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Range Nearest-Neighbor Query
January 2006 (vol. 18 no. 1)
pp. 78-91
A range nearest-neighbor (RNN) query retrieves the nearest neighbor (NN) for every point in a range. It is a natural generalization of point and continuous nearest-neighbor queries and has many applications. In this paper, we consider the ranges as (hyper)rectangles and propose efficient in-memory processing and secondary memory pruning techniques for RNN queries in both 2D and high-dimensional spaces. These techniques are generalized for kRNN queries, which return the k nearest neighbors for every point in the range. In addition, we devise an auxiliary solution-based index EXO-tree to speed up any type of NN query. EXO-tree is orthogonal to any existing NN processing algorithm and, thus, can be transparently integrated. An extensive empirical study was conducted to evaluate the CPU and I/O performance of these techniques, and the study showed that they are efficient and robust under various data sets, query ranges, numbers of nearest neighbors, dimensions, and cache sizes.

[1] F. Korn, N. Sidiropoulos, C. Faloutsos, E. Siegel, and Z. Protopapas, “Fast Nearest Neighbor Search in Medical Image Databases,” Proc. 22nd Int'l Conf. Very Large Data Bases, pp. 215-226, 1996.
[2] T. Seidl and H.-P. Kriegel, “Optimal Multi-Step K-Nearest Neighbor Search,” Proc. 1998 ACM SIGMOD Int'l Conf. Management of Data, pp. 154-165, 1998.
[3] R. Weber, H.-J. Schek, and S. Blott, “A Quantitative Analysis and Performance Study for Similarity-Search Methods in High-Dimensional Spaces,” Proc. 24th Int'l Conf. Very Large Data Bases, pp. 194-205, 1998.
[4] B. Cui, B.C. Ooi, J. Su, and K.-L. Tan, “Contorting High Dimensional Data for Efficient Main Memory KNN Processing,” Proc. 2003 ACM SIGMOD Int'l Conf. Management of Data, pp. 479-490, 2003.
[5] Y. Tao, D. Papadias, and Q. Shen, “Continuous Nearest Neighbor Search,” Proc. Very Large Data Bases Conf., pp. 287-298, 2002.
[6] N. Roussopoulos, S. Kelley, and F. Vincent, “Nearest Neighbor Queries,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 71-79, 1995.
[7] G.R. Hjaltason and H. Samet, “Distance Browsing in Spatial Databases,” ACM Trans. Database Systems (TODS), vol. 24, no. 2, pp. 265-318, 1999.
[8] A. Guttman, “R-Trees: A Dynamic Index Structure for Spatial Searching,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 47-57, 1984.
[9] T. Sellis, N. Roussopoulos, and C. Faloutsos, “The ${\rm{R^+{\hbox{-}}Tree}}$ : A Dynamic Index for Multidimensional Objects,” Proc. Very Large Data Bases Conf., pp. 3-11, 1987.
[10] N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger, “The ${\rm{R^+{\hbox{-}}Tree}}$ : An Efficient and Robust Access Method for Points and Rectangles,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 322-331, 1990.
[11] Z. Song and N. Roussopoulos, “K-Nearest Neighbor Search for Moving Query Point,” Proc. Symp. Spatial and Temporal Databases, pp. 79-96, 2001.
[12] D.A. White and R. Jain, “Similarity Indexing with the SS-Tree,” Proc. Int'l Conf. Data Eng. (ICDE), pp. 516-523, 1996.
[13] N. Katayama and S. Satoh, “The SR-Tree: An Index Structure for High-Dimensional Nearest Neighbor Queries,” Proc. ACM SIGMOD Int'l Conf. Management of Data, pp. 369-380, 1997.
[14] S.-W. Kim, C.C. Aggarwal, and P.S. Yu, “Effective Nearest Neighbor Indexing with the Euclidean Metric,” Proc. Conf. Information and Knowledge Management, pp. 9-16, 2001.
[15] C. Yu, B.C. Ooi, K.-L. Tan, and H.V. Jagadish, “Indexing the Distance: An Efficient Method to KNN Processing,” Proc. Very Large Data Bases Conf., pp. 421-430, 2001.
[16] S. Berchtold, B. Ertl, D.A. Keim, H.-P. Kriegel, and T. Seidl, “Fast Nearest Neighbor Search in High-Dimensional Spaces,” Proc. Int'l Conf. Data Eng., pp. 209-218, 1998.
[17] S. Berchtold, D.A. Keim, H.-P. Kriegel, and T. Seidl, “Indexing the Solution Space: A New Technique for Nearest Neighbor Search in High-Dimensional Space,” IEEE Trans. Knowledge and Data Eng., vol. 12, no. 1, pp. 45-57, Jan./Feb. 2000.
[18] B. Zheng, J. Xu, W.-C. Lee, and D.L. Lee, “Grid-Partition Index: A Hybrid Approach to Nearest-Neighbor Queries in Wireless Location-Based Services,” VLDB J., 2005.
[19] M.J. Panik, Linear Programming: Mathematics, Theory and Algorithms. Kluwer Academic, 1996.
[20] B. Moon, H. Jagadish, C. Faloutsos, and J.H. Saltz, “Analysis of the Clustering Properties of the Hilbert Space-Filling Curve,” IEEE Trans. Knowledge and Data Eng., vol. 13, no. 1, pp. 124-141, Jan./Feb. 2001.
[21] Y. Theodoridis, “Spatial Datasets: An Unofficial Collection,” http://dke.cti.gr/People/ytheod/research/ datasets spatial. html, 2005.
[22] S.J. Fortune, “A Sweepline Algorithm for Voronoi Diagrams,” Algorithmica, vol. 2, pp. 153-174, 1987.
[23] A. Hinneburg, C.C. Aggarwal, and D.A. Keim, “What is the Nearest Neighbor in High Dimensional Spaces?” Proc. 26th Int'l Conf. Very Large Data Bases, pp. 506-515, 2000.

Index Terms:
Index Terms- Spatial database, nearest-neighbor search.
Citation:
Haibo Hu, Dik Lun Lee, "Range Nearest-Neighbor Query," IEEE Transactions on Knowledge and Data Engineering, vol. 18, no. 1, pp. 78-91, Jan. 2006, doi:10.1109/TKDE.2006.15
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