Lyn Bartram

Navigating hierarchically clustered networks through fisheye and full-zoom methods

Many information structures are represented as two-dimensional networks (connected graphs) of links and nodes. Because these network tend to be large and quite complex, people often perfer to view part or all of the network at varying levels of detail. Hierarchical clustering provides a framework for viewing the network at different levels of detail by superimposing a hierarchy on it. Nodes are grouped into clusters, and clusters are themselves place into other clusters. Users can then navigate these clusters until an appropiate level of detail is reached. This article describes an experiment comparing two methods for viewing hierarchically clustered networks. Traditional full-zoom techniques provide details of only the current level of the hierarchy. In contrast, fisheye views, generated by the “variable-zoom” algorithm described in this article, provide information about higher levels as well. Subjects using both viewing methods were given problem-solving tasks requiring them to navigate a network, in this case, a simulated telephone system, and to reroute links in it. Results suggest that the greater context provided by fisheye views significantly improved user performance. Users were quicker to complete their task and made fewer unnecessary navigational steps through the hierarchy. This validation of fisheye views in important for designers of interfaces to complicated monitoring systems, such as control rooms for supervisory control and data acquistion systems, where efficient human performance is often critical. However, control room operators remained concerned about the size and visibility tradeoffs between the fine room operators remained concerned about the size and visibility tradeoffs between the fine detail provided by full-zoom techniques and the global context supplied by fisheye views. Specific interface feaures are required to reconcile the differences.

The continuous zoom: a constrained fisheye technique for viewing and navigating large information spaces

Navigating and viewing large information spaces, such as hierarchically-organized networks from complex realtime systems, suffer the problems of viewing a large space on a small screen. Distorted-view approaches, such as fisheye techniques, have great potential to reduce these problems by representing detail within its larger context but introduce new issues of focus, transition between views and user disorientation from excessive distortion. We present a fisheyebased method which supports multiple focus points, enhances continuity through smooth transitions between views, and maintains location constraints to reduce the user’s sense of spatial disorientation. These are important requirements for the representation and navigation of networked systems in supervisory control applications. The method consists of two steps: a global allocation of space to rectangular sections of the display, based on scale factors, followed by degree-of-interest adjustments. Previous versions of the algorithm relied solely on relative scale factors to assign size; we present a new version which allocates space more efficiently using a dynamically calculated degree of interest. In addition to the automatic system sizing, manual user control over the amount of space assigned each area is supported. The amount of detail shown in various parts of the network is controlled by pruning the hierarchy and presenting those sections in summary form.

Moticons: detection, distraction and task

In this paper, we describe an empirical investigation of the utility of several perceptual properties of motion in information-dense displays applied to notification. Notification relates to awareness and how dynamic information is communicated from the system to the user. Key to a notification technique is how easily the notification is detected and identified. Our initial studies show that icons with simple motions, termed moticons, are effective coding techniques for notification and in fact are often better detected and identified than colour and shape codes, especially in the periphery. A subsequent experiment compared the detection and distraction effects of different motion types in several task conditions. Our resutts reveal how different attributes of motion contribute to detection, identification and distraction and provide initial guidelines on how motion codes can be designed to support awareness in information-rich interfaces while minimizing unwanted side effects of distraction and irritation.

AMPds: A public dataset for load disaggregation and eco-feedback research

A home-based intelligent energy conservation system needs to know what appliances (or loads) are being used in the home and when they are being used in order to provide intelligent feedback or to make intelligent decisions. This analysis task is known as load disaggregation or non-intrusive load monitoring (NILM). The datasets used for NILM research generally contain real power readings, with the data often being too coarse for more sophisticated analysis algorithms, and often covering too short a time period. We present the Almanac of Minutely Power dataset (AMPds) for load disaggregation research; it contains one year of data that includes 11 measurements at one minute intervals for 21 sub-meters. AMPds also includes natural gas and water consumption data. Finally, we use AMPds to present findings from our own load disaggregation algorithm to show that current, rather than real power, is a more effective measure for NILM.

Introduction: design and evaluation of notification user interfaces

Notification systems attempt to deliver current, important information to the computer screen in an efficient and effective manner. All notification systems require that the user attends to them to at least some degree if they are to succeed. Examples of notification systems include instant messaging systems, system and user status updates, email alerts and news and stock tickers. The benefits of notification systems are numerous, including rapid availability of important information, access to nearly instantaneous communication and heightened awareness of the availability of personal contacts. While the popularity of these systems has skyrocketed in recent years, the effects of incoming notifications on ongoing computing tasks have been relatively unexplored. The investigation of the costs, benefits and the optimal display of instant messages and all notifications in the context of desktop or mobile computing tasks falls in the general arena of psychological research on alerting and disruptions, but also requires research contributions from design, computer science and information visualization. To date, much of the psychological research on interruption leverages theoretical task constructions. In this special issue, we focus on the nature of interruptions such as messaging while computing and how to optimize the user experience.

Personal Visualization and Personal Visual Analytics

Data surrounds each and every one of us in our daily lives, ranging from exercise logs, to archives of our interactions with others on social media, to online resources pertaining to our hobbies. There is enormous potential for us to use these data to understand ourselves better and make positive changes in our lives. Visualization (Vis) and visual analytics (VA) offer substantial opportunities to help individuals gain insights about themselves, their communities and their interests; however, designing tools to support data analysis in non-professional life brings a unique set of research and design challenges. We investigate the requirements and research directions required to take full advantage of Vis and VA in a personal context. We develop a taxonomy of design dimensions to provide a coherent vocabulary for discussing personal visualization and personal visual analytics. By identifying and exploring clusters in the design space, we discuss challenges and share perspectives on future research. This work brings together research that was previously scattered across disciplines. Our goal is to call research attention to this space and engage researchers to explore the enabling techniques and technology that will support people to better understand data relevant to their personal lives, interests, and needs.

Filtering and brushing with motion

Visualizing information in user interfaces to complex, large-scale systems is difficult due to visual fragmentation caused by an enormous amount of inter-related data distributed across multiple views. New display dimensions are required to help the user visually integrate and filter such spatially distributed and heterogeneous information. Motion holds promise in this regard as a perceptually efficient display dimension. It has long been known to have a strong grouping effect, suggesting it has potential for filtering and brushing techniques. However, there is little known about which properties of motion are most effective. This paper reviews the prior literature relating to the use of motion for display and discusses the requirements for how motion can be usefully applied to these problems, especially for visualizations incorporating multiple groups of data objects. Results from previous research by the authors suggested motion type was a strong distinguishing feature. Three types of motions in pairwise combinations were compared: linear, circular and expansion/contraction. Combinations of linear directions were also compared to evaluate how great angular separation needs to be to enforce perceptual distinction. The results showed that motion can effectively group objects that are otherwise dissimilar. Type differentiation is more effective than directional differences (except for 90°). Of the three types studied, circular demands the most attention. Angular separation must be 90° to be equally effective. These results suggest that motion can be usefully applied to both filtering and brushing. They also provide the beginnings of a vocabulary of simple motions that can be applied to information visualization.

Exploring Ambient and Artistic Visualization for Residential Energy Use Feedback

Providing effective feedback on resource consumption in the home is a key challenge of environmental conservation efforts. One promising approach for providing feedback about residential energy consumption is the use of ambient and artistic visualizations. Pervasive computing technologies enable the integration of such feedback into the home in the form of distributed point-of-consumption feedback devices to support decision-making in everyday activities. However, introducing these devices into the home requires sensitivity to the domestic context. In this paper we describe three abstract visualizations and suggest four design requirements that this type of device must meet to be effective: pragmatic, aesthetic, ambient, and ecological. We report on the findings from a mixed methods user study that explores the viability of using ambient and artistic feedback in the home based on these requirements. Our findings suggest that this approach is a viable way to provide resource use feedback and that both the aesthetics of the representation and the context of use are important elements that must be considered in this design space.

Chasing the Negawatt: Visualization for Sustainable Living

Energy and resource management is an important and growing research area at the intersection of conservation, sustainable design, alternative energy production, and social behavior. Energy consumption can be significantly reduced by simply changing how occupants inhabit and use buildings, with little or no additional costs. Reflecting this fact, an emerging measure of grid energy capacity is the negawatt: a unit of power saved by increasing efficiency or reducing consumption.Visualization clearly has an important role in enabling residents to understand and manage their energy use. This role is tied to providing real-time feedback of energy use, which encourages people to conserve energy.The challenge is to understand not only what kinds of visualizations are most effective but also where and how they fit into a larger information system to help residents make informed decisions. In this article, we also examine the effective display of home energy-use data using a net-zero solar-powered home (North House) and the Adaptive Living Interface System (ALIS), North Houses information backbone.

A continuously variable zoom for navigating large hierarchical networks

We present the continuous zoom, a distorted view method for displaying hierarchically-organized, two-dimensional networks. The method is suitable for large networks, such as those found in interfaces to complex supervisory control systems. The continuous zoom shows detail in context, unlike simple pan and zoom techniques, and allows for more than one focus point, unlike fisheye viewing. The algorithm and variations are described.<<ETX>>