XVCollab: An Immersive Analytics Tool for Asymmetric Collaboration across the Virtuality Spectrum Misc

Mohammad Rajabi Seraji, Wolfgang Stuerzlinger

Abstract:

Research has shown that when a group of people collaborate in decision-making scenarios, they can be more effective than when they work alone. Studies also show that in a data analytics context, using immersive technologies could make users perform better in data understanding, pattern recognition, and finding connections. In this work, we are leveraging previous knowledge in Collaborative Immersive Analytics (CIA) and Cross-virtuality Analytics (XVA) to develop an asymmetric system that enables two groups from different places on the Virtuality-Reality spectrum to simultaneously work on analyzing data. We divide users into two groups: the non-immersive desktop group and the immersive AR group. These two groups can both author and modify visualizations in their virtuality and share it with the other group when they see fit. For this, we designed a non-interruptive interface for both groups to transform a visualization from non-immersive 2D to immersive AR and vice-versa. We also provide multiple awareness cues in the system that keep either group aware of the other and their actions. We designed these features to boost user performance and ease of use in a collaborative setting and incentivize them to rely on the other group for visualization tasks that are difficult to perform on their end of the virtuality spectrum. Our limited pilot study shows that users find the system engaging, easy to use, and helpful in their data-understanding journey within the collaborative context. Going forward, we plan to conduct more rigorous studies to verify our claims and explore other research questions on this topic.

Date of publication: Oct - 2022
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