The wide availability of touch-enabled devices is a unique opportunity for visualization research to invent novel techniques to fluently explore, analyse, and understand complex and large-scale data. In this paper, we introduce Scribble Query, a novel interaction technique for fluid freehand scribbling (casual drawing) on touch-enabled devices to support interactive querying in data visualizations. Inspired by the low-entry yet rich interaction of touch drawing applications, a Scribble Query can be created with a single touch stroke yet have the expressiveness of multiple brushes (a conventionally used interaction technique). We have applied the Scribble Query interaction technique in a multivariate visualization tool, deployed the tool with domain experts from five different domains, and conducted deployment studies with these domain experts on their utilization of multivariate visualization with Scribble Query. The studies suggest that Scribble Query has a low entry barrier facilitating easy adoption, casual and infrequent usage, and in one case, enabled live dissemination of findings by the domain expert to managers in the organization.

We present a design space exploration of interaction techniques for supporting multiple collaborators exploring data on a shared large display. Our proposed solution is based on users controlling individual lenses using both explicit gestures as well as proxemics: the spatial relations between people and physical artifacts such as their distance, orientation, and movement. We discuss different design considerations for implicit and explicit interactions through the lens, and evaluate the user experience to find a balance between the implicit and explicit interaction styles. Our findings indicate that users favor implicit interaction through proxemics for navigation and collaboration, but prefer using explicit mid-air gestures to perform actions that are perceived to be direct, such as terminating a lens composition. Based on these results, we propose a hybrid technique utilizing both proxemics and mid-air gestures, along with examples applying this technique to other datasets. Finally, we performed a usability evaluation of the hybrid technique and observed user performance improvements in the presence of both implicit and explicit interaction styles.

Topic modeling, which reveals underlying topics of a document corpus, has been actively adopted in visual analytics for large-scale document collections. However, due to its significant processing time and non-interactive nature, topic modeling has so far not been tightly integrated into a visual analytics workflow. Instead, most such systems are limited to utilizing a fixed, initial set of topics. Motivated by this gap in the literature, we propose a novel interaction technique called TopicLens that allows a user to dynamically explore data through a lens interface where topic modeling and the corresponding 2D embedding are efficiently computed on the fly. To support this interaction in real time while maintaining view consistency, we propose a novel efficient topic modeling method and a semi-supervised 2D embedding algorithm. Our work is based on improving state-of-the-art methods such as nonnegative matrix factorization and t-distributed stochastic neighbor embedding. Furthermore, we have built a web-based visual analytics system integrated with TopicLens. We use this system to measure the performance and the visualization quality of our proposed methods. We provide several scenarios showcasing the capability of TopicLens using real-world datasets.

Online comments submitted by readers of news articles can provide valuable feedback and critique, personal views and perspectives, and opportunities for discussion. The varying quality of these comments necessitates that publishers remove the low quality ones, but there is also a growing awareness that by identifying and highlighting high quality contributions this can promote the general quality of the community. In this paper we take a user-centered design approach towards developing a system, CommentIQ, which supports comment moderators in interactively identifying high quality comments using a combination of comment analytic scores as well as visualizations and flexible UI components. We evaluated this system with professional comment moderators working at local and national news outlets and provide insights into the utility and appropriateness of features for journalistic tasks, as well as how the system may enable or transform journalistic practices around online comments.

We present TimeFork, an interactive prediction technique to support users predicting the future of time-series data, such as in financial, scientific, or medical domains. TimeFork combines visual representations of multiple time series with prediction information generated by computational models. Using this method, analysts engage in a back-and-forth dialogue with the computational model by alternating between manually predicting future changes through interaction and letting the model automatically determine the most likely outcomes, to eventually come to a common prediction using the model. This computer-supported prediction approach allows for harnessing the user’s knowledge of factors influencing future behavior, as well as sophisticated computational models drawing on past performance. To validate the TimeFork technique, we conducted a user study in a stock market prediction game. We present evidence of improved performance for participants using TimeFork compared to fully manual or fully automatic predictions, and characterize qualitative usage patterns observed during the user study.

Based on kinesiology research demonstrating that translation and rotation are inseparable actions in the physical world, we present Mushaca, a 3-degrees-of-freedom mouse that senses rotation in addition to traditional planar position. We present an optical realization of the Mushaca device based on two optical sensors and then evaluate the device through a series of controlled experiments. Our results show that rotation is indeed a useful input modality for a pointing device, and also give some insight into how users perceive the changing coordinate system of the rotating mouse and adapt to this change through kinesthetic learning.