Chris Rooney

A User Study on Curved Edges in Graph Visualization

Recently there has been increasing research interest in displaying graphs with curved edges to produce more readable visualizations. While there are several automatic techniques, little has been done to evaluate their effectiveness empirically. In this paper we present two experiments studying the impact of edge curvature on graph readability. The goal is to understand the advantages and disadvantages of using curved edges for common graph tasks compared to straight line segments, which are the conventional choice for showing edges in node-link diagrams. We included several edge variations: straight edges, edges with different curvature levels, and mixed straight and curved edges. During the experiments, participants were asked to complete network tasks including determination of connectivity, shortest path, node degree, and common neighbors. We also asked the participants to provide subjective ratings of the aesthetics of different edge types. The results show significant performance differences between the straight and curved edges and clear distinctions between variations of curved edges.

INVISQUE: intuitive information exploration through interactive visualization

In this paper we present INVISQUE, a novel system designed for interactive information exploration. Instead of a conventional list-style arrangement, in INVISQUE information is represented by a two-dimensional spatial canvas, with each dimension representing user-defined semantics. Search results are presented as index cards, ordered in both dimensions. Intuitive interactions are used to perform tasks such as keyword searching, results browsing, categorizing, and linking to online resources such as Google and Twitter. The interaction-based query style also naturally lends the system to different types of user input such as multi-touch gestures. As a result, INVISQUE gives users a much more intuitive and smooth experience of exploring large information spaces.

INVISQUE as a tool for intelligence analysis: the construction of explanatory narratives

This article reports an exploratory user study in which a group of civil servants with experience of, or involvement in, intelligence analysis used the tool INVISQUE to address a problem using the 2011 VAST data set. INVISQUE uses a visual metaphor that combines searching, clustering, and sorting of document surrogates with free-form manipulation on an infinite canvas. The study looks into exposing the behaviors and related cognitive strategies that users would employ to better understand how this and similar environments might better support intelligence type work. The results include the observation that the search and spatial features of the system supported participants in establishing, elaborating, and systematically evaluating explanatory narratives that accounted for the data. Also, visual persistence at the interface allowed them to keep track of searches and to re-find documents when their importance became apparent. The article concludes with reflections on our findings and propose a set of guidelines for developing systems that support sensemaking.

Interactive visualization for low literacy users: from lessons learnt to design

This paper aims to address the problems low literacy (LL) users face when searching for information online. The first part of this paper summarizes the problems that LL users face, and establishes a set of design principles for interfaces suitable for LL users. This is followed by a description of how these design principles are mapped to a novel interface for interactive data retrieval. The interface was realized into a working system and evaluated against a traditional web interface for both high literacy (HL) and LL users. The suitability of the designs was analyzed using performance data, subjective feedback and an observational analysis. The findings from the study suggest that LL users perform better and prefer the proposed designs over a traditional web interface.

Improving Window Manipulation and Content Interaction on High-Resolution, Wall-Sized Displays

Interaction with high-resolution wall-sized (Powerwall) displays can be a tedious and difficult task due to large display areas and small target sizes. To overcome this, we developed techniques that reduce the precision required to manipulate windows and select data. The manipulation layer speeds up the common tasks of moving and resizing application windows by overlaying them with large, transparent target areas. The Power-Lens magnifies target sizes by automatically appearing once the cursor reaches the region of interest. Two experiments evaluated these techniques against conventional desktop-style interfaces. Experiment 1 showed the window manipulation layer to speed up the tasks of moving and resizing a window by 24% and 27%, respectively. Experiment 2 showed the Power-Lens to speed up the selection of 5 × 5 pixel targets by 18%. Together, our new techniques help to make interaction more fluid on Powerwall displays.

A New Method for Interacting with Multi-Window Applications on Large, High Resolution Displays

Physically large display walls can now be constructed using off-the-shelf computer hardware. The high resolution of these displays (e.g., 50 million pixels) means that a large quantity of data can be presented to users, so the displays are well suited to visualization applications. However, current methods of interacting with display walls are somewhat time consuming. We have analyzed how users solve real visualization problems using three desktop applications (XmdvTool, Iris Explorer and Arc View), and used a new taxonomy to classify users’ actions and illustrate the deficiencies of current display wall interaction methods. Following this we designed a novel methodfor interacting with display walls, which aims to let users interact as quickly as when a visualization application is used on a desktop system. Informal feedback gathered from our working prototype shows that interaction is both fast and fluid.

POWERWALL: int. workshop on interactive, ultra-high-resolution displays

Ultra-high-resolution (Powerwall) displays offer a vast real estate for displaying data, but also present a new set of interaction issues. This workshop aims to bring together researchers and practitioners in the research area of Powerwall technologies. The workshop focuses on four key research questions: how do the interaction metaphors change as we move away from desktop interaction? How do we make the most of the visual resolution for data visualization? How can the physical size support collaborative work? And what are the lessons learnt from installing such displays?

Trialling the SMART Approach Identifying and Assessing Sense-making

It is important to develop tools that support sense-making by providing representations that help to capture the externalisation of the thinking process. The paper proposes SMART, a new method for identifying the sense-making processes of experts by combing probes with cognitive task analysis methods. The Data-Frame sense-making model is used as a theoretical frame, and the probes have been developed around the model to elicit experts’ sense-making strategies. However, we found that the SMART probes presently lacked the resolution to capture the experts sense-making and a stronger emphasis of cognitive task analysis methods and observations were required to interpret the findings.

Police Analyst Workstation: Towards a Multi-Surface User Interface

Developing applications for multi-surface user interfaces is challenging. Sharing and transferring information between these surfaces requires the need for multi-modal interaction methods. In this paper we describe the Police Analyst work- station for supporting multi-surface interaction for criminal intelligence analysis with sense making using multi-touch and mid-air hand gestures for input. We outline our requirements, design, and an initial implementation.

HiReD: a high-resolution multi-window visualisation environment for cluster-driven displays

High-resolution, wall-size displays often rely on bespoke software for performing interactive data visualisation, leading to interface designs with little or no consistency between displays. This makes adoption for novice users difficult when migrating from desktop environments. However, desktop interface techniques (such as task- and menu- bars) do not scale well and so cannot be relied on to drive the design of large display interfaces. In this paper we present HiReD, a multi-window environment for cluster-driven displays. As well as describing the technical details of the system, we also describe a suite of low-precision interface techniques that aim to provide a familiar desktop environment to the user while overcoming the scalability issues of high-resolution displays. We hope that these techniques, as well as the implementation of HiReD itself, can encourage good practice in the design and development of future interfaces for high-resolution, wall-size displays.