Skip to main content


Efficient Reduction of Access Latency through Object Correlations in Virtual Environments

  • 699 Accesses

  • 1 Citations


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.


  1. 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.

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.2003.1260746

  9. 9.

    Hoppe H: Progressive meshes. Proceedings of the 23rd Annual Conference on Computer Graphics (SIGGRAPH '96), August 1996, New Orleans, La, USA 99–108.

  10. 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.x

  11. 11.

    Yoon S-E, Lindstrom P, Pascucci V, Manocha D: Cache-oblivious mesh layouts. ACM Transactions on Graphics 2005,24(3):886-893. 10.1145/1073204.1073278

  12. 12.

    Yoon S-E, Lindstrom P: Mesh layouts for block-based caches. IEEE Transactions on Visualization and Computer Graphics 2006,12(5):1213-1220.

  13. 13.

    Chakrabarti S: Mining the Web: Discovering Knowledge from Hypertext Data. Morgan Kaufmann, San Francisco, Calif, USA; 2003.

  14. 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. 10.1109/69.683753

  15. 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.

  16. 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.

  17. 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. 10.1145/380995.381033

  18. 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.

  19. 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.

  20. 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.

  21. 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.

  22. 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.

  23. 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, Portugal

  24. 24.

    Samet H: The Design of Analysis of Spatial Data Structure. Addison Wesley, Reading, Mass, USA; 1990.

  25. 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.

  26. 26.

    Pajarola R, Rossignac J: Compressed progressive meshes. IEEE Transactions on Visualization and Computer Graphics 2000,6(1):79–93. 10.1109/2945.841122

  27. 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 20

  28. 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.

  29. 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.

  30. 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)00090-4

  31. 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.

  32. 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.

  33. 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.

  34. 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.

  35. 35.

    El-Sana J, Chiang Y-J: External memory view-dependent simplication. Computer Graphics Forum 2000,19(3):139-150. 10.1111/1467-8659.00406

  36. 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.

  37. 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.

  38. 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 notes

  39. 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.

  40. 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.

  41. 41.

    Luebke DP: A developer's survey of polygonal simplification algorithms. IEEE Computer Graphics and Applications 2001,21(3):24-35.

  42. 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.

  43. 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.

  44. 44.

    Zaki M: SPADE: an efficient algorithm for mining frequent sequences. Machine Learning 2001,42(1-2):31-60.

  45. 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.

  46. 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.

  47. 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.

  48. 48.

    Kaufman L, Rousseuw PJ: Finding Groups in Data. John Wiley & Sons, New York, NY, USA; 1990.

  49. 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. 10.1109/34.244673

  50. 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.

  51. 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.

  52. 52.

    Karypis G: CLUTO—a clustering toolkit. In Tech. Rep. 02-017. University of Minnesota, Minneapolis, Minn, USA; 2002.

  53. 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.x

  54. 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.

  55. 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.

  56. 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.

  57. 57.

    Smith AJ: Sequentiality and prefetching in database systems. ACM Transactions on Database Systems 1978,3(3):223-247. 10.1145/320263.320276

  58. 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.

  59. 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.

  60. 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.

  61. 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.

  62. 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.

Download references

Author information

Correspondence to Shao-Shin Hung.

Rights and permissions

Open Access This article is distributed under the terms of the Creative Commons Attribution 2.0 International License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Reprints and Permissions

About this article

Cite this article

Hung, S., Liu, D.S. Efficient Reduction of Access Latency through Object Correlations in Virtual Environments. EURASIP J. Adv. Signal Process. 2007, 010289 (2007) doi:10.1155/2007/10289

Download citation


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