Using prefetching to improve walkthrough latency

Abstract

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...

Efficient 3-D Scene Prefetching From Learning User Access Patterns

“…In their scheme, the user's interest is simply defined as the number of accesses to a given object. Hung et al [28] mine the correlations among 3-D objects and use the association rules thus obtained to reconstruct the placement order of 3-D objects in the storage system to improve disk I/O performance. Rahimi et al [29] propose a context-aware prioritized 3-D streaming algorithm that uses a hidden Markov model (HMM) to predict a user's future activities in a 3-D game.…”

Utilizing Traveral Sequence Order for Storage Layout in Walkthrough

“…Instead, they do not require any knowledge of cache parameters or block sizes of the memory hierarchy involved in the computation. In addition, large polygons of such highly complex scenes require a lot of hard disk space so that the additional data could exceed the available capacities [4,9,10,11]. Moreover, the semantics of data access is more important in defining the placement policy [2,3,4,11].…”

“…In addition, large polygons of such highly complex scenes require a lot of hard disk space so that the additional data could exceed the available capacities [4,9,10,11]. Moreover, the semantics of data access is more important in defining the placement policy [2,3,4,11]. To meet these requirements, an appropriate data structure and an efficient technique should be developed with the constraints of memory consumption.…”

“…Well known drawbacks of traditional geometric scene modelers make it difficult, and even sometimes impossible, intuitive design of 3D scenes [2][3][4]7,9,10]. In this section we will discuss on intelligent storage layout modeling, that is modeling using intelligent techniques during the designing process and thus allowing intuitive storage layout design.…”

“…What are common across such sequences of the same behaviours are the underlying induced walkthrough fields. This observation was presented [1][2][3][4][5], where data mining schemes were applied for traversal behaviour recognition. However, dense, continuous, and sequence-based traversal behaviour estimation is highly noisy and unreliable.…”