This research project addresses the fundamental question of how we can use the existing ecosystem of networked devices in our surroundings to make sense of and exploit massive, heterogeneous, and multi-scale data anywhere and at any time. Assembling these devices into unified sensemaking environments would enable deep analysis in the field. Examples include managing heterogeneous data in scientific lab notebooks, scaffolding undergraduate classroom learning with examples, manuals, and videos, and supporting police investigation by linking facts, findings, and evidence. On a higher level, this concept would stimulate our digital economy by supporting fields such as design and creativity, command and control, and scientific discovery. However, despite this ready access to a myriad of handheld devices as well as those integrated in our physical environments, each individual device is currently designed to be the focus of attention, cannot easily be combined with other devices to improve productivity, and has limited computational and storage resources. This project introduces a comprehensive new approach called ubiquitous analytics (ubilytics) for harnessing these ever-present digital devices into unified environments for anywhere analysis and sensemaking of data.
Ubilytics draws on human-computer interaction, visual analytics, and ubiquitous computing as well as a synthesis of distributed, extended, and embodied cognition, based on three principles. First, universal interaction requires designing an interaction model that combines several devices into a holistic distributed interface, transparently bridges multiple devices, surfaces, and even physical objects, and unifies interaction with various data types. Second, flexible visual structures must be created in order to generate representations that adapt to varying device dimensions, resolution, viewing angle, and distance, support space and layout management in ego-centric and world-centric configurations, and can utilize both novel and appropriated displays for output. Third, efficient distributed architecture must be achieved through methods for discovering, merging, and synchronizing heterogeneous devices with support for a generic component model to facilitate reuse, offloading costly computation into the cloud, and meshing ubilytics environments for collaboration.
Sensemaking is often attributed to professional analysts finding meaning from observed data, but this research will take a comprehensive view of sensemaking for both casual and expert users, in both dedicated and mobile settings, and with both large-scale and small-scale datasets. This work will therefore benefit society by focusing on three example domains: (1) scientific discovery, (2) classroom learning, and (3) police investigation. It will also advance discovery and understanding by integrating the research in an undergraduate programming course used as a testbed for learning in ubilytics environments. Another goal is to broaden participation of underrepresented groups by engaging in a women in engineering program as well as by mentoring minority undergraduate students during summer research internships. Results, software, and documentation will be disseminated under Open Source and Creative Commons licenses.
