Grid-partition index: a hybrid method for nearest-neighbor queries in wireless location-based services
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Traditional nearest-neighbor (NN) search is based on two basic indexing approaches: object-based indexing and solution-based indexing. The former is constructed based on the locations of data objects: using some distance heuristics on object locations. The latter is built on a precomputed solution space. Thus, NN queries can be reduced to and processed as simple point queries in this solution space. Both approaches exhibit some disadvantages, especially when employed for wireless data broadcast in mobile computing environments.
In this paper, we introduce a new index method, called the grid-partition index, to support NN search in both on-demand access and periodic broadcast modes of mobile computing. The grid-partition index is constructed based on the Voronoi diagram, i.e., the solution space of NN queries. However, it has two distinctive characteristics. First, it divides the solution space into grid cells such that a query point can be efficiently mapped into a grid cell around which the nearest object is located. This significantly reduces the search space. Second, the grid-partition index stores the objects that are potential NNs of any query falling within the cell. The storage of objects, instead of the Voronoi cells, makes the grid-partition index a hybrid of the solution-based and object-based approaches. As a result, it achieves a much more compact representation than the pure solution-based approach and avoids backtracked traversals required in the typical object-based approach, thus realizing the advantages of both approaches.
We develop an incremental construction algorithm to address the issue of object update. In addition, we present a cost model to approximate the search cost of different grid partitioning schemes. The performances of the grid-partition index and existing indexes are evaluated using both synthetic and real data. The results show that, overall, the grid-partition index significantly outperforms object-based indexes and solution-based indexes. Furthermore, we extend the grid-partition index to support continuous-nearest-neighbor search. Both algorithms and experimental results are presented.
KeywordsNearest-neighbor search Continuous-nearest-neighbor search Index structure Location-dependent data Wireless broadcast
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- 2.Berg, M., Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications Chap. 7. Berlin, Heidelberg, New York: Springer (1996)Google Scholar
- 4.Ferhatosmanoglu, H., Tuncel, E., Agrawal, D., Abbadi, A.E.: Approximate nearest neighbor searching in multimedia databases. In: Proc. 17th IEEE Int. Conf. on Data Eng. (ICDE’01) (2001)Google Scholar
- 5.Goldstein, J., Ramakrishnan, R.: Contrast plots and p-sphere trees: Space vs. time in nearest neighbor searches. In: Proc. 26th Int. Conf. on Very Large Data Bases (VLDB’00), pp. 429–440, Cairo, Egypt (2000)Google Scholar
- 6.Guttman, A.: R-trees: a dynamic index structure for spatial searching. In: Proc. ACM SIGMOD Int. Conf. on Manage. of Data (SIGMOD’84), pp. 47–54 (1984)Google Scholar
- 7.Hinneburg, A., Aggarwal, C.C., Keim, D.A.: What is the nearest neighbor in high dimensional spaces? In: Proc. 26th Int. Conf. on Very Large Data Bases (VLDB’00) (2000)Google Scholar
- 9.Hu, Q.L., Lee, W.-C., Lee, D.L.: Power conservative multi-attribute queries on data broadcast. In: Proc. 16th Int. Conf. on Data Eng. (ICDE’00), pp. 157–166. San Diego (2000)Google Scholar
- 11.Kamel, I., Faloutsos, C.: Hilbert r-tree: an improved r-tree using fractals. In: Proc. 20th Int. Conf. on Very Large Data Bases (VLDB’94), pp. 500–509. Santiago de Chile, Chile (1994)Google Scholar
- 13.Leutenegger, S.T., Edgington, J.M., Lopez, M.A.: Str: a simple and efficient algorithm for r-tree packing. In: Proc. 13th Int. Conf. on Data Eng. (ICDE’97), pp. 497–506. Birmingham, UK (1997)Google Scholar
- 15.Pramanik, S., Li, J.: Fast approximate search algorithm for nearest neighbor queries in high dimensions. In: Proc. 15th Int. Conf. on Data Eng. (ICDE’99), p. 251 (1999)Google Scholar
- 16.Roussopoulos, N., Kelley, S., Vincent, F.: Nearest neighbor queries. In: Proc. 1995 ACM SIGMOD Int. Conf. on Manage. of Data (SIGMOD’95), pp. 71–79 (1995)Google Scholar
- 17.Computer Science and Telecommunications Board. IT Roadmap to a Geospatial Future. Washington, DC: National Academies Press (2003)Google Scholar
- 18.Sistla, A.P., Wolfson, O., Chamberlain, S., Dao, S.: Modeling and querying moving objects. In: Proc. 13th Int. Conf. on Data Eng. (ICDE’97), pp. 422–432. Birmingham, UK (1997)Google Scholar
- 19.Song, Z., Roussopoulos, N.: K-nearest neighbor search for moving query point. In: Proc. 7th Int. Symp. Spatial Temp. Databases (SSTD’01), pp. 79–96. Los Angeles (2001)Google Scholar
- 21.Tao, Y., Papadias, D.: Time parameterized queries in spatio-temporal databases. In: Proc. 2002 ACM SIGMOD Int. Conf. on Manage. of Data (SIGMOD’02), pp. 334–345. Madison, WI (2002)Google Scholar
- 22.Tao, Y., Papadias, D., Shen, Q.: Continuous nearest neighbor search. In: Proc. 28th Int. Conf. on Very Large Data Bases (VLDB’02), Hong Kong (2002)Google Scholar
- 23.Weber, R., Blott, S.: An approximation based structure for similarity search. Technical Report 24, ESPRIT Project HERMES (No. 9141) (1997)Google Scholar
- 24.Weber, R., Schek, H., Blott, S.: A quantitative analysis and performance study for similarity-search methods in high-dimensional spaces. In: Proc. 24th Int. Conf. on Very Large Data Bases (VLDB’98), pp. 194–205. New York (1998)Google Scholar
- 25.Wilfong, G.T.: Nearest neighbor problems. In: Symp. on Computat. Geometry, pp. 224–233 (1991)Google Scholar
- 26.Xu, J., Zheng, B., Lee, W.-C., Lee, D.L.: Energy efficient index for querying location-dependent data in mobile broadcast environments. In: Proc. 19th IEEE Int. Conf. on Data Eng. (ICDE’03), pp. 239–250. Bangalore, India (2003)Google Scholar
- 27.Yu, C., Ooi, B.C., Tan, K.-L., Jagadish, H.V.: Indexing the distance: an efficient method to KNN processing. Proc. 27th Int. Conf. on Very Large Data Bases (VLDB’01), pp. 421–430. Rome (2001)Google Scholar
- 28.Zheng, B., Xu, J., Lee, W.C., Lee, D.L.: Energy-conserving air indexes for nearest neighbor search. In: Proc. 9th Int. Conf. on Extending Database Technol. (EDBT’04). Heraklion, Crete, Greece (2004)Google Scholar