Sungwon In
The emergence of immersive technologies shows great potential for data analysis, leading to the development of immersive data science tools focusing on specific tasks (e.g., data transformation and visualization). However, real-world data analysis is iterative, requiring frequent tool-switching, which incurs high context-switching costs and disrupts the analytical flow. Computational notebooks integrate multiple data science tools in one space, but their standard WIMP (windows, icons, menus, pointers) design cannot fully leverage immersive environments’ spatial and embodied capabilities. In response, we introduce ICoN, a prototype system that combines computational notebooks with embodied workspaces into one unified space, taking advantage of each platform’s advantages. To provide empirical knowledge regarding ICoN’s two major characteristics (i.e., immersion and unified workspace), we conducted controlled comparisons of desktop vs. VR and separated vs. unified workspaces. We found that ICoN benefited from the spatial and embodied interactions provided by the immersive environment and seamless transitions within its unified workspace.
Suggested Papers
Immersive Computational Notebook
Immersive Data Transformation
Immersive Genome Information
Suggested Demo Videos
1. ICoN: Immersive Computational Notebook for Data Science
2. Evaluating a Navigation and Comparison in Immersive Computational Notebook
3. Immersive Data Transformation