Approximately Searching Aggregate k-Nearest Neighbors on Remote Spatial Databases Using Representative Query Points

  • Hideki Sato
Part of the Studies in Computational Intelligence book series (SCI, volume 376)

Abstract

Aggregate k-Nearest Neighbor (k-ANN) queries are required to develop a new promising Location-Based Service (LBS) which supports a group of mobile users in spatial decision making. As a procedure for computing exact results of k-ANN queries over some Web services has to access remote spatial databases through simple and restrictive Web API interfaces, it suffers from a large amount of communication. To overcome the problem, this paper presents another procedure for computing approximate results of k-ANN queries. It relies on a Representative Query Point (RQP) to be used as a key of a k-Nearest Neighbor (k-NN) query for searching spatial data. According to the experiments using synthetic and real data (objects), Precision of sum k-NN query results using a minimal point as RQP is less than 0.9 in the most case that the number of query points is 10, and over 0.9 in the other most cases. On the other hand, Precision of max k-NN query results using a minimal point as RQP ranges 0.47 to 0.93 according to the experiments using synthetic data (objects). The experiments using real data (objects) show that Precision of max k-NN query results is less than 0.8 in case that k is 10, and over 0.8 in the other cases. From these results, it is concluded that accuracy of sum k-NN query results is allowable and accuracy of max k-NN query results is partially allowable.

Keywords

Data Object Mobile User Minimal Point Near Neighbor Range Query 
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.

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References

  1. 1.
    Roussopoulos, N., Kelly, S., Vincent, F.: Nearest Neighbor Queries. In: Proc. ACM SIGMOD Int’l Conf. on Management of Data, pp. 71–79 (1995)Google Scholar
  2. 2.
    Hjaltason, G.R., Samet, H.: Distance Browsing in Spatial Databases. ACM Trans. Database Systems 24(2), 265–318 (1999)CrossRefGoogle Scholar
  3. 3.
    Guttman, A.: R-trees: A Dynamic Index Structure for Spatial Searching. In: Proc. ACM SIGMOD Int’l Conf. on Management of Data, pp. 47–57 (1984)Google Scholar
  4. 4.
    Fagin, R., Lotem, A., Naor, M.: Optimal Aggregation Algorithms for Middleware. In: Proc. Symp. Principles of Database Systems, pp. 102–113 (2001)Google Scholar
  5. 5.
    Ilyas, H.F., Beskales, G., Soliman, M.A.: A Survey of Top-k Query Processing Techniques in Relational Database Systems. ACM Computing Survey 40(4), Article 11 (2008)Google Scholar
  6. 6.
    Nelder, J.A., Mead, R.: A Simplex Method for Function Minimization. Computational Journal, 308–313 (1965)Google Scholar
  7. 7.
    Berg, M.D., Kreveld, M.V., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications. Springer, Heidelberg (1997)MATHGoogle Scholar
  8. 8.
    Ilarri, S., Menna, E., Illarramendi, A.: Location-Dependent Query Processing: Where We Are and Where We Are Heading. ACM Computing Survey 42(3), Article 12 (2010)Google Scholar
  9. 9.
    Korn, F., Muthukrishnan, S.: Influence Sets Based on Reverse Nearest Neighbor Queries. In: Proc. ACM SIGMOD Int’l Conf. on Management of Data, pp. 201–212 (2000)Google Scholar
  10. 10.
    Ferhatosmanoglu, H., Stanoi, I., Agrawal, D., Abbadi, A.E.: Constrained Nearest Neighbor Queries. In: Proc. Seventh Int’l Symp. Advances in Spatial and Temporal Databases, pp. 257–278 (2001)Google Scholar
  11. 11.
    Papadias, D., Shen, Q., Tao, Y., Mouratidis, K.: Group Nearest Neighbor Queries. In: Proc. Int’l Conf. Data Eng., pp. 301–312 (2004)Google Scholar
  12. 12.
    Yiu, M.L., Mamoulis, M., Papadias, D.: Aggregate Nearest Neighbor Queries in Road Networks. IEEE Trans. on Knowledge and Data Engineering 17(6), 820–833 (2005)CrossRefGoogle Scholar
  13. 13.
    Nutanong, S., Tanin, E., Zhang, R.: Visible nearest neighbor queries. In: Proc. Int’l Conf. DASFAA, pp. 876–883 (2007)Google Scholar
  14. 14.
    Liu, D., Lim, E., Ng, W.: Efficient k-Nearest Neighbor Queries on Remote Spatial Databases Using Range Estimation. In: Proc. SSDBM, pp. 121–130 (2002)Google Scholar
  15. 15.
    Bae, W.D., Alkobaisi, S., Kim, S.H., Narayanappa, S., Shahabi, C.: Supporting Range Queries on Web Data Using k-Nearest Neighbor Search. In: Proc. W2GIS, pp. 61–75 (2007)Google Scholar
  16. 16.
    Xu, B., Wolfson, O.: Time-Series Prediction with Applications to Traffic and Moving Objetcs Databases. In: Proc. Third ACM Int’l Workshop on MobiDE, pp. 56–60 (2003)Google Scholar
  17. 17.
    Trajcevski, G., Wolfson, O., Xu, B., Nelson, P.: Managing Uncertainty in Moving Objects Databases. ACM Trans. Database Systems 29(3), 463–507 (2004)CrossRefGoogle Scholar
  18. 18.
    Yu, P.S., Chen, S.K., Wu, K.L.: Incremental Processing of Continual Range Queries over Moving Objects. IEEE Trans. Knowl. Data Eng. 18(11), 1560–1575 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Hideki Sato
    • 1
  1. 1.School of InformaticsDaido UniversityNagoyaJapan

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