DONNA: A Data Model for Enabling Extensible and Efficient Metaverse Applications

Abstract

The advent of Metaverse applications, that exploit extended reality technologies, has the potential to disrupt multiple industries including gaming, social networks, entertainment and travel. While there has been initial work on network, compute and synchronization features needed for the Metaverse, a comprehensive data model that captures the interactions between the physical and virtual worlds has not been evaluated extensively. Providing a formal data model would be crucial to ensure interoperability, model extensibility and applicability to multiple use cases. In this paper, we propose DONNA: A Data Model for Enabling Extensible and Efficient Metaverse Applications. DONNA provides a detailed data model of interactions between physical space, virtual spaces, sensors, devices, physical participants and avatars. Via the use of property graph schemas, we demonstrate the varied interactions between the physical and virtual worlds and the extensibility of the approach across multiple use cases. The data model is specifically demonstrated over a virtual museum visit use case to explain the nuances of sensing, dynamic property update and semantic interactions between physical and virtual objects.