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The VLDB Journal

, Volume 18, Issue 3, pp 675–693 | Cite as

Instance optimal query processing in spatial networks

  • Ke DengEmail author
  • Xiaofang Zhou
  • Heng Tao Shen
  • Shazia Sadiq
  • Xue Li
Regular Paper

Abstract

The performance optimization of query processing in spatial networks focuses on minimizing network data accesses and the cost of network distance calculations. This paper proposes algorithms for network k-NN queries, range queries, closest-pair queries and multi-source skyline queries based on a novel processing framework, namely, incremental lower bound constraint. By giving high processing priority to the query associated data points and utilizing the incremental nature of the lower bound, the performance of our algorithms is better optimized in contrast to the corresponding algorithms based on known framework incremental Euclidean restriction and incremental network expansion. More importantly, the proposed algorithms are proven to be instance optimal among classes of algorithms. Through experiments on real road network datasets, the superiority of the proposed algorithms is demonstrated.

Keywords

Spatial networks Spatial queries Instance optimality Incremental lower bound constraint 

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References

  1. 1.
    Borodin A., El-Yaniv E.: Online Computation and Competitve Analysis. Cambridge University Press, New York (2006)Google Scholar
  2. 2.
    Dar S., Ramakrishnan R.: A performance study of transitive closure algorithms. ACM SIGMOD Rec. 23(2), 454–465 (1994)CrossRefGoogle Scholar
  3. 3.
    Deng, K., Zhou, X., Shen, H.: Multi-source skyline query processing in road networks. In: Proceedings of the IEEE 20th International Conference on Data Engineering (ICDE), pp. 796–805 (2007)Google Scholar
  4. 4.
    Dijkstra E.: A note on two problems in connection with graphs. Numerische Mathematik 1, 269–271 (1959)zbMATHCrossRefMathSciNetGoogle Scholar
  5. 5.
    Fagin R., Lotem A., Naor M.: Optimal aggregation algorithms for middleware. J. Comput. Syst. Sci. 66, 614–656 (2003)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Goldman, R., Shivakumar, N.: Proximity search in databases. In: Proceedings of the 24th International Conference Very Large Data Bases (VLDB), pp. 26–37 (1998)Google Scholar
  7. 7.
    Hochbaum, D. (ed.): Approximation algorithms for np-hard problems. PWS, Boston (1997)Google Scholar
  8. 8.
    Hu, H., Lee, D.L., Lee, V.: Distance indexing on road networks. In: Proceedings of the 32nd International Conference Very Large Data Bases (VLDB), pp. 894–905 (2006)Google Scholar
  9. 9.
    Jung S., Pramanik S.: An efficient path computation model for hierarchically structured topographical road maps. TKDE 14(5), 1029–1047 (2002)Google Scholar
  10. 10.
    Kolahdouzan, M., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: Proceedings of the 30nd International Conference Very Large Data Bases (VLDB), pp. 840–851 (2004)Google Scholar
  11. 11.
    Kung, R., Hanson, E., Ioannidis, Y., Sellis, T., Shapiro, L., Stonebraker, M.: Heuristic search in data base system. In: Proceedings of the 1st International Workshop on Expert Database Systems, pp. 537–548 (1986)Google Scholar
  12. 12.
    Papadias, D., Tao, Y., Fu, G., Seeger, B.: An optimal and progressive algorithm for skyline queries. SIGMOD, pp. 467–478 (2003)Google Scholar
  13. 13.
    Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: Proceedings of the 29th International Conference Very Large Data Bases (VLDB), pp. 790–801 (2003)Google Scholar
  14. 14.
    Phillips S., Westbrook J.: On-line Algorithms: Competitive Analysis and Beyond. Algorithms and Theory of Computation Handbook. CRC Press, Boca Raton (1999)Google Scholar
  15. 15.
    Ramakrishnan R., Gehrke J.: Database Management System, 2nd edn. McGraw-Hill, New York (2000)Google Scholar
  16. 16.
    Sharifzadeh, M., Shahabi, C.: The spatial skyline queries. In: Proceedings of the 32nd International Conference Very Large Data Bases (VLDB), pp. 751–762 (2006)Google Scholar
  17. 17.
    Sleator, D., Tarjan, R.: Amortized efficiency of list updata and paging rules. COMM. ACM 28, pp. 202–208 (1985)Google Scholar
  18. 18.
    Yiu M., Mamoulis N., Papadias D.: Aggregate nearest neighbor queries in road networks. TKDE 17(6), 820–833 (2005)Google Scholar

Copyright information

© Springer-Verlag 2008

Authors and Affiliations

  • Ke Deng
    • 1
    Email author
  • Xiaofang Zhou
    • 1
  • Heng Tao Shen
    • 1
  • Shazia Sadiq
    • 1
  • Xue Li
    • 1
  1. 1.School of Information Technology and Electrical EngineeringThe University of QueenslandBrisbaneAustralia

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