Single-Source Multi-Target A* Algorithm for POI Queries on Road Network

  • Htoo Htoo
  • Yutaka Ohsawa
  • Noboru Sonehara
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7142)

Abstract

Searching for the shortest paths from a starting point to several target points on a road network is an essential operation for several kinds of queries in location based services. This search can be easily done using Dijkstra’s algorithm. Although an A* algorithm is faster for finding the shortest path between two points, it is not so quick when several target points are given, because it must iterate pairwise searches. As the number of target points increases, the number of duplicated calculations for road network nodes also increases. This duplication degrades efficiency. A single-source multi-target A* (SSMTA*) algorithm is proposed to cope with this problem. It requires only one calculation per node and considerably outperforms Dijkstra’s algorithm, especially when the target points are distributed with bias. An application with this algorithm for aggregate nearest neighbor search demonstrated its efficiency, especially when the number of target points is large.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Dijkstra, E.W.: A note on two problems in connection with graphs. Numeriche Mathematik 1, 269–271 (1959)MathSciNetMATHCrossRefGoogle Scholar
  2. 2.
    Hart, P.E., Nilsson, N.J., Raphael, B.: A formal basis for the heuristic determination of minimum cost paths. IEEE Transactions of Systems Science and Cybernetics SSC-4(2), 100–107 (1968)CrossRefGoogle Scholar
  3. 3.
    Yiu, M.L., Mamoulis, N., Papadias, D.: Aggregate nearest neighbor queries in road networks. IEEE Transactions on Knowledge and Data Engineeing 17(6), 820–833 (2005)CrossRefGoogle Scholar
  4. 4.
    Deng, K., Zhou, X., Shen, H.T.: Multi-source skyline query processing in road networks. In: Proceeding of IEEE 23rd International Conference on Data Engineering (2007)Google Scholar
  5. 5.
    Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: Proc. 29th VLDB, pp. 790–801 (2003)Google Scholar
  6. 6.
    Sharifzadeh, M., Kalahdouzan, M.R., Shahabi, C.: The optimal sequenced route query. Technical report, Computer Science Department, University of Southern Calfornia (2005)Google Scholar
  7. 7.
    Li, F., Cheng, D., Hadjieleftheriou, M., Kollios, G., Teng, S.H.: On Trip Planning Queries in Spatial Databases. In: Anshelevich, E., Egenhofer, M.J., Hwang, J. (eds.) SSTD 2005. LNCS, vol. 3633, pp. 273–290. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  8. 8.
    Hu, H., Lee, D.L., Lee, V.C.: Distance indexing on road networks. In: Poc. 32nd VLDB, pp. 894–905 (2006)Google Scholar
  9. 9.
    Samet, H., Sankaranarayanan, J., Alborzi, H.: Scalable network distance browsing in spatial databases. In: Proc. of the ACM SIGMOD Conference, pp. 43–54 (2008)Google Scholar
  10. 10.
    Kolahdouzan, M., Shahabi, C.: Voronoi-based K nearest neighbor search for spatial network databases. In: Proc. 30th VLDB, pp. 840–851 (2004)Google Scholar
  11. 11.
    Zhu, L., Sun, Y.J.W., Mao, D., Liu, P.: Voronoi-based aggregate nearest neighbor query processing in road networks. In: ACM GIS 2010 (2010)Google Scholar
  12. 12.
    Shaw, K., Ioup, E., Sample, J., Abdelguerfi, M., Tabone, O.: Efficient approximation of spatial network queries using the M-tree wirh road network embedding. In: 19th International Conference on Scientific and Statistical Database Management (2007)Google Scholar
  13. 13.
    Ciaccia, P., Patella, M., Zezula, P.: M-tree: An efficient access method for similarity search in metric spaces. In: Proceedings of the 23rd VLDB Conference, pp. 426–435 (1997)Google Scholar
  14. 14.
    Ioup, E., Shaw, K., Sample, J., Abdelguerfi, M.: Efficient AKNN spatial network queries using the M-tree. In: ACM GIS 2007 (2007)Google Scholar
  15. 15.
    Papadias, D., Tao, Y., Mouratidis, K., Hui, C.K.: Aggregate nearest neighbor queries in spatial databases. ACM Transactions on Database Systems 30(2), 529–576 (2005)CrossRefGoogle Scholar
  16. 16.
    Papadias, D., Shen, Q., Tao, Y., Mouratidis, K.: Group nearest neighbor queries. In: Proceedings of the 20th International Conference on Data Engineering, pp. 301–312 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Htoo Htoo
    • 1
  • Yutaka Ohsawa
    • 1
  • Noboru Sonehara
    • 2
  1. 1.Graduate School of Science and EngineeringSaitama UniversityJapan
  2. 2.National Institute of InformaticsJapan

Personalised recommendations