice of visual representation for such multivariate data is key to enabling analysts to extract patterns and trends. Prior work has compared geo-temporal visualization techniques for a single thematic variable that evolves over space and time, or for two variables at a specific point in time. But how effective visualization techniques are at communicating correlation between two variables that evolve over space and time remains to be investigated. We report on a study comparing three techniques that are representative of different strategies to visualize geo-temporal multivariate data: either juxtaposing all locations for a given time step, or juxtaposing all time steps for a given location; and encoding thematic attributes either using symbols overlaid on top of map features, or using visual channels of the map features themselves. Participants performed a series of tasks that required them to identify if two variables were correlated over time and if there was a pattern in their evolution. Tasks varied in granularity for both dimensions: time (all time steps, a subrange of steps, one step only) and space (all locations, locations in a subregion, one location only). Our results show that a visualization's effectiveness depends strongly on the task to be carried out. Based on these findings we present a set of design guidelines about geo-temporal visualization techniques for communicating correlation.

During sensemaking, people annotate insights: underlining sentences in a document or circling regions on a map. They jot down their hypotheses: drawing correlation lines on scatterplots or creating personal legends to track patterns. We present ActiveInk, a system enabling people to seamlessly transition between exploring data and externalizing their thoughts using pen and touch. ActiveInk enables the natural use of pen for active reading behaviors, while supporting analytic actions by activating any of these ink strokes. Through a qualitative study with eight participants, we contribute observations of active reading behaviors during data exploration and design principles to support sensemaking.

When editing or reviewing a document, people directly overlay ink marks on content. For instance, they underline words, or circle elements in a fgure. These overlay marks often accompany in-context annotations in the form of handwritten footnotes and marginalia. People tend to put annotations close to the content that elicited them, but have to compose with the often-limited whitespace. We introduce SpaceInk, a design space of pen+touch techniques that make room for in-context annotations by dynamically refowing documents. We identify representative techniques in this design space, spanning both new ones and existing ones. We evaluate them in a user study, with results that inform the design of a prototype system. Our system lets users concentrate on capturing feeting thoughts, streamlining the overall annotation process by enabling the fuid inverleaving of space-making gestures with freeform ink.

Before-and-after image pairs show how entities in a given region have evolved over a specific period of time. Satellite images are a major source of such data, that capture how natural phenomena or human activity impact a geographical area. These images are used both for data analysis and to illustrate the resulting findings to diverse audiences. The simple techniques used to display them, including juxtaposing, swapping and monolithic blending, often fail to convey the underlying phenomenon in a meaningful manner. We introduce Baia, a framework to create advanced animated transitions, called animation plans, between before-and-after images. Baia relies on a pixel-based transition model that gives authors much expressive power, while keeping animations for common types of changes easy to create thanks to predefined animation primitives. We describe our model, the associated animation editor, and report on two user studies. In the first study, advanced transitions enabled by Baia were compared to monolithic blending, and perceived as more realistic and better at focusing viewer's attention on a region of interest than the latter. The second study aimed at gathering feedback about the usability of Baia's animation editor.

TouchTokens were introduced recently as a means to design low-cost tangible interfaces. The technique consists in recognizing multi-touch patterns associated with specific tokens, and works on any touch-sensitive surface, with passive tokens that can be made out of any material. TouchTokens have so far been limited to a few basic geometrical shapes only, which puts a significant practical limit to how tailored token sets can be. In this article, we introduce TouchTokenBuilder and TouchTokenTracker that, taken together, aim at facilitating the development of tailor-made tangible interfaces. TouchTokenBuilder is an application that assists interface designers in creating token sets using a simple direct-manipulation interface. TouchTokenTracker is a library that enables tracking the tokens' full geometry. We report on experiments with those tools, showing the strengths and limitations of tangible interfaces with passive tokens.

Network edge data attributes are usually encoded using color, opacity, stroke thickness and stroke pattern, or some combination thereof. In addition to these static variables, it is also possible to animate dynamic particles flowing along the edges. This opens a larger design space of animated edge textures, featuring additional visual encodings that have potential not only in terms of visual mapping capacity but also playfulness and aesthetics. Such animated edge textures have been used in several commercial and design-oriented visualizations, but to our knowledge almost always in a relatively ad hoc manner. We introduce a design space and Web-based framework for generating animated edge textures, and report on an initial evaluation of particle properties - particle speed, pattern and frequency - in terms of visual perception.

Multi-scale navigation interfaces were originally designed to enable single users to explore large visual information spaces on desktop workstations. These interfaces can also be quite useful on tabletops. However, their adaptation to co-located multi-user contexts is not straightforward. The literature describes different interfaces, that only offer a limited subset of navigation actions. In this paper, we first identify a comprehensive set of actions to effectively support multi-scale navigation. We report on a guessability study in which we elicited user-defined gestures for triggering these actions, showing that there is no natural design solution, but that users heavily rely on the now-ubiquitous slide, pinch and turn gestures. We then propose two interface designs based on this set of three basic gestures: one involves two-hand variations on these gestures, the other combines them with widgets. A comparative study suggests that users can easily learn both, and that the gesture-based, visually-minimalist design is a viable option, that saves display space for other controls.

TouchTokens are passive tokens that can be recognized on any capacitive surface based on the spatial configuration of the fingers that hold them. However, interaction with these tokens is confined to the basic two-state model of touch interaction as the system only knows the tokens' position and cannot detect tokens that are not touched. We increase the expressive power of TouchTokens by introducing laser-cut lattice hinges in their design, so as to make them flexible. A new recognizer, that analyzes the micro-movements of the fingers that hold the tokens, enables the system to detect when a token is left on the surface rather than taken off it. It can also detect bend events that can be mapped to command triggers, and a squeezed state that can be used for quasi-modal interaction.

GIS software applications and other mapping tools enable users to correlate data from multiple layers and gain insight from the resulting visualizations. However, most of these applications only feature basic, monolithic layer compositing techniques. These techniques do not always support users effectively in their tasks, as we observed during interviews with GIS experts. We introduce MapMosaic, a novel approach based on dynamic visual compositing that enables users to interactively create and manipulate local composites of multiple vector and raster map layers, taking into account the semantics and attribute values of objects and fields in the compositing process. We evaluate MapMosaic ’s interaction model against that of QGIS (a widely used desktop GIS) and MAPublisher (a professional cartography tool) using the ‘Cognitive Dimensions’ framework and through an analytical comparison, showing that MapMosaic ’s model is more flexible and can support users more effectively in their tasks. We also report on feedback obtained from experts, which further confirms the potential of this highly dynamic approach to map layer compositing.

TouchTokens make it possible to easily build interfaces that combine tangible and gestural input using passive tokens and a regular multi-touch surface. The tokens constrain users' grasp, and thus, the relative spatial configuration of fingers on the surface, theoretically making it possible to design algorithms that can recognize the resulting touch patterns. We performed a formative user study to collect and analyze touch patterns with tokens of varying shape and size. The analysis of this pattern collection showed that individual users have a consistent grasp for each token, but that this grasp is user-dependent and that different grasp strategies can lead to confounding patterns. We thus designed a second set of tokens featuring notches that constrain users' grasp. Our recognition algorithm can classify the resulting patterns with a high level of accuracy (>95%) without any training, enabling application designers to associate rich touch input vocabularies with command triggers and parameter controls.

Geovisualization applications typically organize data into layers. These layers hold different types of geographical features, describe different characteristics of the same features, or represent those features at different points in time. Layers can be composited in various ways, most often employing a juxtaposition or superimposition strategy, to produce maps that users can explore interactively. From an HCI perspective, one of the main challenges is to design interactive compositions that optimize the legibility of the resulting map and that ease layer comparison. We characterize five representative techniques, and empirically evaluate them using a set of real-world maps in which we purposefully introduce six types of differences amenable to inter-layer visual comparison. We discuss the merits of these techniques in terms of visual interference, user attention and scanning strategy. Our results can help inform the design of map-based visualizations for supporting geo-analysis tasks in many application areas.

Drag-and-drop has become ubiquitous, both on desktop computers and touch-sensitive surfaces. It is used to move and edit the geometry of elements in graphics editors, to adjust parameters using controllers such as sliders, or to manage views (e.g., moving and resizing windows, panning maps). Reverting changes made via a drag-and-drop usually entails performing the reciprocal drag-and-drop action. This can be costly as users have to remember the previous position of the object and put it back precisely. We introduce the D n D −1 model that handles all past locations of graphical objects. We redesign the Dwell-and-Spring widget to interact with this history, and explain how applications can implement D n D −1 to enable users to perform reciprocal drag-and-drop to any past location for both individual objects and groups of objects. We report on two user studies, whose results show that users understand D n D −1 , and that Dwell-and-Spring enables them to interact with this model effectively.

Ultra-high resolution wall-sized displays (“ultra-walls”) are effective for presenting large datasets, but their size and resolution make traditional pointing techniques inadequate for precision pointing. We study mid-air pointing techniques that can be combined with other, domain-specific interactions. We first explore the limits of existing single-mode remote pointing techniques and demonstrate theoretically that they do not support high-precision pointing on ultra-walls. We then explore solutions to improve mid-air pointing efficiency: a tunable acceleration function and a framework for dual-precision (DP) techniques, both with precise tuning guidelines. We designed novel pointing techniques following these guidelines, several of which outperform existing techniques in controlled experiments that involve pointing difficulties never tested prior to this work. We discuss the strengths and weaknesses of our techniques to help interaction designers choose the best technique according to the task and equipment at hand. Finally, we discuss the cognitive mechanisms that affect pointing performance with these techniques.

Millions of people go to the Web to search for geographical itineraries. Inspecting those map itineraries remains tedious because they seldom fit on screen, requiring much panning & zooming to see details. Focus+context techniques address this problem by displaying routes at a scale that allows them to fully fit on screen: users see the entire route at once, and perform magnified steering using a lens to navigate along the path, revealing additional detail. Navigation based on magnified steering has been shown to outperform pan & zoom for large steering tasks. Yet, this task remains challenging, in part because paths have a tendency to "slip off" the side of the lens. RouteLenses automatically adjust their position based on the geometry of the path that users steer through. RouteLenses make it easier for users to follow a route, yet do not constrain movements too strictly, leaving them free to move the lens away from the path to explore its surroundings.

Designing visualizations of dynamic networks is challenging, both because the data sets tend to be complex and because the tasks associated with them are often cognitively demand- ing. We introduce the Matrix Cube, a novel visual representation and navigation model for dynamic networks, inspired by the way people comprehend and manipulate physical cubes. Users can change their perspective on the data by rotating or decomposing the 3D cube. These manipulations can produce a range of different 2D visualizations that emphasize specific aspects of the dynamic network suited to particular analysis tasks. We describe Matrix Cubes and the interactions that can be performed on them in the Cubix system. We then show how two domain experts, an astronomer and a neurologist, used Cubix to explore and report on their own network data.

Identifying, tracking and understanding changes in dynamic networks are complex and cognitively demanding tasks. We present GraphDiaries, a visual interface designed to improve support for these tasks in any node-link based graph visualization system. GraphDiaries relies on animated transitions that highlight changes in the network between time steps, thus helping users identify and understand those changes. To better understand the tasks related to the exploration of dynamic networks, we first introduce a task taxonomy, that informs the design of GraphDiaries, presented afterwards. We then report on a user study, based on representative tasks identified through the taxonomy, and that compares GraphDiaries to existing techniques for temporal navigation in dynamic networks, showing that it outperforms them in terms of both task time and errors for several of these tasks.

Rich interaction with high-resolution wall displays is not limited to remotely pointing at targets. Other relevant types of interaction include virtual navigation, text entry, and direct manipulation of control widgets. However, most techniques for remotely acquiring targets with high precision have studied remote pointing in isolation, focusing on pointing efficiency and ignoring the need to support these other types of interaction. We investigate high-precision pointing techniques capable of acquiring targets as small as 4 millimeters on a 5.5 meters wide display while leaving up to 93 % of a typical tablet device's screen space available for task-specific widgets. We compare these techniques to state-of-the-art distant pointing techniques and show that two of our techniques, a purely relative one and one that uses head orientation, perform as well or better than the best pointing-only input techniques while using a fraction of the interaction resources.

Virtual navigation on a mobile touchscreen is usually performed using finger gestures: drag and flick to scroll or pan, pinch to zoom. While easy to learn and perform, these gestures cause significant occlusion of the display. They also require users to explicitly switch between navigation mode and edit mode to either change the viewport's position in the document, or manipulate the actual content displayed in that viewport, respectively. SidePress augments mobile devices with two continuous pressure sensors co-located on one of their sides. It provides users with generic bidirectional navigation capabilities at different levels of granularity, all seamlessly integrated to act as an alternative to traditional navigation techniques, including scrollbars, drag-and-flick, or pinch-to-zoom. We describe the hardware prototype, detail the associated interaction vocabulary for different applications, and report on two laboratory studies. The first shows that users can precisely and efficiently control SidePress; the second, that SidePress can be more efficient than drag-and-flick touch gestures when scrolling large documents.

The Power-up Button is a physical button that combines pressure and proximity sensing to enable gestural interaction with one thumb. Combined with a gesture recognizer that takes the hand's anatomy into account, the Power-up Button can recognize six different mid-air gestures performed on the side of a mobile device. This gives it, for instance, enough expressive power to provide full one-handed control of interface widgets displayed on screen. This technology can complement touch input, and can be particularly useful when interacting eyes-free. It also opens up a larger design space for widget organization on screen: the button enables a more compact layout of interface components than what touch input alone would allow. This can be useful when, e.g., filling the numerous fields of a long Web form, or for very small devices.

Complex 3D virtual scenes such as CAD models of airplanes and representations of the human body are notoriously hard to visualize. Those models are made of many parts, pieces and layers of varying size, that partially occlude or even fully surround one another. We introduce Gimlenses, a multi-view, detail-in-context visualization technique that enables users to navigate complex 3D models by interactively drilling holes into their outer layers to reveal objects that are buried, possibly deep, into the scene. Those holes get constantly adjusted so as to guarantee the visibility of objects of interest from the parent view. Gimlenses can be cascaded and constrained with respect to one another, providing synchronized, complementary viewpoints on the scene. Gimlenses enable users to quickly identify elements of interest, get detailed views of those elements, relate them, and put them in a broader spatial context.

Research on visualizing Semantic Web data has yielded many tools that rely on information visualization techniques to better support the user in understanding and editing these data. Most tools structure the visualization according to the concept definitions and interrelations that constitute the ontology’s vocabulary. Instances are often treated as somewhat peripheral information, when considered at all. These instances, that populate ontologies, represent an essential part of any knowledge base. Understanding instance-level data might be easier for users because of their higher concreteness, but instances will often be orders of magnitude more numerous than the concept definitions that give them machine-processable meaning. As such, the visualization of instance-level data poses different but real challenges. The authors present a visualization technique designed to enable users to visualize large instance sets and the relations that connect them. This visualization uses both node-link and adjacency matrix representations of graphs to visualize different parts of the data depending on their semantic and local structural properties. The technique was originally devised for simple social network visualization. The authors extend it to handle the richer and more complex graph structures of populated ontologies, exploiting ontological knowledge to drive the layout of, and navigation in, the representation embedded in a smooth zoomable environment.

Using tiled monitors to build wall-sized displays has multiple advantages: higher pixel density, simpler setup and easier calibration. However, the resulting display walls suffer from the visual discontinuity caused by the bezels that frame each monitor. To avoid introducing distortion, the image has to be rendered as if some pixels were drawn behind the bezels. In turn, this raises the issue that a non-negligible part of the rendered image, that might contain important information, is visually occluded. We propose to draw upon the analogy to french windows that is often used to describe this approach, and make the display really behave as if the visualization were observed through a french window. We present and evaluate two interaction techniques that let users reveal content hidden behind bezels. ePan enables users to offset the entire image through explicit touch gestures. GridScape adopts a more implicit approach: it makes the grid formed by bezels act like a true french window using head tracking to simulate motion parallax, adapting to users' physical movements in front of the display. The two techniques work for both single- and multiple-user contexts.

In graphical user interfaces, direct manipulation consists in incremental actions that should be reversible. Typical examples include manipulating geometrical shapes in a vector graphics editor, navigating a document using a scrollbar, or moving and resizing windows on the desktop. As in many such cases, there will not be any mechanism to undo them, requiring users to manually revert to the previous state using a similar sequence of direct manipulation actions. The associated motor and cognitive costs can be high. We argue that proper and consistent mechanisms to support undo in this context are lacking, and present Dwell-and-Spring, an interaction technique that uses the metaphor of springs to enable users to undo direct manipulations. A spring widget pops up whenever the user dwells during a press-drag-release interaction, giving her the opportunity to either cancel the current manipulation or undo the last one. The technique is generic and can easily be implemented on top of existing applications to complement the traditional undo command. Empirical evaluation shows that users quickly adopt it as soon as they discover it.

Focus+context lens-based techniques smoothly integrate two levels of detail using spatial distortion to connect the magnified region and the context. Distortion guarantees visual continuity, but causes problems of interpretation and focus targeting, partly due to the fact that most techniques are based on statically-defined, regular lens shapes, that result in far-from-optimal magnification and distortion. JellyLenses dynamically adapt to the shape of the objects of interest, providing detail-in-context visualizations of higher relevance by optimizing what regions fall into the focus, context and spatially-distorted transition regions. This both improves the visibility of content in the focus region and preserves a larger part of the context region. We describe the approach and its implementation, and report on a controlled experiment that evaluates the usability of JellyLenses compared to regular fisheye lenses, showing clear performance improvements with the new technique for a multi-scale visual search task.

Very-high-resolution wall-sized displays offer new opportunities for interacting with large data sets. While pointing on this type of display has been studied extensively, higher-level, more complex tasks such as pan-zoom navigation have received little attention. It thus remains unclear which techniques are best suited to perform multiscale navigation in these environments. Building upon empirical data gathered from studies of pan-and-zoom on desktop computers and studies of remote pointing, we identified three key factors for the design of mid-air pan-and-zoom techniques: uni- vs. bimanual interaction, linear vs. circular movements, and level of guidance to accomplish the gestures in mid-air. After an extensive phase of iterative design and pilot testing, we ran a controlled experiment aimed at better understanding the influence of these factors on task performance. Significant effects were obtained for all three factors: bimanual interaction, linear gestures and a high level of guidance resulted in significantly improved performance. Moreover, the interaction effects among some of the dimensions suggest possible combinations for more complex, real-world tasks.

Visual representations of time-series are useful for tasks such as identifying trends, patterns and anomalies in the data. Many techniques have been devised to make these visual representations more scalable, enabling the simultaneous display of multiple variables, as well as the multi-scale display of time-series of very high resolution or that span long time periods. There has been comparatively little research on how to support the more elaborate tasks associated with the exploratory visual analysis of timeseries, e.g., visualizing derived values, identifying correlations, or discovering anomalies beyond obvious outliers. Such tasks typically require deriving new time-series from the original data, trying different functions and parameters in an iterative manner. We introduce a novel visualization technique called ChronoLenses, aimed at supporting users in such exploratory tasks. ChronoLenses perform on-the-fly transformation of the data points in their focus area, tightly integrating visual analysis with user actions, and enabling the progressive construction of advanced visual analysis pipelines.

Focus+context interfaces provide in-place magnification of a region of the display, smoothly integrating the focus of attention into its surroundings. Two representations of the data exist simultaneously at two different scales, providing an alternative to classical pan & zoom for navigating multi-scale interfaces. For many practical applications however, the magnification range of focus+context techniques is too limited. This paper addresses this limitation by exploring the quantization problem: the mismatch between visual and motor precision in the magnified region. We introduce three new interaction techniques that solve this problem by

Focus+context interaction techniques based on the metaphor of lenses are used to navigate and interact with objects in large information spaces. They provide in-place magnification of a region of the display without requiring users to zoom into the representation and consequently lose context. In order to avoid occlusion of its immediate surroundings, the magnified region is often integrated in the context using smooth transitions based on spatial distortion. Such lenses have been developed for various types of representations using techniques often tightly coupled with the underlying graphics framework. We describe a representation-independent solution that can be implemented with minimal effort in different graphics frameworks, ranging from 3D graphics to rich multiscale 2D graphics combining text, bitmaps, and vector graphics. Our solution is not limited to spatial distortion and provides a unified model that makes it possible to define new focus+context interaction techniques based on lenses whose transition is defined by a combination of dynamic displacement and compositing functions. We present the results of a series of user evaluations that show that one such new lens, the speed-coupled blending lens, significantly outperforms all others.

We present DynaSpot, a new technique for acquiring targets based on the area cursor. DynaSpot couples the cursor's activation area with its speed, behaving like a point cursor at low speed or when motionless. This technique minimizes visual distraction and allows pointing anywhere in empty space without requiring an explicit mode switch, thus enabling users to perform common interactions such as region selections seamlessly. The results of our controlled experiments show that the performance of DynaSpot can be modeled by Fitts' law, and that DynaSpot significantly outperforms the point cursor and achieves, in most conditions, the same level of performance as one of the most promising techniques to date, the Bubble cursor.

Applications supporting navigation in large networks are used every days by millions of people. They include road map navigators, flight route visualization systems, and network visualization systems using node-link diagrams. These applications currently provide generic interaction methods for navigation: pan-and-zoom and sometimes bird's eye views.

Focus + context techniques such as fisheye lenses are used to navigate and manipulate objects in multi-scale worlds. They provide in-place magnification of a region without requiring users to zoom the whole representation and consequently lose context. Their adoption is however hindered by usability problems mostly due to the nature of the transition between focus and context. Existing transitions are often based on a physical metaphor (magnifying glass, fisheye, rubber sheet), and are almost always achieved through a single dimension: space. We investigate how other dimensions, namely time and translucence, can be used to achieve more efficient transitions. We present an extension to Carpendale's framework for unifying presentation space accommodating these new dimensions. We define new lenses in that space, called Sigma lenses, and compare them to existing lenses through experiments based on a generic task: focus targeting. Results show that one new lens, the Speed-coupled flattening lens, significantly outperforms all others.

A number of experimental studies based on domain-specific tasks have evaluated the efficiency of navigation techniques for searching multi-scale worlds. The discrepancies among their results call for a more generic framework similar in spirit to Fitts' reciprocal pointing task, but adapted to a task that significantly differs from pure pointing. We introduce such a framework based on an abstract task and evaluate how four multi-scale navigation techniques perform in one particular multi-scale world configuration. Experimental findings indicate that, in this context, pan & zoom combined with an overview is the most efficient technique of all four, and that focus + context techniques perform better than classical pan & zoom. We relate these findings to more realistic situations, discuss their applicability, and how the framework can be used to cover a broad range of situations.

As noted almost a decade ago, HCI (human-computer interaction) aspects of visual language environments are under-developed. This remains a fact, in spite of the central role played by user interfaces in the acceptance and usability of visual languages. We introduce ZVTM, a toolkit aimed at promoting the development of HCI aspects of visual environments by making the creation of interactive structured graphical editors easier, while favoring the rapid integration of novel interaction techniques such as zoomable user interfaces, distortion lenses, superimposed layers, and alternate scrolling and pointing methods.

After a brief introduction to the Circus programming language, we present a simple type set to model XML structures. We then describe a transformation that takes a mail as input and produces a reply, showing how subtyping is used in order to refine the type control and specialize the transformation. Conclusions are drawn both on our (easy to use but clearly limited) XML data model and on Circus itself ; expected qualities of the language are verified ; the need for some new features is expressed. Finally, we sketch some language extensions, a richer model for XML structures, and explain our choices and expectations.

The domain of XML transformations is becoming more and more important as a result of the increasing number of applications adopting XML as their format for data exchange or representation. Most of the existing solutions for expressing XML transformations are textual languages, such as XSLT or DOM combined with a general-purpose programming language. Several tools build on top of these languages, providing a graphical environment. Transformations are however still specified in a textual way using the underlying language (often XSLT), thus requiring the user to learn the associated textual language.We believe that visual programming techniques are well-suited to representing XML structures and make the specification of transformations simpler. We present a visual programming language for the specification of XML transformations in an interactive environment, based on a zoomable user interface toolkit. Transformations can be run from the application or exported to two target languages: XSLT and Circus, a general-purpose structure transformation language designed by the second author and briefly introduced in this paper.