Sequential Traversal Patterns Research Articles

Utilizing Traveral Sequence Order for Storage Layout in Walkthrough

3D walkthrough (henceforth referred as WT) becomes popular and apparent when the volume of query processing in some 3D scenery (e.g., the walkthrough system and the 3D museum navigation systems) is considered. In this scenario, different people with similar behaviours induce completely different space-time traversal patterns in a recoded traversal sequence. This is because they navigate different paths, and their surrounding backgrounds are different. What are common across such sequences of the same behaviours are the underlying induced walkthrough fields. We want to make use of regions of common sequential traversal patterns for acceleration and compaction purposes. In this paper we propose a new Sequence-based Pattern Similarity (SPS) approach based on a behavioural walkthrough system that exploits sequence-based semantic-oriented clustering techniques, such as association, intra-relationships, and inter-relationships, to explore additional links throughout the behavioural walkthrough system. The experimentation shows that in such cases the use of so-called I/O-efficient algorithms that minimize the number of disk accesses can lead to tremendous runtime improvements.

Efficient Reduction of Access Latency through Object Correlations in Virtual Environments

Object correlations are common semantic patterns in virtual environments. They can be exploited to improve the effectiveness of storage caching, prefetching, data layout, and disk scheduling. However, we have little approaches for discovering object correlations in VE to improve the performance of storage systems. Being an interactive feedback-driven paradigm, it is critical that the user receives responses to his navigation requests with little or no time lag. Therefore, we propose a class of view-based projection-generation method for mining various frequent sequential traversal patterns in the virtual environments. The frequent sequential traversal patterns are used to predict the user navigation behavior and, through clustering scheme, help to reduce disk access time with proper patterns placement into disk blocks. Finally, the effectiveness of these schemes is shown through simulation to demonstrate how these proposed techniques not only significantly cut down disk access time, but also enhance the accuracy of data prefetching.

Using prefetching to improve walkthrough latency

AbstractObject 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 © 2006 John Wiley & Sons, Ltd.