This paper describes a comparative experiment with five wellknown tree visualization systems, and Windows Explorer as a baseline system. Subjects performed tasks relating to the structure of a directory hierarchy, and to attributes of files and directories. Task completion times, correctness and user satisfaction were measured, and video recordings of subjects ’ interaction with the systems were made. Significant system and task type effects and an interaction between system and task type were found. Qualitative analyses of the video recordings were thereupon conducted to determine reasons for the observed differences, resulting in several findings and design recommendations as well as implications for future experiments with tree visualization systems.

Abstract—In many domains, increased collaboration has lead to more innovation by fostering the sharing of knowledge, skills, and ideas. Shared analysis of information visualizations does not only lead to increased information processing power, but team members can also share, negotiate, and discuss their views and interpretations on a dataset and contribute unique perspectives on a given problem. Designing technologies to support collaboration around information visualizations poses special challenges and relatively few systems have been designed. We focus on supporting small groups collaborating around information visualizations in a co-located setting, using a shared interactive tabletop display. We introduce an analysis of challenges and requirements for the design of colocated collaborative information visualization systems. We then present a new system that facilitates hierarchical data comparison tasks for this type of collaborative work. Our system supports multi-user input, shared and individual views on the hierarchical data visualization, flexible use of representations, and flexible workspace organization to facilitate group work around visualizations. Index Terms—Information visualization, collaboration, co-located work, hierarchical data comparison. 1

Abstract: This paper reports an ongoing longitudinal study of the adoption of information visualization systems by administrative data analysts. Participants were initially excited about the anticipated potential of visual data analysis for their work, but gradually discovered difficulties that eventually precluded a true integration of the visualization system into their daily work practices. These difficulties are unrelated to the specific visualization system used. We conclude that data analysts can take much better advantage of the benefits of information visualization systems when these systems are redesigned to be complementary products of current data analysis and workflow systems, rather than being stand-alone products as is currently the case. Our study offers some insights about how this complementarity can be achieved. Keywords: Workplace studies, Information Visualization. Category: H.1.2, H.5.2, J.1, K.4.3, K.6

In this paper we describe a model of the process by which people solve problems using information visualization systems. The model was based on video analysis of forty dyads who performed information visualization tasks in an experiment. We examined the following variables: focused questions vs. free data discovery, remote vs. collocated collaboration, and systems judged to have high and low transparency. The model describes the stages of reasoning and generating solutions with visual data. We found the model to be fairly robust across task type, collaborative setting, and system type, though subtle differences were found. We propose that system transparency can support some stages of the process, and that support is needed in the last stage to help users translate their findings from visual to written representations. 1.

We introduce grounded evaluation as a process that attempts to ensure that the evaluation of an information visualization tool is situated within the context of its intended use. We discuss the process and scope of grounded evaluation in general, and then describe how qualitative inquiry may be a beneficial approach as part of this process. We advocate for increased attention to the field of qualitative inquiry early in the information visualization development life cycle, as it tries to achieve a richer understanding by using a more holistic approach considering the interplay between factors that influence visualizations, their development, and their use. We present three case studies in which we successfully used observational techniques to inform our understanding of the visual analytics process in groups, medical diagnostic reasoning, and visualization use among computational linguists.

We present an empirical study in which we investigated group vs. individual performance with collaborative information visualization environments (CIVEs), the effects of system transparency on users' performance and the effects of different collaborative settings on CIVE usage. Subjects searched for findings with CIVEs, working either alone, in a collocated dyad using a shared electronic whiteboard, or in a remote dyad using application sharing. Groups answered more questions correctly and took less time with the more transparent CIVE than groups using the less transparent CIVE. We interpret our results to mean that groups have better self-corrective abilities when the system is transparent. We present a stage model to explain the collaborative process of using CIVEs, which accounts for task type, collaborative setting, and system transparency.

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Information visualizations can improve collaborative problem solving, but this improvement may depend on whether visualizations promote communication. In an experiment on the effect of network visualizations, remote pairs worked synchronously to identify a serial killer. They discussed disparate evidence distributed across the pair using IM. Four conditions, respectively, offered (a) spreadsheet only (controls), (b) individual unshared visualizations, (c) view-only shared visualizations, and (d) a full-access shared visualization of all evidence. We examined collaborative performance, use of the visualization tool, and communication as a function of condition. All visualization conditions improved remote collaborators ’ performance over the control condition. Full access to a shared visualization best facilitated remote collaboration by encouraging tool use and fostering discussion between the partners. Shared visualization without full access impaired performance somewhat and made communication even more vital to identifying the serial killer. This study provides direct evidence of visualization tool features and partner behavior that promote collaboration.

Abstract—To design information visualization tools that support users ’ needs, we need to understand how users engage with information visualizations in their analysis process. With the rapid growth in size and complexity of datasets, the practicality of an individual analyzing an entire dataset is becoming unrealistic. Instead, the expertise to make informed decisions about these information-rich datasets is often best accomplished by a team. However, there exist relatively few models that describe the visual analysis process, and only few studies that explore the differences between how individuals and teams use visualizations. We present an observational study where we explored the information analysis process of groups and individuals in the context of visual information. From the analysis of our study, we derive a framework that captures the activities of co-located teams and individuals engaged in information analysis. This framework has implications for the design, heuristic evaluation, and analysis of both collaborative and single-user digital information visualization tools.

Abstract. We followed an international research network that holds regular meetings in technology-enhanced working environments. The team is geographically distributed and uses a set of technical artefacts to support their collaborative work, including a videoconferencing system and a media space. We