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Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries

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Abstract

The moving k nearest neighbor (MkNN) query continuously finds the k nearest neighbors of a moving query point. MkNN queries can be efficiently processed through the use of safe regions. In general, a safe region is a region within which the query point can move without changing the query answer. This paper presents an incremental safe-region-based technique for answering MkNN queries, called the V*-Diagram, as well as analysis and evaluation of its associated algorithm, V*-kNN. Traditional safe-region approaches compute a safe region based on the data objects but independent of the query location. Our approach exploits the knowledge of the query location and the boundary of the search space in addition to the data objects. As a result, V*-kNN has much smaller I/O and computation costs than existing methods. We further provide cost models to estimate the number of data accesses for V*-kNN and a competitive technique, RIS-kNN. The V*-Diagram and V*-kNN are also applicable to the domain of spatial networks and we present algorithms to construct a spatial-network V*-Diagram. Our experimental results show that V*-kNN significantly outperforms the competitive technique. The results also verify the accuracy of the cost models.

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References

  1. Beckmann, N., Kriegel, H., Schneider, R., Seeger, B.: The R*-tree: an efficient and robust access method for points and rectangles. In: SIGMOD, pp. 322–331 (1990)

  2. Benetis R., Jensen C.S., Karciauskas G., Saltenis S.: Nearest and reverse nearest neighbor queries for moving objects. VLDB J. 15(3), 229–249 (2006)

    Article  Google Scholar 

  3. Böhm C.: A cost model for query processing in high dimensional data spaces. ACM Trans. Database Syst. 25(2), 129–178 (2000)

    Article  Google Scholar 

  4. Chávez E., Navarro G., Baeza-Yates R.A., Marroquín J.L.: Searching in metric spaces. ACM Comput. Surv. 33(3), 273–321 (2001)

    Article  Google Scholar 

  5. Cho, H.J., Chung, C.W.: An efficient and scalable approach to CNN queries in a road network. In: VLDB, pp. 865–876 (2005)

  6. Dijkstra E.W.: A note on two problems in connection with graphs. Numer. Math. 1, 269–271 (1959)

    Article  MATH  MathSciNet  Google Scholar 

  7. Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: SIGMOD, pp. 47–57 (1984)

  8. Hennessy, J.L., Patterson, D.A.: Computer Architecture: A Quantitative Approach. Morgan Kaufmann, San Francisco (2006)

  9. Hjaltason, G.R., Samet, H.: Ranking in spatial databases. In: Symposium on Large Spatial Databases, pp. 83–95 (1995)

  10. Hjaltason G.R., Samet H.: Distance browsing in spatial databases. ACM Trans. Database Syst. 24(2), 265–318 (1999)

    Article  Google Scholar 

  11. Hu, H., Xu, J., Lee, D.L.: A generic framework for monitoring continuous spatial queries over moving objects. In: SIGMOD, pp. 479–490 (2005)

  12. Huang, X., Jensen, C.S., Lu, H., Saltenis, S.: S-GRID: A versatile approach to efficient query processing in spatial networks. In: SSTD, pp. 93–111 (2007)

  13. Huang, X., Jensen, C.S., Saltenis, S.: The islands approach to nearest neighbor querying in spatial networks. In: SSTD, pp. 73–90 (2005)

  14. Iwerks G.S., Samet H., Smith K.P.: Maintenance of k-nn and spatial join queries on continuously moving points. ACM Trans. Database Syst. 31(2), 485–536 (2006)

    Article  Google Scholar 

  15. Kolahdouzan, M.R., Shahabi, C.: Voronoi-based k nearest neighbor search for spatial network databases. In: VLDB, pp. 840–851 (2004)

  16. Kolahdouzan M.R., Shahabi C.: Alternative solutions for continuous k nearest neighbor queries in spatial network databases. GeoInformatica 9(4), 321–341 (2005)

    Article  Google Scholar 

  17. Kulik, L., Tanin, E.: Incremental rank updates for moving query points. In: GIScience, pp. 251–268 (2006)

  18. Mouratidis K., Papadias D., Bakiras S., Tao Y.: A threshold-based algorithm for continuous monitoring of k nearest neighbors. IEEE Trans. Knowl. Data Eng. 17(11), 1451–1464 (2005)

    Article  Google Scholar 

  19. Nutanong, S., Zhang, R., Tanin, E., Kulik, L.: The V*-Diagram: A query dependent approach to moving kNN queries. In: VLDB, pp. 1095–1106 (2008)

  20. Okabe A., Boots B., Sugihara K.: Spatial tessellations: concepts and applications of Voronoi diagrams. John Wiley & Sons, Inc., New York (1992)

    MATH  Google Scholar 

  21. Papadias, D., Zhang, J., Mamoulis, N., Tao, Y.: Query processing in spatial network databases. In: VLDB, pp. 802–813 (2003)

  22. Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: SIGMOD, pp. 71–79 (1995)

  23. Samet H.: Foundations of multidimensional and metric data structures. Morgan Kaufmann, San Francisco (2006)

    MATH  Google Scholar 

  24. Samet, H., Sankaranarayanan, J., Alborzi, H.: Scalable network distance browsing in spatial databases. In: SIGMOD, pp. 43–55 (2008)

  25. Song, Z., Roussopoulos, N.: K-nearest neighbor search for moving query point. In: SSTD, pp. 79–96 (2001)

  26. Splitzer F.: Principles of Random Walk. Springer, Berlin (2001)

    Google Scholar 

  27. Tao, Y., Papadias, D.: Time-parameterized queries in spatio- temporal databases. In: SIGMOD, pp. 334–345 (2002)

  28. Tao Y., Papadias D.: Spatial queries in dynamic environments. ACM Trans. Database Syst. 28(2), 101–139 (2003)

    Article  Google Scholar 

  29. Tao, Y., Papadias, D., Shen, Q.: Continuous nearest neighbor search. In: VLDB, pp. 287–298 (2002)

  30. Tao Y., Zhang J., Papadias D., Mamoulis N.: An efficient cost model for optimization of nearest neighbor search in low and medium dimensional spaces. IEEE Trans. Knowl. Data Eng. 16(10), 1169–1184 (2004)

    Article  Google Scholar 

  31. U.S. Census Bureau: Economic census (retail trade United States) (2002)

  32. Yu, X., Pu, K.Q., Koudas, N.: Monitoring k-nearest neighbor queries over moving objects. In: ICDE, pp. 631–642 (2005)

  33. Zhang, J., Zhu, M., Papadias, D., Tao, Y., Lee, D.L.: Location-based spatial queries. In: SIGMOD, pp. 443–454 (2003)

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Authors and Affiliations

  1. Department of Computer Science and Software Engineering, University of Melbourne, Melbourne, VIC, Australia

    Sarana Nutanong, Rui Zhang, Egemen Tanin & Lars Kulik

  2. NICTA Victoria, Melbourne, VIC, Australia

    Sarana Nutanong, Egemen Tanin & Lars Kulik

Authors
  1. Sarana Nutanong
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  2. Rui Zhang
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  3. Egemen Tanin
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  4. Lars Kulik
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Correspondence to Egemen Tanin.

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Nutanong, S., Zhang, R., Tanin, E. et al. Analysis and evaluation of V*-kNN: an efficient algorithm for moving kNN queries. The VLDB Journal 19, 307–332 (2010). https://doi.org/10.1007/s00778-009-0163-0

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  • DOI: https://doi.org/10.1007/s00778-009-0163-0

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