Using prefetching to improve walkthrough latency
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 lVF 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 (c) 2006 John Wiley & Sons, Ltd.
prefetching walkthrough latency mining clustering pattern growth