Mining Objects Correlations to Improve Interactive Virtual Reality Latency

DOI

10.2991/jcis.2006.104How to use a DOI?

Keywords

Virtual reality, frequent pattern mining, clustering, object correlations, data layout

Abstract

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 query-response 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 divide-and-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.

Copyright

© 2006, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shao-Shin Hung
AU  - Hsing-Jen Chen
AU  - Damon Shing-Min Liu
PY  - 2006/10
DA  - 2006/10
TI  - Mining Objects Correlations to Improve Interactive Virtual Reality Latency
BT  - Proceedings of the 9th Joint International Conference on Information Sciences (JCIS-06)
PB  - Atlantis Press
SN  - 1951-6851
UR  - https://doi.org/10.2991/jcis.2006.104
DO  - 10.2991/jcis.2006.104
ID  - Hung2006/10
ER  -