論文アブストラクト： We explore the combination of smartwatches and a large interactive display to support visual data analysis. These two extremes of interactive surfaces are increasingly popular, but feature different characteristics-display and input modalities, personal/public use, performance, and portability. In this paper, we first identify possible roles for both devices and the interplay between them through an example scenario. We then propose a conceptual framework to enable analysts to explore data items, track interaction histories, and alter visualization configurations through mechanisms using both devices in combination. We validate an implementation of our framework through a formative evaluation and a user study. The results show that this device combination, compared to just a large display, allows users to develop complex insights more fluidly by leveraging the roles of the two devices. Finally, we report on the interaction patterns and interplay between the devices for visual exploration as observed during our study.
論文アブストラクト： With the rise of digital photography and social networking, people are sharing personal photos online at an unprecedented rate. In addition to their main subject matter, photographs often capture various incidental information that could harm people's privacy. While blurring and other image filters may help obscure private content, they also often affect the utility and aesthetics of the photos, which is important since images shared in social media are mainly for human consumption. Existing studies of privacy-enhancing image filters either primarily focus on obscuring faces, or do not systematically study how filters affect image utility. To understand the trade-offs when obscuring various sensitive aspects of images, we study eleven filters applied to obfuscate twenty different objects and attributes, and evaluate how effectively they protect privacy and preserve image quality for human viewers.
論文アブストラクト： Immersive technologies such as augmented reality devices are opening up a new design space for the visual analysis of data. This paper studies the potential of an augmented reality environment for the purpose of collaborative analysis of multidimensional, abstract data. We present ART, a collaborative analysis tool to visualize multidimensional data in augmented reality using an interactive, 3D parallel coordinates visualization. The visualization is anchored to a touch-sensitive tabletop, benefiting from well-established interaction techniques. The results of group-based, expert walkthroughs show that ART can facilitate immersion in the data, a fluid analysis process, and collaboration. Based on the results, we provide a set of guidelines and discuss future research areas to foster the development of immersive technologies as tools for the collaborative analysis of multidimensional data.
論文アブストラクト： Effective use of data involving personal or sensitive information often requires different people to have access to personal information, which significantly reduces the personal privacy of those whose data is stored and increases risk of identity theft, data leaks, or social engineering attacks. Our research studies the tradeoffs between privacy and utility of personal information for human decision making. Using a record-linkage scenario, this paper presents a controlled study of how varying degrees of information availability influences the ability to effectively use personal information. We compared the quality of human decision-making using a visual interface that controls the amount of personal information available using visual markup to highlight data discrepancies. With this interface, study participants who viewed only 30% of data content had decision quality similar to those who had full 100% access. The results demonstrate that it is possible to greatly limit the amount of personal information available to human decision makers without negatively affecting utility or human effectiveness. However, the findings also show there is a limit to how much data can be hidden before negatively influencing the quality of judgment in decisions involving person-level data. Despite the reduced accuracy with extreme data hiding, the study demonstrates that with proper interface designs, many correct decisions can be made with even legally de-identified data that is fully masked (74.5% accuracy with fully-masked data compared to 84.1% with full access). Thus, when legal requirements only allow for de-identified data access, use of well-designed interface can significantly improve data utility.