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Bayesian nearest Neighbor Search in a Spatial Database

by K. Balasaravanan, K. Duraiswamy
International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 1 - Number 7
Year of Publication: 2012
Authors: K. Balasaravanan, K. Duraiswamy
10.5120/104-0201

K. Balasaravanan, K. Duraiswamy . Bayesian nearest Neighbor Search in a Spatial Database. International Journal of Applied Information Systems. 1, 7 ( March 2012), 7-10. DOI=10.5120/104-0201

@article{ 10.5120/104-0201,
author = { K. Balasaravanan, K. Duraiswamy },
title = { Bayesian nearest Neighbor Search in a Spatial Database },
journal = { International Journal of Applied Information Systems },
issue_date = { March 2012 },
volume = { 1 },
number = { 7 },
month = { March },
year = { 2012 },
issn = { 2249-0868 },
pages = { 7-10 },
numpages = {9},
url = { https://www.ijais.org/archives/volume1/number7/104-0201/ },
doi = { 10.5120/104-0201 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T10:41:44.807880+05:30
%A K. Balasaravanan
%A K. Duraiswamy
%T Bayesian nearest Neighbor Search in a Spatial Database
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 1
%N 7
%P 7-10
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In an uncertain spatial database, identifying nearest neighbor is the important task to perform. To perform the nearest neighbor search (NN), existing work have presented Authenticated Multi-step NN (AMNN) and Superseding Nearest Neighbor (SNN) search. The AMNN efficiently performed the NN search using query authentication and trusted authority centre in which NN search has done only in single server not for distributed server and communication overhead also increased. The main drawback of SNN is that it cannot be applied to high dimensional data structure. To overcome all these issues, in this paper we implements a new technique named BNN (Bayesian Nearest Neighbor) for NN search and similarity search in a spatial database. BNN performs NN search efficiently and retrieve the distance information not only from single server but also from distributed servers. It can be applied to high dimensional data structure and it automatically reduces the communication overhead. The query result returned by BNN will be a reliable one. The experimental evaluation shows that BNN performs Nearest neighbor search and similarity search well than existing AMNN and SNN.

References
  1. Sze Man Yuen, Yufei Tao, Xiaokui Xiao, Jian Pei, and Donghui Zhang, “ Superseding Nearest Neighbor Search on Uncertain Spatial Databases” IEEE Transaction on Knowledge and Data Engineering VOL. 22, NO. 7, July 2010.
  2. Stavros Papadopoulos, Lixing Wang, Yin Yang, Dimitris Papadias, and Panagiotis Karras, “Authenticated Multistep Nearest Neighbor Search”, IEEE Transactions on Knowledge and Data Engineering, vol. 23, no. 5, May 2011.
  3. H.-P. Kriegel, P. Kunath, and M. Renz, “Probabilistic Nearest- Neighbor Query on Uncertain Objects,” Proc. Database Systems for Advanced Applications (DASFAA), pp. 337-348, 2007.
  4. Kyriakos Mouratidis and Man Lung Yiu “ Anonymous Query Processing “ieee transactions on knowledge and data engineering, vol. 22, no. 1, january 2010.
  5. H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, “Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures,” Proc. Int’l Conf. Very Large Data Base Endowment (VLDB ’08), vol. 1, pp. 1542-1552, 2008.
  6. A. Kundu and E. Bertino, “Structural Signatures for Tree Data Structures,” Proc. Int’l Conf. Very Large Data Base Endowment (VLDB ’08), 2008.
  7. H. Pang and K. Mouratidis, “Authenticating the Query Results of Text Search Engines,” Proc. Int’l Conf. Very Large Data Base Endowment (VLDB ’08), 2008.
  8. Y. Tao, K. Yi, C. Sheng, and P. Kalnis, “Quality and Efficiency in High Dimensional Nearest Neighbor Search,” Proc. ACM SIGMOD, 2009.
  9. Y. Yang, S. Papadopoulos, D. Papadias, and G. Kollios, “Spatial Outsourcing for Location-Based Services,” Proc. Int’l Conf. Data Eng. (ICDE ’08), 2008.
  10. K. Yi, F. Li, M. Hadjieleftheriou, G. Kollios, and D. Srivastava, “Randomized Synopses for Query Assurance on Data Streams,” Proc. Int’l Conf. Data Eng. (ICDE ’08), 2008.
  11. G. Beskales, M.A. Soliman, and I.F. Ilyas, “Efficient Search for the Top-k Probable Nearest Neighbors in Uncertain Databases,” Proc. Very Large Data Bases (VLDB), vol. 1, no. 1, pp. 326-339, 2008.
  12. R. Cheng, J. Chen, M.F. Mokbel, and C.-Y. Chow, “Probabilistic Verifiers: Evaluating Constrained Nearest-Neighbor Queries over Uncertain Data,” Proc. Int’l Conf. Data Eng. (ICDE), pp. 973-982, 2008.
  13. M. Hua, J. Pei, W. Zhang, and X. Lin, “Ranking Queries on Uncertain Data: A Probabilistic Threshold Approach,” Proc. ACM SIGMOD, pp. 673-686, 2008.
Index Terms

Computer Science
Information Sciences

Keywords

Keywords: Query Authentication BNN (Bayesian Nearest Neighbor) Distributed server