A Road Network Embedding Technique for K-Nearest Neighbor Search in Moving Object Databases
- 633 Downloads
A very important class of queries in GIS applications is the class of K-nearest neighbor queries. Most of the current studies on the K-nearest neighbor queries utilize spatial index structures and hence are based on the Euclidean distances between the points. In real-world road networks, however, the shortest distance between two points depends on the actual path connecting the points and cannot be computed accurately using one of the Minkowski metrics. Thus, the Euclidean distance may not properly approximate the real distance. In this paper, we apply an embedding technique to transform a road network to a high dimensional space in order to utilize computationally simple Minkowski metrics for distance measurement. Subsequently, we extend our approach to dynamically transform new points into the embedding space. Finally, we propose an efficient technique that can find the actual shortest path between two points in the original road network using only the embedding space. Our empirical experiments indicate that the Chessboard distance metric (L∞) in the embedding space preserves the ordering of the distances between a point and its neighbors more precisely as compared to the Euclidean distance in the original road network.
Unable to display preview. Download preview PDF.
- 1.S. Berchtold, B. Ertl, D.A. Keim, H.-P. Kriegel, and T. Seidl. “Fast nearest neighbor search in high-dimensional spaces,” in Proc. 14th IEEE Conf. Data Engineering, ICDE, 23–27, 1998.Google Scholar
- 2.S. Berchtold, D.A. Keim, and H.-P. Kriegel. “The X-tree: An index structure for high-dimensional data,” in Proceedings of the 22nd International Conference on Very Large Databases, 28–39, Morgan Kaufmann Publishers, San Francisco, U.S.A., 1996.Google Scholar
- 3.C. Faloutsos and K.-I. Lin. “Fastmap: A fast algorithm for indexing, data-mining and visualization of traditional and multimedia datasets,” in Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, San Jose, California, 163–174, ACM Press, May 22–25, 1995.Google Scholar
- 4.H. Ferhatosmanoglu, I. Stanoi, D. Agrawal, and A.E. Abbadi. “Constrained nearest neighbor queries,” in SSTD, Redondo Beach, USA, 257–278, July 12–15, 2001.Google Scholar
- 5.A. Guttman. “R-trees: A dynamic index structure for spatial searching,” in SIGMOD'84, Proceedings of Annual Meeting, Boston, Massachusetts, 47–57, ACM Press, June 18–21, 1984.Google Scholar
- 6.S.L. Hakimi, M. Labbe, and E. Schmeichel. “The voronoi partition of a network and its implications in location theory,” in ORSA Journal on Computing, Vol. 4, 1992.Google Scholar
- 7.G.R. Hjaltason and H. Samet. “Distance browsing in spatial databases,” in ACM Transactions on Database Systems, Vol. 24, 265–318, 1999.Google Scholar
- 8.N. Linial, E. London, and Y. Rabinovich. “The geometry of graphs and some of its algorithmic applications,” in IEEE Symposium on Foundations of Computer Science, 577–591, 1994.Google Scholar
- 9.N. Roussopoulos, S. Kelley, and F. Vincent. “Nearest neighbor queries,” in Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data, San Jose, California, 71–79, ACM Press, May 22–25, 1995.Google Scholar
- 10.Z. Song and N. Roussopoulos. “K-nearest neighbor search for moving query point,” in SSTD, Redondo Beach, USA, July 12–15, 2001.Google Scholar
- 11.Y. Tao, D. Papadias, and Q. Shen. “Continuous nearest neighbor search,” in Proceedings of the Very Large Data Bases Conference (VLDB), Hong Kong, China, 2002.Google Scholar
- 12.C. Yu, B.C. Ooi, K.-L. Tan, and H.V. Jagadish. “Indexing the distance: An efficient method to KNN processing,” The VLDB Journal, 421–430, 2001.Google Scholar