Analyze the Wild Birds’ Migration Tracks by MPI-Based Parallel Clustering Algorithm

  • HaiMing Zhang
  • YuanChun Zhou
  • JianHui Li
  • XueZhi Wang
  • BaoPing Yan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6440)

Abstract

Aiming at the avian influenza outbreak in Qinghai Lake area, the satellite tracking of migratory birds in Qinghai Lake is studied to analyze the relationship between bird migration, virus spread and ecological environment. These biological problems have been converted into computational studies in previous studies in which spatial clustering is the key factor. A bird migration data analysis system based on DBSCAN algorithm was designed in previous work, by which data can be systematically analyzed, and knowledge patterns are subsequently available for deep biological studies. As the GPS (Global Positioning System) raw data grows rapidly which is large scale with high complexity, DBSCAN takes long time (several minutes) to get the result. In this paper, parallel STING (statistical information grid) algorithm is designed and implemented based on MPI (message passing interface) for spatial clustering. By using parallel STING algorithm, it only takes several seconds to get the result.

Keywords

Clustering Parallel MPI STING Bird Migration Scientific data Tracks Qinghai Lake 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Liu, J., et al.: Highly pathogenic H5N1 influenza virus infection in migratory birds. Science 309, 1206 (2005)CrossRefGoogle Scholar
  2. 2.
    Tang, M., Zhou, Y., Cui, P., Wang, W., Li, J., Zhang, H., Hou, Y., Yan, B.: Discovery of Migration Habitats and Routes of Wild Bird Species by Clustering and Association Analysis. In: Huang, R., Yang, Q., Pei, J., Gama, J., Meng, X., Li, X. (eds.) ADMA 2009. LNCS, vol. 5678, pp. 288–301. Springer, Heidelberg (2009)Google Scholar
  3. 3.
    Wang, W., Yang, J., Muntz, R.: STING: A Statistical Information Grid Approach to Spatial Data Mining. In: Proceedings of the 23rd International Conference on Very Large Data Bases, pp. 186–195 (1997)Google Scholar
  4. 4.
    Yutaka, K., et al.: Discovery of breeding grounds of a Siberian Crane Grus leucogeranus flock that winter sin Iran, via satellite telemetry. In: Bird Conservation International, pp. 327–333 (2002)Google Scholar
  5. 5.
    Mathevet, R., Tamisier, A.: Creation of a nature reserve, its effects on hunting management and waterfowl distribution in the Camargue (southern France). In: Biodiversity and Conservation, pp. 509–519 (2002)Google Scholar
  6. 6.
    Shimazaki, H., et al.: Migration routes and important stopover sites of endangered oriental white storks (Ciconia boyciana) as revealed by satellite tracking. Mem. Natl Inst. Polar Res., Spec. Issue 58, 162–178 (2004)Google Scholar
  7. 7.
    Ball, G.H., Hall, D.J.: ISODATA: a novel method of data analysis and pattern classification. Technical Report of Stanford Research Institute, Menlo Park, CA, Stanford Research Institute, p. 66 (1965)Google Scholar
  8. 8.
    Wang, W., Yang, J., Muntz, R.: STING+: An Approach to Active Spatial Data Mining. In: Proceedings of the International Conference on Data Engineering (ICDE 1999), Sydney, pp. 116–125 (1999)Google Scholar
  9. 9.
    Berkhin, P.: A survey of clustering data mining techniques. In: Jacob, K., Charles, N., Marc, T. (eds.) Grouping Multidimensional Data: Recent Advances in Clustering, pp. 25–71. Springer, Heidelberg (2006b)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • HaiMing Zhang
    • 1
  • YuanChun Zhou
    • 1
  • JianHui Li
    • 1
  • XueZhi Wang
    • 1
  • BaoPing Yan
    • 1
  1. 1.Computer Network Information CenterChinese Academy of SciencesBeijingChina

Personalised recommendations