Enabling Collaborative Visual Analysis across Heterogeneous Devices

Abstract

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 with 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 using visualization. It 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. This dissertation covers interaction models, coordination techniques, and software platforms to enable full stack support for C2-VA. Firstly, we connected devices to form an ecosystem using software primitives introduced in the early frameworks from this dissertation. To work in a device ecosystem, we designed multi-user interaction for visual analysis in front of large displays by finding a balance between proxemics and mid-air gestures. Extending these techniques, we considered the roles of different devices–large and small–to present a conceptual framework for utilizing multiple devices for visual analytics. When applying this framework, findings from a user study showcase flexibility in the analytic workflow and potential for generation of complex insights in device ecosystems. Beyond this, we supported coordination between multiple users in a device ecosystem by depicting the presence, attention, and data coverage of each analyst within a group. Building on these parts of the C2-VA stack, the culmination of this dissertation is a platform called Vistrates. This platform introduces a component model for modular creation of user interfaces that work across multiple devices and users. A component is an analytical primitive–a data processing method, a visualization, or an interaction technique–that is reusable, composable, and extensible. Together, components can support a complex analytical activity. On top of the component model, the support for collaboration and device ecosystems comes for granted in Vistrates. Overall, this enables the exploration of new research ideas within C2-VA.