A Gaze-enabled Graph Visualization to Improve Graph Reading Tasks

Performing typical network tasks such as node scanning and path tracing can be difficult in large and dense graphs. To alleviate this problem we use eye-tracking as an interactive input to detect tasks that users intend to perform and then produce unobtrusive visual changes that support these tasks. First, we introduce a novel fovea based filtering that dims out edges with endpoints far removed from a user's view focus. Second, we highlight edges that are being traced at any given moment or have been the focus of recent attention. Third, we track recently viewed nodes and increase the saliency of their neighborhoods. All visual responses are unobtrusive and easily ignored to avoid unintentional distraction and to account for the imprecise and low-resolution nature of eye-tracking. We also introduce a novel gaze-correction approach that relies on knowledge about the network layout to reduce eye-tracking error. Finally, we present results from a controlled user study showing that our methods led to a statistically significant accuracy improvement in one of two network tasks and that our gaze-correction algorithm enables more accurate eye-tracking interaction

Designing Attentive Information Dashboards

Information dashboards are a critical capability in contemporary business intelligence and analytics systems. Despite their strong potential to support better decision-making, the massive amount of information they provide challenges users performing data exploration tasks. Accordingly, dashboard users face difficulties in managing their limited attentional resources when processing the presented information on dashboards. Also, studies have shown that the amount of concentrated time humans can spend on a task has dramatically decreased in recent years; thus, there is a need for designing user interfaces that support users attention management. In this design science research project, we propose attentive information dashboards that provide individualized visual attention feedback (VAF) as an innovative artifact to solve this problem. We articulate theoretically grounded design principles and instantiate a software artifact leveraging users eye movement data in real time to provide individualized VAF. We evaluated the instantiated artifact in a controlled lab experiment with 92 participants. The results from analyzing users eye movement after receiving individualized VAF reveal that our proposed design has a positive effect on users attentional resource allocation, attention shift rate, and attentional resource management. We contribute a system architecture for attentive information dashboards that support data exploration and two theoretically grounded design principles that provide prescriptive knowledge on how to provide individualized VAF. Practitioners can leverage the prescriptive knowledge derived from our research to design innovative systems that support users information processing by managing their limited attentional resources

Supporting Web-based and Crowdsourced Evaluations of Data Visualizations

User studies play a vital role in data visualization research because they help measure the strengths and weaknesses of different visualization techniques quantitatively. In addition, they provide insight into what makes one technique more effective than another; and they are used to validate research contributions in the field of information visualization. For example, a new algorithm, visual encoding, or interaction technique is not considered a contribution unless it has been validated to be better than the state of the art and its competing alternatives or has been validated to be useful to intended users. However, conducting user studies is challenging, time consuming, and expensive. User studies generally requires careful experimental designs, iterative refinement, recruitment of study participants, careful management of participants during the run of the studies, accurately collecting user responses, and expertise in statistical analysis of study results. There are several variables that are taken into consideration which can impact user study outcome if not carefully managed. Hence the process of conducting user studies successfully can take several weeks to months. In this dissertation, we investigated how to design an online framework that can reduce the overhead involved in conducting controlled user studies involving web-based visualizations. Our main goal in this research was to lower the overhead of evaluating data visualizations quantitatively through user studies. To this end, we leveraged current research opportunities to provide a framework design that reduces the overhead involved in designing and running controlled user studies of data visualizations. Specifically, we explored the design and implementation of an open-source framework and an online service (VisUnit) that allows visualization designers to easily configure user studies for their web-based data visualizations, deploy user studies online, collect user responses, and analyze incoming results automatically. This allows evaluations to be done more easily, cheaply, and frequently to rapidly test hypotheses about visualization designs. We evaluated the effectiveness of our framework (VisUnit) by showing that it can be used to replicate 84% of 101 controlled user studies published in IEEE Information Visualization conferences between 1995 and 2015. We evaluated the efficiency of VisUnit by showing that graduate students can use it to design sample user studies in less than an hour. Our contributions are two-fold: first, we contribute a flexible design and implementation that facilitates the creation of a wide range of user studies with limited effort; second, we provide an evaluation of our design that shows that it can be used to replicate a wide range of user studies, can be used to reduce the time evaluators spend on user studies, and can be used to support new research

The state of the art in empirical user evaluation of graph visualizations

While graph drawing focuses more on the aesthetic representation of node-link diagrams, graph visualization takes into account other visual metaphors making them useful for graph exploration tasks in information visualization and visual analytics. Although there are aesthetic graph drawing criteria that describe how a graph should be presented to make it faster and more reliably explorable, many controlled and uncontrolled empirical user studies flourished over the past years. The goal of them is to uncover how well the human user performs graph-specific tasks, in many cases compared to previously designed graph visualizations. Due to the fact that many parameters in a graph dataset as well as the visual representation of them might be varied and many user studies have been conducted in this space, a state-of-the-art survey is needed to understand evaluation results and findings to inform the future design, research, and application of graph visualizations. In this paper, we classify the present literature on the topmost level into graph interpretation, graph memorability, and graph creation where the users with their tasks stand in focus of the evaluation not the computational aspects. As another outcome of this work, we identify the white spots in this field and sketch ideas for future research directions