PhD Defense: Enabling Collaborative Visual Analysis across Heterogeneous Devices
We are surrounded by novel device technologies emerging at an unprecedented pace. These devices are heterogeneous in nature: in large and small sizes with many input and sensing mechanisms. When many such devices are used by multiple users towards a shared goal, they form a heterogeneous device ecosystem. A device ecosystem has great potential in data science to act as a natural medium for multiple analysts to make sense of data through visualizations. This is essential as today's big data problems require more than a single mind or a single machine to solve them. Towards this vision, I introduce the concept of collaborative, cross-device visual analytics (C2-VA) and outline a reference model to develop user interfaces for C2-VA. Settings for C2-VA are rich with devices and users. They can increase the analytic bandwidth by tapping into the collective intelligence of the users.This dissertation presents interaction models, coordination techniques, and software platforms for visual analysis to enable C2-VA. Firstly, to connect many devices and users in visual analysis, PolyChrome and Munin frameworks provide the software primitives for C2-VA applications. To utilize heterogeneous devices, the Proxemic Lens technique introduces multi-user interaction based on proxemics and gestures for visual analysis on large wall-sized displays. Building on top, Visfer, and David and Goliath techniques consider the roles of large displays and small devices to propose cross-device interactions that help users work flexibly and develop better insights. To help multiple users coordinate in a device ecosystem, the InsightsDrive tool provides group awareness to understand the data coverage of each collaborator in a team of analysts.To combine the knowledge from this research, the Vistrates platform introduces a component model for modular creation of user interfaces for C2-VA. Vistrates is the culmination of this dissertation but it exposes new opportunities for future research in this direction. With Vistrates, the support for collaboration and device ecosystems comes for granted, which enables us to investigate new ideas on top for data exploration and communication. In fact, it is already being used beyond our group to answer new research questions in C2-VA. Overall, the outcomes of this dissertation help multiple users use heterogeneous devices for visual analytics.
Chair: Dr. Niklas Elmqvist Dean's rep: Dr. Hector Corrada Bravo Members: Dr. Catherine Plaisant Dr. Jean-Daniel Fekete Dr. Eun Kyuong Choe Dr. Huaishu Peng