Dynamic Data Distribution of High Level Architecture Based on Publication and Subscription Tree (original) (raw)

Abstract

To ensure the efficiency of data exchange between simulation members via multicast groups in the simulation system based on High Level Architecture (HLA), this paper proposes a novel method of dynamic data distribution based on publication and subscription tree (PS-Tree). The main contributions of this paper include: (1) Proposing the structure of PS-Tree which can manifest the relationship of data exchange between simulation members. (2) Describing the method of dynamic data distribution based on PS-Tree by mining association rule and (3) Analyzing the performance. Experiment shows that this dynamic data distribution method can implement data distribution efficiently and effectively.

Supported by Grant of National Science Foundation of China (60473071), Specialized Research Fund for Doctoral Program by the Ministry of Education (SRFDP 20020610007) and the Software Innovation Project of Sichuan Youth (AA0807).

Preview

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Han, J., Kambr, M.: Data Mining-Concepts and Techniques, pp. 225–245. Higher Education Press, Beijing (2001)
    Google Scholar
  2. Department of Defense High Level Architecture Interface Specification, Version 1.3. DMSO (April 1998), available at: http://hla.dmso.mil
  3. High Level Architecture Run-Time Infrastructure Programmer’s Guide. DMSO (1998), available at: http://hla.dmso.mil
  4. Morse, K.L., Steinman, J.S.: Data Distribution Management in the HLA: Multidimensional Regions and Physically Correct Filtering. In: Proc. the 1997 Spring Simulation Interoperability Workshop, Orlando, FL, March 1998, pp. 343–352 (Spring 1998)
    Google Scholar
  5. Rak, S.J., Van Hook, D.J.: Evaluation of Grid-Based Relevance Filtering for Multicast Group Assignment. In: Proc. 14th Workshop on Standards for the interoperability of Distributed Simulations, Orlando, FL, September, pp. 739–747 (March 1996)
    Google Scholar
  6. Morse, K.L., Bic, L., Tsai, K.: Multicast grouping for dynamic data distribution management. In: Proc. 31st Society for Computer Simulation Conference, SCSC 1999 (1999)
    Google Scholar
  7. Chunlei, X., Liang, Z., Sikun, L.: An Efficient Multi-level Grids Based RelevanceFiltering Method. Proc. Journal of National University of Defense Technology 24(4) (January 2002)
    Google Scholar
  8. Yachong, Z., Guoji, S., Hairong, Y.: New Algorithm of Data Distribution Management for Distributed Interactive Simulation. Proc. Journal of System Simulation 7(1) (January 2005)
    Google Scholar

Download references

Author information

Authors and Affiliations

  1. School of Computer Science, Sichuan University, Chengdu, 610065, China
    Yintian Liu, Changjie Tang, Chuan Li, Minfang Zhu & Tao Zeng
  2. Nanjing Army Command College, Nanjing, 210045, China
    Yintian Liu
  3. Dept. of Computer Sci. & Tech., Shaanxi Univ. of Tech., Hanzhong, 723003, China
    Minfang Zhu

Authors

  1. Yintian Liu
  2. Changjie Tang
  3. Chuan Li
  4. Minfang Zhu
  5. Tao Zeng

Editor information

Editors and Affiliations

  1. Chinese University of Hong Kong, Hong Kong, China
    Jeffrey Xu Yu
  2. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, 153-8505, Tokyo, Japan
    Masaru Kitsuregawa
  3. Department of Computing, Hong Kong Polytechnic University, Hong Kong
    Hong Va Leong

Rights and permissions

© 2006 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Liu, Y., Tang, C., Li, C., Zhu, M., Zeng, T. (2006). Dynamic Data Distribution of High Level Architecture Based on Publication and Subscription Tree. In: Yu, J.X., Kitsuregawa, M., Leong, H.V. (eds) Advances in Web-Age Information Management. WAIM 2006. Lecture Notes in Computer Science, vol 4016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11775300\_45

Download citation

Keywords

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Publish with us