Youn ah Kang

Toward a Deeper Understanding of the Role of Interaction in Information Visualization

Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a users intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.

Evaluating visual analytics systems for investigative analysis: Deriving design principles from a case study

Despite the growing number of systems providing visual analytic support for investigative analysis, few empirical studies of the potential benefits of such systems have been conducted, particularly controlled, comparative evaluations. Determining how such systems foster insight and sensemaking is important for their continued growth and study, however. Furthermore, studies that identify how people use such systems and why they benefit (or not) can help inform the design of new systems in this area. We conducted an evaluation of the visual analytics system Jigsaw employed in a small investigative sensemaking exercise, and we compared its use to three other more traditional methods of analysis. Sixteen participants performed a simulated intelligence analysis task under one of the four conditions. Experimental results suggest that Jigsaw assisted participants to analyze the data and identify an embedded threat. We describe different analysis strategies used by study participants and how computational support (or the lack thereof) influenced the strategies. We then illustrate several characteristics of the sensemaking process identified in the study and provide design implications for investigative analysis tools based thereon. We conclude with recommendations for metrics and techniques for evaluating other visual analytics investigative analysis tools.

Characterizing the intelligence analysis process: Informing visual analytics design through a longitudinal field study

While intelligence analysis has been a primary target domain for visual analytics system development, relatively little user and task analysis has been conducted within this area. Our research communitys understanding of the work processes and practices of intelligence analysts is not deep enough to adequately address their needs. Without a better understanding of the analysts and their problems, we cannot build visual analytics systems that integrate well with their work processes and truly provide benefit to them. In order to close this knowledge gap, we conducted a longitudinal, observational field study of intelligence analysts in training within the intelligence program at Mercyhurst College. We observed three teams of analysts, each working on an intelligence problem for a ten-week period. Based upon study findings, we describe and characterize processes and methods of intelligence analysis that we observed, make clarifications regarding the processes and practices, and suggest design implications for visual analytics systems for intelligence analysis.

How do People Make Sense of Unfamiliar Visualizations?: A Grounded Model of Novice's Information Visualization Sensemaking

In this paper, we would like to investigate how people make sense of unfamiliar information visualizations. In order to achieve the research goal, we conducted a qualitative study by observing 13 participants when they endeavored to make sense of three unfamiliar visualizations (i.e., a parallel-coordinates plot, a chord diagram, and a treemap) that they encountered for the first time. We collected data including audio/video record of think-aloud sessions and semi-structured interview; and analyzed the data using the grounded theory method. The primary result of this study is a grounded model of NOvices information VIsualization Sensemaking (NOVIS model), which consists of the five major cognitive activities: 1 encountering visualization, 2 constructing a frame, 3 exploring visualization, 4 questioning the frame, and 5 floundering on visualization. We introduce the NOVIS model by explaining the five activities with representative quotes from our participants. We also explore the dynamics in the model. Lastly, we compare with other existing models and share further research directions that arose from our observations.

Examining the Use of a Visual Analytics System for Sensemaking Tasks: Case Studies with Domain Experts

While the formal evaluation of systems in visual analytics is still relatively uncommon, particularly rare are case studies of prolonged system use by domain analysts working with their own data. Conducting case studies can be challenging, but it can be a particularly effective way to examine whether visual analytics systems are truly helping expert users to accomplish their goals. We studied the use of a visual analytics system for sensemaking tasks on documents by six analysts from a variety of domains. We describe their application of the system along with the benefits, issues, and problems that we uncovered. Findings from the studies identify features that visual analytics systems should emphasize as well as missing capabilities that should be addressed. These findings inform design implications for future systems.

Visual Analytics Support for Intelligence Analysis

Intelligence analysts must explore and evaluate volumes of data, from narrative recordings of field agents to open source news articles. Insights from visual analytics projects and a hypothetical scenario show the potential of visual analytics to aid these investigations.

RevisiTour: Enriching the Tourism Experience With User-Generated Content

We have explored design opportunities to enrich the tourism experience of people at the Georgia Aquarium by providing a context of photos and by motivating people to be active creators of content to share their experiences with others. We designed a system named RevisiTour to enable visitors to reorganize photos taken from tour sites and share the photos with others. A visitor’s path and timestamp are recorded on a badge with a sensor throughout a trip. After the trip, the visitor can access a website where s/he uploads photos, synchronizes them with the path, and shares the photos with others. We report on how the system was designed, developed, and refined. After developing a prototype, we evaluated a mock-up of the system with actual visitors in the Georgia Aquarium. The analysis and design implications show the possibility of user-generated content systems for tour sites.

Characterizing the intelligence analysis process through a longitudinal field study: Implications for visual analytics

While intelligence analysis has been a primary target domain for visual analytics system development, relatively little user and task analysis has been conducted within this area. Our research community’s understanding of the work processes and practices of intelligence analysts is not deep enough to adequately address their needs. Without a better understanding of the analysts and their problems, we cannot build visual analytics systems that integrate well with their work processes and truly provide benefit to them. In order to close this knowledge gap, we conducted a longitudinal, observational field study of intelligence analysts in training within the intelligence program at Mercyhurst College. We observed three teams of analysts, each working on an intelligence problem for a 10-week period. Based on the findings of the study, we describe and characterize processes and methods of intelligence analysis that we observed, make clarifications regarding the processes and practices, and suggest design implications for visual analytics systems for intelligence analysis.

Informing design of systems for intelligence analysis: understanding users, user tasks, and tool usage

Although intelligence analysts are one of the main target users of visual analytics systems, we still do not understand their work practices and methodologies well. The lack of understanding about how intelligence analysts work and how they can benefit from visual analytics systems has created a gap between tools being developed and real world practices. I argue that we need a better understanding of these analysts and their tool usage to build systems that better support their tasks and add utility to their current work practices. By characterizing the analysis process and identifying leverage points for systems through empirical studies, I ultimately seek to develop a set of design guidelines and implications that can be used for building visual analytics systems for intelligence analysis.