Hyunjoo Song

A comparative evaluation on tree visualization methods for hierarchical structures with large fan-outs

Hierarchical structures with large fan-outs are hard to browse and understand. In the conventional node-link tree visualization, the screen quickly becomes overcrowded as users open nodes that have too many child nodes to fit in one screen. To address this problem, we propose two extensions to the conventional node-link tree visualization: a list view with a scrollbar and a multi-column interface. We compared them against the conventional tree visualization interface in a user study. Results show that users are able to browse and understand the tree structure faster with the multi-column interface than the other two interfaces. Overall, they also liked the multi-column better than others.

ChartSense: Interactive Data Extraction from Chart Images

Charts are commonly used to present data in digital documents such as web pages, research papers, or presentation slides. When the underlying data is not available, it is necessary to extract the data from a chart image to utilize the data for further analysis or improve the chart for more accurate perception. In this paper, we present ChartSense, an interactive chart data extraction system. ChartSense first determines the chart type of a given chart image using a deep learning based classifier, and then extracts underlying data from the chart image using semi-automatic, interactive extraction algorithms optimized for each chart type. To evaluate chart type classification accuracy, we compared ChartSense with ReVision, a system with the state-of-the-art chart type classifier. We found that ChartSense was more accurate than ReVision. In addition, to evaluate data extraction performance, we conducted a user study, comparing ChartSense with WebPlotDigitizer, one of the most effective chart data extraction tools among publicly accessible ones. Our results showed that ChartSense was better than WebPlotDigitizer in terms of task completion time, error rate, and subjective preference.

DiffMatrix: Matrix-based Interactive Visualization for Comparing Temporal Trends

Line graphs have been commonly used for visualizing temporal trends in time series data. Since comparing trends is one of the main tasks for analyzing multiple temporal trends, many efforts have been made to enhance visual representations of line graphs to help people efficiently compare multiple temporal trends. However, as the number of line graphs increases, the overlap makes it difficult to perform comparison and other analyses. In this paper, we introduce DiffMatrix, a matrix-based interactive visualization designed to support effective analyses of a large number of time series data. It employs four visual representations for each cell in the matrix to show the difference between two time series—dual lines, diff line, diff area, diff heatmap—and a detail view to support more indepth analyses on individual line graphs. DiffMatrix allows users to seamlessly switch between these representations that best support their tasks. We also report possible future work we identified through case studies with three real-world time series datasets with a large number of series.

Adaptive Zoom-based Gaze Tracking for Enhanced Accuracy and Precision

The accuracy and precision of video-based remote gaze trackers is affected by numerous factors (e.g. the head movement of the participant). However, it is challenging to control all factors that have an influence, and doing so (e.g., using a chin-rest to control geometry) could lead to losing the benefit of using gaze trackers, i.e., the ecological validity of their unobtrusive nature. We propose an adaptive zoom-based gaze tracking technique, ZoomTrack that addresses this problem by improving the resolution of the gaze tracking results. Our approach magnifies a region-of-interest (ROI) and retrieves gaze points at a higher resolution under two different zooming modes: only when the gaze reaches the ROI (temporary) or whenever a participant stares at the stimuli (omnipresent). We compared these against the base case without magnification in a user study. The results are then used to summarize the advantages and limitations of our technique.