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Object correlations are common semantic patterns in virtual reality systems. They can be exploited for improving the effectiveness of storage caching, prefecthing, data layout, and minimization of queryresponse times. Unfortunately, this information about object correlations is unavailable at the storage system level. Previous approaches for reducing I/O access time are seldom investigated. On the other side, data mining techniques extract implicit, previously unknown and potentially useful information from the databases. This paper proposes a class of novel and efficient pattern-growth method for mining various frequent sequential traversal patterns in the virtual reality. Our pattern-growth method adopts a divideand-conquer approach to decompose both the mining tasks and the databases. Moreover, our efficient data structures are proposed to avoid expensive, repeated database scans. The frequent sequential traversal patterns are used to predict the user navigation behavior and help to reduce disk access time with proper placement patterns into disk blocks. We also define the terminologies such as paths, views and objects used in the model. We have done extensive experiments to demonstrate how these proposed techniques not only significantly cut down disk access time, but also enhance the accuracy of data prefetching.

Mining Objects Correlations to Improve Interactive Virtual Reality Latency