Skip to main content


  • Research Article
  • Open Access
  • Efficient Reduction of Access Latency through Object Correlations in Virtual Environments

    EURASIP Journal on Advances in Signal Processing20072007:010289

    • Received: 1 September 2006
    • Accepted: 22 February 2007
    • Published:


    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.


    • Virtual Environment
    • Storage System
    • Cluster Scheme
    • Efficient Reduction
    • Access Latency

    Authors’ Affiliations

    Department of Computer Science and Information Engineering, National Chung Cheng University, Chia-Yi, 62107, Taiwan


    1. Christodoulakis S, Triantafillou P, Zioga F: Principles of optimally placing data in tertiary storage libraries. Proceedings of the 23rd International Conference on Very Large Data Bases (VLDB '97), August 1997, Athens, Greece 236–245.Google Scholar
    2. More S, Muthukrishnan S, Shriver EAM: Efficiently sequencing tape-resident jobs. Proceedings of the 18th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems (PODS '99), May-June 1999, Philadelphia, Pa, USA 33–43.View ArticleGoogle Scholar
    3. Sarawagi S: Database systems for efficient access to tertiary memory. Proceedings of the 14th IEEE Symposium on Mass Storage Systems (MSS '95), September 1995, Monterey, Calif, USA 120–126.View ArticleGoogle Scholar
    4. Yu J, Dewitt DJ: Processing satellite images on tertiary storage: a study of the impact of tile size on performance. Proceedings of the 5th NASA Goddard Conference on Mass Storage Systems and Technologies, September 1996, College Park, Md, USA 460–476.Google Scholar
    5. Thereska E, Abd-El-Malek M, Wylie JJ, Narayanan D, Ganger GR: Informed data distribution selection in a self-predicting storage system. Proceedings of the 3rd IEEE International Conference on Autonomic Computing (ICAC '06), June 2006, Dublin, Ireland 187–198.Google Scholar
    6. Amer A, Long DDE, Paris J-F, Burns RC: File access prediction with adjustable accuracy. Proceedings of 21st IEEE International Performance, Computing, and Communications Conference (IPCCC '02), April 2002, Phoenix, Ariz, USA 131–140.Google Scholar
    7. Yang J, Ward MO, Rundensteiner EA, Huang S: Visual hierarchical dimension reduction for exploration of high dimensional datasets. Proceedings of Symposium on Visualization (VisSym '03), May 2003, Grenoble, France 19–28.Google Scholar
    8. Cignoni P, Montani C, Rocchini C, Scopigno R: External memory management and simplification of huge meshes. IEEE Transactions on Visualization and Computer Graphics 2003,9(4):525-537. 10.1109/TVCG.2003.1260746View ArticleGoogle Scholar
    9. Hoppe H: Progressive meshes. Proceedings of the 23rd Annual Conference on Computer Graphics (SIGGRAPH '96), August 1996, New Orleans, La, USA 99–108.Google Scholar
    10. Yoon S-E, Manocha D: Cache-efficient layouts of bounding volume hierarchies. Computer Graphics Forum 2006,25(3):507-516. 10.1111/j.1467-8659.2006.00970.xView ArticleGoogle Scholar
    11. Yoon S-E, Lindstrom P, Pascucci V, Manocha D: Cache-oblivious mesh layouts. ACM Transactions on Graphics 2005,24(3):886-893. 10.1145/1073204.1073278View ArticleGoogle Scholar
    12. Yoon S-E, Lindstrom P: Mesh layouts for block-based caches. IEEE Transactions on Visualization and Computer Graphics 2006,12(5):1213-1220.View ArticleGoogle Scholar
    13. Chakrabarti S: Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann, San Francisco, Calif, USA; 2003.Google Scholar
    14. Chen M-S, Park JS, Yu PS: Efficient data mining for path traversal patterns. IEEE Transactions on Knowledge and Data Engineering 1998,10(2):209-221. 10.1109/69.683753View ArticleGoogle Scholar
    15. Pei J, Han J, Mortazavi-Asl B, et al.: PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth. Proceedings of the 17th International Conference on Data Engineering (ICDE '01), April 2001, Heidelberg, Germany 215–224.Google Scholar
    16. Han J, Pei J, Mortazavi-Asl B, Chen Q, Dayal U, Hsu M: FreeSpan: frequent pattern-projected sequential pattern mining. Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '00), August 2000, Boston, Mass, USA 355–359.View ArticleGoogle Scholar
    17. Kohavi R, Brodley CE, Frasca B, Mason L, Zheng Z: KDD-Cup 2000 organizers' report: peeling the onion. SIGKDD Explorations 2000,2(2):86-98. 10.1145/380995.381033View ArticleGoogle Scholar
    18. Sarawagi S, Stonebraker M: Efficient organization of large multidimensional arrays. Proceedings of the 10th International Conference Data Engineering (ICDE '94), February 1994, Houston, Tex, USA 328–336.Google Scholar
    19. More S, Choudhary A: Tertiary storage organization for large multidimensional datasets. Proceedings of the 8th NASA Goddard Space Flight Center Conference on Mass Storage Systems and Technologies in Cooperation with the 17th IEEE Symposium on Mass Storage Systems, March 2000, College Park, Md, USA 203–210.Google Scholar
    20. Rhodes PJ, Ramakrishnan S: Iteration aware prefetching for remote data access. Proceedings of the 1st International Conference on e-Science and Grid Computing, December 2005, Melbourne, Australia 279–286.Google Scholar
    21. Correa WT, Klosowski JT, Silva CT: Visibility-based prefetching for interactive out-of-core rendering. Proceedings of the IEEE Symposium on Parallel and Large-Data Visualization and Graphics (PVG '03), October 2003, Seattle, Wash, USA 1–8.Google Scholar
    22. Rhodes PJ, Tang X, Bergeron RD, Sparr TM: Out-of-core visualization using iterator-aware multidimensional prefetching. Visualization and Data Analysis, January 2005, San Jose, Calif, USA, Proceedings of SPIE 5669: 295–306.Google Scholar
    23. Chisnall D, Chen M, Hansen C: Knowledge-based out-of-core algorithms for data management in visualization. Eurographics/IEEE-VGTC Symposium on Visualization (EuroVis '06), May 2006, Lisbon, PortugalGoogle Scholar
    24. Samet H: The Design of Analysis of Spatial Data Structure. Addison Wesley, Reading, Mass, USA; 1990.Google Scholar
    25. De Floriani L, Magillo P, Puppo E: Multi-resolution mesh representation: models and data structures. In Principle of Multi-Resolution in Geometric Modeling, Lecture Notes in Mathematics. Springler, Berlin, Germany; 2002:363–418.Google Scholar
    26. Pajarola R, Rossignac J: Compressed progressive meshes. IEEE Transactions on Visualization and Computer Graphics 2000,6(1):79–93. 10.1109/2945.841122View ArticleGoogle Scholar
    27. Nielson GM: Tools for triangulations and tetrahedralizations and constructing functions defined over them. In Scientific Visualization: Overviews, Methodologies and Techniques. IEEE Computer Society, Silver Spring, Md, USA; 1997:429–525. chapter 20Google Scholar
    28. Lario R, Pajarola R, Tirado F: Cached geometry manager for view-dependent LOD rendering. Proceedings of the 13th International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG '05), January-February 2005, Plzen-Bory, Czech Republic 9–16.Google Scholar
    29. Hilbert K, Brunnett G: A hybrid LOD based rendering approach for dynamic scenes. Proceedings of Computer Graphics International Conference (CGI '04), June 2004, Crete, Greece 274–277.Google Scholar
    30. Bartz D, Meißner M, Hüttner T: OpenGL-assisted occlusion culling for large polygonal models. Computers & Graphics 1999,23(5):667-679. 10.1016/S0097-8493(99)00090-4View ArticleGoogle Scholar
    31. Greene N, Kass M, Miller G: Hierarchical Z-buffer visibility. Proceedings of the 20th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '93), August 1993, Anaheim, Calif, USA 231–238.View ArticleGoogle Scholar
    32. Zhang H, Manocha D, Hudson T, Hoff KE III: Visibility culling using hierarchical occlusion maps. Proceedings of the 24th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '97), August 1997, Los Angeles, Calif, USA 77–88.View ArticleGoogle Scholar
    33. Correa WT, Klosowski JT, Silva CT: iWalk: interactive out-of-core rendering of large models. In Tech. Rep. TR-653-02. Princeton University, Princeton, NJ, USA; 2002.Google Scholar
    34. Sobierajski L, Schroeder W: Interactive visualization of aircraft and power generation engines. Proceedings of IEEE Conference on Information Visualization, October 1997, Phoenix, Ariz, USA 483–486.Google Scholar
    35. El-Sana J, Chiang Y-J: External memory view-dependent simplication. Computer Graphics Forum 2000,19(3):139-150. 10.1111/1467-8659.00406View ArticleGoogle Scholar
    36. Tamada T, Nakamura Y, Takeda S: An efficient 3D object management and interactive walkthrough for the 3D facility management system. Proceedings of the 20th International Conference on Industrial Electronics, Control and Instrumentation (IECON '94), September 1994, Bologna, Italy 3: 1937–1941.Google Scholar
    37. Teller SJ, Sequin CH: Visibility preprocessing for interactive walkthroughs. Proceedings of the 18th Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '91), July-August 1991, Las Vegas, Nev, USA 61–70.View ArticleGoogle Scholar
    38. Cohen-Or D, Chrysanthou Y, Silva CT: A survey of visibility for walkthrough applications. Proceedings of the 27th Annual Conference on Computer Graphics (SIGGRAPH '00), July 2000, New Orleans, La, USA 61–69. courses notesGoogle Scholar
    39. Airey J, Rohlf J, Brooks FP Jr.: Towards image realism with interactive update rates in complex virtual building environments. Proceedings of Symposium on Interactive 3D Graphics, March 1990, Snowbird, Utah, USA 24(2):41-50.Google Scholar
    40. Ohbuchi E: A real-time refraction renderer for volume objects using a polygon-rendering scheme. Proceedings of Computer Graphics International (CGI '03), July 2003, Tokyo, Japan 190–195.Google Scholar
    41. Luebke DP: A developer's survey of polygonal simplification algorithms. IEEE Computer Graphics and Applications 2001,21(3):24-35.View ArticleGoogle Scholar
    42. Agrawal R, Srikant R: Mining sequential patterns. Proceedings of the 11th International Conference on Data Engineering (ICDE '95), March 1995, Taipei, Taiwan 3–14.View ArticleGoogle Scholar
    43. Srikant R, Agrawal R: Mining quantitative association rules in large relational tables. Proceedings of the ACM SIGMOD International Conference on Management of Data, June 1996, Montreal, Canada 25(2):1-12.View ArticleGoogle Scholar
    44. Zaki M: SPADE: an efficient algorithm for mining frequent sequences. Machine Learning 2001,42(1-2):31-60.View ArticleGoogle Scholar
    45. Hung S-S, Kuo T-C, Liu DS-M: PrefixUnion: mining traversal patterns efficiently in virtual environments. Proceedings of the 5th International Conference International Conference on Computational Science (ICCS '05), May 2005, Atlanta, Ga, USA, Lecture Notes in Computer Science 3516: 830–833.Google Scholar
    46. Choi J, Noh SH, Min SL, Cho Y: Towards application/file-level characterization of block references: a case for fine-grained buffer management. Proceedings of the ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems, June 2000, Santa Clara, Calif, USA 286–295.Google Scholar
    47. MacQueen JB: Some methods for classification and analysis of multivariate observations. Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability, 1967, Berkeley, Calif, USA 1: 281–297.MathSciNetMATHGoogle Scholar
    48. Kaufman L, Rousseuw PJ: Finding Groups in Data. John Wiley & Sons, New York, NY, USA; 1990.View ArticleGoogle Scholar
    49. Wu Z, Leahy R: An optimal graph theoretic approach to data clustering: theory and its application to image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 1993,15(11):1101-1113. 10.1109/34.244673View ArticleGoogle Scholar
    50. Karypis G, Han E, Kumar V: Chameleon: a hierarchical clustering algorithm using dynamic modeling. In Tech. Rep. 432. University of Minnesota, Minneapolis, Minn, USA; 1999.Google Scholar
    51. Goil S, Nagesh H, Choudhary A: MAFIA: efficient and scalable subspace clustering for very large data sets. In Tech. Rep. CPDC-TR-9906-010. Northwestern University, Evanston, Ill, USA; 1999.Google Scholar
    52. Karypis G: CLUTO—a clustering toolkit. In Tech. Rep. 02-017. University of Minnesota, Minneapolis, Minn, USA; 2002.Google Scholar
    53. Jaccard P: The distribution of the flora in the Alpine zone. New Phytologist 1912,11(2):37-50. 10.1111/j.1469-8137.1912.tb05611.xView ArticleGoogle Scholar
    54. Ng C-M, Nguyen C-T, Tran D-N, Tan T-S, Yeow S-W: Analyzing pre-fetching in large-scale visual simulation. Proceedings of Computer Graphic International Conference, June 2005, New York, NY, USA 100–107.Google Scholar
    55. Schindler J, Griffin JL, Lumb CR, Ganger GR: Track-aligned extents: matching access patterns to disk drive characteristics. Proceedings of the 1st USENIX Conference on File and Storage Technologies (FAST '02), January 2002, Monterey, Calif, USA 259–274.Google Scholar
    56. Sivathanu M, Prabhakaran V, Popovici FI, Denehy TE, Arpaci-Dusseau AC, Arpaci-Dusseau RH: Semantically-smart disk systems. Proceedings of the 2nd USENIX Conference on File and Storage Technologies (FAST '03), March-April 2003, San Francisco, Calif, USA 73–88.Google Scholar
    57. Smith AJ: Sequentiality and prefetching in database systems. ACM Transactions on Database Systems 1978,3(3):223-247. 10.1145/320263.320276View ArticleGoogle Scholar
    58. Kim JM, Choi J, Kim J, et al.: A low-overhead, high-performance unified buffer management scheme that exploits sequential and looping references. Proceedings of the 4th Symposium on Operating System Design and Implementation (OSDI '00), October 2000, San Diego, Calif, USA 119–134.Google Scholar
    59. Srikant R, Agrawal R: Mining sequential patterns: generalizations and performance improvements. Proceeding of the 5th International Conference on Extending Database Technology (EDBT '96), March 1996, Avignon, France 3–17.Google Scholar
    60. Joshi A, Krishnapuram R: On mining web access logs. Proceedings of the SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (DMKD '00), May 2000, Dallas, Tex, USA 63–69.Google Scholar
    61. Agrawal R, Imielinski T, Swami AN: Mining association rules between sets of items in large databases. Proceedings of ACM SIGMOD International Conference on Management of Data, May 1993, Washington, DC, USA 207–216.Google Scholar
    62. Agarwal R, Aggarwal CC, Prasad VVV: Depth first generation of long patterns. Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD '00), August 2000, Boston, Mass, USA 108–118.View ArticleGoogle Scholar


    © S.-S. Hung and D. S.-M. Liu. 2007

    This article is published under license to BioMed Central Ltd. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.