Efficient k-Nearest Neighbor Searches for Parallel Multidimensional Index Structures

  • Kyoung Soo Bok
  • Seok Il Song
  • Jae Soo Yoo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3882)


In this paper, we propose a parallel multidimensional index structure and range search and k-NN search methods for the index structures. The proposed index structure is nP(processor)-n×mD(disk) architecture which is the hybrid type of nP-nD and 1P-nD. Its node structure increases fan-out and reduces the height of an index tree. Also, the proposed range search methods are designed to maximize I/O parallelism of the index structure. Finally, we present a new method to transform k-NN queries to range search queries. Through various experiments, it is shown that the proposed method outperforms other parallel index structures.


Leaf Node Child Node Index Structure Range Query Range Search 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Kamel, Faloutsos, C.: Parallel R-trees. In: Proc. ACM SIGMOD, pp. 195–204 (1992)Google Scholar
  2. 2.
    Bang, K.S., Lu, H.: The PML-tree: An Efficient Parallel Spatial Index Structure for Spatial Databases. In: Proc. ACM Annual Computer Science Conference, pp. 79–88 (1996)Google Scholar
  3. 3.
    Koudas, N., Faloutsos, C., Kamel, I.: Declustering Spatial Databases on a Multi-Computer Architecture. In: Apers, P.M.G., Bouzeghoub, M., Gardarin, G. (eds.) EDBT 1996. LNCS, vol. 1057, pp. 592–614. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  4. 4.
    Ali, M.H., Saad, A.A., Ismail, M.A.: The PN-Tree: A parallel and distributed multi-dimensional index. Distributed and Parallel Databases 17(2), 111–133 (2005)CrossRefGoogle Scholar
  5. 5.
    Gaede, V., Gunther, O.: Multidimensional access methods. ACM Computing Surveys 30(2), 170–231 (1998)CrossRefGoogle Scholar
  6. 6.
    Scnnitzer, B., Leutenegger, S.T.: Master-Client R-trees: A New Parallel R-tree Architecture. In: Proc. SSDBM, pp. 68–77 (1999)Google Scholar
  7. 7.
    Wang, B., Horinokuchi, H., Kaneko, K., Makinouchi, A.: Parallel R-tree Search Algo-rithm on DSVM. In: Proc. DASFAA, pp. 237–245 (1999)Google Scholar
  8. 8.
    An, J., Chen, Y.P., Xu, Q., Zhou, X.: A New Indexing Method for High Dimensional Dataset. In: Zhou, L.-z., Ooi, B.-C., Meng, X. (eds.) DASFAA 2005. LNCS, vol. 3453, pp. 385–397. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  9. 9.
    Zhou, X., Wang, G., Yu, J.X., Yu, G.: M+-tree: A New Dynamical Multidimensional Index for Metric Spaces. In: Proc. ADC 2003, pp. 161–168 (2003)Google Scholar
  10. 10.
    Taniar, D., Rahayu, J.W.: Global parallel index for multi-processors database systems. Information Science 165(1-2), 103–127 (2004)CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Kyoung Soo Bok
    • 1
  • Seok Il Song
    • 2
  • Jae Soo Yoo
    • 3
  1. 1.Department of Computer ScienceKorea Advanced Institute of Science and TechnologyKorea
  2. 2.Department of Computer EngineeringChungju National UniversityKorea
  3. 3.Department of Computer and Communication EngineeringChungbuk National UniversityKorea

Personalised recommendations