CDI Type I: Virtual Worlds: Scalability and Content Creation

Stanford University, Stanford CA

Investigators

Abstract

Virtual worlds are networked three-dimensional environments that simulate physical interaction in three-dimensional spaces and decouple such interaction from geographic constraints. Virtual worlds open new avenues for education, business, and scientific discovery, and can significantly enhance collaboration in virtual organizations. To accomplish this requires scalable and secure system architectures, complemented by appropriate tools for creating virtual world content. This project aims to design, build, deploy, and evaluate a virtual world platform for use as a research and development platform for virtual world technology and applications. The two major research thrusts are system architecture and content creation. For system architecture, the project will develop: (1) continuous dynamic world partitioning for maximal resource utilization and fault tolerance, (2) a capability-based security engine that seamlessly enforces access controls, (3) a scalable content distribution network for bandwidth-intensive virtual world content, and (4) privacy-preserving archiving of virtual world events for scientific research and exploration. For content creation, the project will investigate domain-specific modeling tools that leverage domain knowledge and community input to drastically ease the three-dimensional modeling process. The project significantly advances the state of the art in virtual world systems and three-dimensional content creation. The scalable and secure virtual world platform will be able to support millions of participants concurrently interacting in a shared three-dimensional simulated environment. The novel content creation methodologies will enable untrained participants to create unique high-quality three-dimensional objects for a variety of application domains. Dedicated data collection capabilities will support social science, legal, and economic research on previously unseen scales, with analysis of large-scale behavioral data that may yield deep and novel insights into human and societal behavior.