Object correlations are common semantic patterns in walkthrough (WT) systems. They can be exploited for improving the effectiveness of storage caching, prefecthing, data layout, and minimization of query-response times. 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. However, those methods are presented for typical data mining datasets and not suitable for our WT system datasets. This paper proposes a class of novel and efficient pattern-growth method for mining various frequent sequential traversal patterns in the WT. Our pattern-growth method adopts a divide-and-conquer approach to decompose both the mining tasks and the databases. 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. Copyright IntroductionWith the advent of advanced computer hardware and software technologies, walkthrough (WT) are becoming larger and more complicated. To satisfy the growing demanding for fidelity, there is a need for interactive and intelligent schemes that assist and enable effective and efficient storage management. Unfortunately, it is not an easy task to exploit the intelligence in storage systems. One primary reason is the system latency between WT applications and storage systems. In such a case, WT do not consider the problem of access times of objects in the storage systems. They always simply concerned about how to display the object in the next frame. As a result, the WT can only manage data at the rendering and other related levels without knowing any semantic information such as semantic correlations between data. This motivates a more powerful analysis tool to discover more complex patterns, especially semantic patterns, in storage systems. Therefore, the aim of our work is to decrease this latency through intelligent organization of the access data and enabling the clients to perform predictive prefetching.In this paper, we consider the problem and solve this using data mining techniques. Clearly, when users traverse in a virtual environment, some potential semantic characteristics will emerge on their traversal paths. If we collect the users' traversal paths, mine and extract some kind of information of them, such meaningful semantic information can help to improve the performance of the interactive VE. For example, we can reconstruct the placement order of the objects of 3D model in disk according to the common section of users' path. Exploring these correlations is very useful for improving the effectiveness of storage caching, prefetching, data layout, and disk schedul...

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.