In many virtual environments (VE) applications, the size of the database is not only extremely large, it is also growing rapidly. However, little approaches for discovering object correlations in VE to improve the performance of storage systems. In this paper, we develop a class of view-based projection-generation method for mining various frequent sequential traversal patterns in the VE. The frequent sequential traversal patterns are used to predict the user navigation behavior. Furthermore, the hypergraph (HG)-based clustering scheme can help reduce disk access time with proper placement patterns into disk blocks. Finally, 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.