We present RCMVis, a visual analytics system to support interactive Route Choice Modeling analysis. It aims to model which characteristics of routes, such as distance and the number of traffic lights, affect travelers' route choice behaviors and how much they affect the choice during their trips. Through close collaboration with domain experts, we designed a visual analytics framework for Route Choice Modeling. The framework supports three interactive analysis stages: exploration, modeling, and reasoning. In the exploration stage, we help analysts interactively explore trip data from multiple origin-destination (OD) pairs and choose a subset of data they want to focus on. To this end, we provide coordinated multiple OD views with different foci that allow analysts to inspect, rank, and compare OD pairs in terms of their multidimensional attributes. In the modeling stage, we integrate a k-medoids clustering method and a path-size logit model into our system to enable analysts to model route choice behaviors from trips with support for feature selection, hyperparameter tuning, and model comparison. Finally, in the reasoning stage, we help analysts rationalize and refine the model by selectively inspecting the trips that strongly support the modeling result. For evaluation, we conducted a case study and interviews with domain experts. The domain experts discovered unexpected insights from numerous modeling results, allowing them to explore the hyperparameter space more effectively to gain better results. In addition, they gained OD- and road-level insights into which data mainly supported the modeling result, enabling further discussion of the model.

Finding the propagation scope for various types of issues in Software Product Lines (SPLs) is a complicated Multi-Criteria Decision Making (MCDM) problem. This task often requires human-in-the-loop data analysis, which covers not only multiple product attributes but also contextual information (e.g., internal policy, customer requirements, exceptional cases, cost efficiency). We propose an interactive visualization tool to support MCDM tasks in software issue propagation based on the user's mental model. Our tool enables users to explore multiple criteria with their insight intuitively and find the appropriate propagation scope.

Git metadata contains rich information for developers to understand the overall context of a large software development project. Thus it can help new developers, managers, and testers understand the history of development without needing to dig into a large pile of unfamiliar source code. However, the current tools for Git visualization are not adequate to analyze and explore the metadata: They focus mainly on improving the usability of Git commands instead of on helping users understand the development history. Furthermore, they do not scale for large and complex Git commit graphs, which can play an important role in understanding the overall development history. In this paper, we present Githru, an interactive visual analytics system that enables developers to effectively understand the context of development history through the interactive exploration of Git metadata. We design an interactive visual encoding idiom to represent a large Git graph in a scalable manner while preserving the topological structures in the Git graph. To enable scalable exploration of a large Git commit graph, we propose novel techniques (graph reconstruction, clustering, and Context-Preserving Squash Merge (CSM) methods) to abstract a large-scale Git commit graph. Based on these Git commit graph abstraction techniques, Githru provides an interactive summary view to help users gain an overview of the development history and a comparison view in which users can compare different clusters of commits. The efficacy of Githru has been demonstrated by case studies with domain experts using real-world, in-house datasets from a large software development team at a major international IT company. A controlled user study with 12 developers comparing Githru to previous tools also confirms the effectiveness of Githru in terms of task completion time.

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.

We present an interactive visual analytics framework, GazeDx (abbr. of GazeDiagnosis), for the comparative analysis of gaze data from multiple readers examining volumetric images while integrating important contextual information with the gaze data. Gaze pattern comparison is essential to understanding how radiologists examine medical images, and to identifying factors influencing the examination. Most prior work depended upon comparisons with manually juxtaposed static images of gaze tracking results. Comparative gaze analysis with volumetric images is more challenging due to the additional cognitive load on 3D perception. A recent study proposed a visualization design based on direct volume rendering (DVR) for visualizing gaze patterns in volumetric images; however, effective and comprehensive gaze pattern comparison is still challenging due to a lack of interactive visualization tools for comparative gaze analysis. We take the challenge with GazeDx while integrating crucial contextual information such as pupil size and windowing into the analysis process for more in-depth and ecologically valid findings. Among the interactive visualization components in GazeDx, a context-embedded interactive scatterplot is especially designed to help users examine abstract gaze data in diverse contexts by embedding medical imaging representations well known to radiologists in it. We present the results from two case studies with two experienced radiologists, where they compared the gaze patterns of 14 radiologists reading two patients' volumetric CT images.

Most smartphones support multi-tasking with several means to switch between apps (e.g., a "recent apps" button or a "back" button). However, switching between apps is cumbersome when one has to do it frequently for example, when notifications keep interrupting one's current task. We introduce Peek-a-View, a fully transparent flipping screen cover that can reduce task switching overhead by providing an additional virtual screen space for subtasks. We assessed its feasibility in handling notifications. Upon receiving a notification, users can peek into the content of the notification without actually switching apps by slightly lifting the cover. If necessary, users can completely flip the cover to switch to the app that fired the notification. Two user studies showed that flipping and peeking interaction provided improved performance and proved to be useful for tasks that involve subtasks.

Gaze visualization has been used to understand the results from gaze tracking studies in a wide range of fields. In the medical field, diagnoses of medical images have been studied with gaze tracking technology to understand how radiologists read medical images. While prior work were mainly based on diagnosis with a single image, recent work focused on diagnosis with consecutive cross-sectional medical images acquired from preoperative computed tomography (CT) or magnetic resonance imaging (MRI). In the diagnosis, radiologists scroll through a stack of images to get a 3D cognition of organs and lesions. Thus, it is important to understand radiologists' gaze patterns three dimensionally across such contiguous cross-sectional images. However, little has been done to visualize more complicated gaze patterns from the contiguous cross-sectional medical images. To address this problem, we present an interactive 3D gaze visualization tool, GazeVis, where InfoVis and SciVis techniques are harmonized to show the abstract gaze data along with a realistic 3D rendering of the visual stimuli (i.e., organs and lesions). We present case studies with 12 radiologists who use GazeVis to investigate gaze patterns of their colleagues with different levels of expertise, providing empirical evidences about the competence of our gaze visualization system.

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.