Search K Nearest Neighbors on Air
While the K-Nearest-Neighbor (KNN) problem is well studied in the traditional wired, disk-based client-server environment, it has not been tackled in a wireless broadcast environment. In this paper, the problem of organizing location dependent data and answering KNN queries on air are investigated. The linear property of wireless broadcast media and power conserving requirement of mobile devices make this problem particularly interesting and challenging. An efficient data organization, called sorted list, and the corresponding search algorithm are proposed and compared with the well-known spatial index, R-Tree. In addition, we develop an approximate search scope to guide the search at the very beginning of the search process and a learning algorithm to adapt the search scope during the search to improve energy and access efficiency. Simulation based performance evaluation is conducted to compare sorted list and R-Tree. The results show that the utilization of search scope and learning algorithm improves search efficiency of both index mechanisms significantly. While R-Tree is more power efficient when a large number of nearest neighbors is requested, the sorted list has better access efficiency and less power consumption when the number of nearest neighbors is small.
KeywordsKNN location-dependent search wireless broadcast index structure mobile computing
Unable to display preview. Download preview PDF.
- S. Chaudhuri and L. Gravano. Evaluating top-k selection queries. In Proceedings of the 25th International Conference on Very Large Data Bases (VLDB’99), pages 397–410, 1999.Google Scholar
- H. Ferhatosmanoglu, E. Tuncel, D. Agrawal, and A. E. Abbadi. Approximate nearest neighbor searching in multimedia databases. In Proceedings of the 17th IEEE International Conference on Data Engineering (ICDE’01), April 2001.Google Scholar
- G. R. Hjaltason and H. Samet. Ranking in spatial databases. In Proceedings of the 4th International Symposium on Advances in Spatial Databases (SSD’95), pages 83–95, 1995.Google Scholar
- Q. L. Hu, W.-C. Lee, and D. L. Lee. Power conservative multi-attribute queries on data broadcast. In Proceedings of the 16th International Conference on Data Engineering (ICDE’2000), pages 157–166, San Diego, CA, USA, February 2000.Google Scholar
- T. Imielinski, S. Viswanathan, and B. R. Badrinath. Data on air-organization and access. IEEE Transactions on Knowledge and Data Engineering (TKDE), 9(3), May–June 1997.Google Scholar
- N. Katayama and S. Satoh. The SR-tree: An index structure for high-dimensional nearest neighbor queries. In Proceedings of ACM SIGMOD International Conference on Management of Data, Tucson, AZ, May 1997.Google Scholar
- F. Korn, N. Sidiropoulos, C. Faloutsos, E. Siegel, and Z. Protopapas. Fast nearest neighbor search in medical image databases. In Proceedings of the 22th International Conference on Very Large Data Bases (VLDB’96), pages 215–226, 1996.Google Scholar
- D-Z. Liu, E. Lim, and W. Ng. Efficient k nearest neighbor queries on remote spatial databases using range estimation. In Proceedings of the 14th International Conference on Scientific and Statistical Database Management(SSDBM’02), Edinburgh, Scotland, 2002.Google Scholar
- N. Roussopoulos, S. Kelley, and F. Vincent. Nearest neighbor queries. In Proceedings of the 1995 ACM SIGMOD International Conference on Management of Data (Sigmod’95), pages 71–79, May 1995.Google Scholar
- T. Seidl and H. Kriegel. Optimal multi-step k-nearest neighbor search. In Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data (Sigmod’98), pages 154–165, July 1998.Google Scholar
- B. Zheng, W. C. Lee, and D. L. Lee. K-nearest neighbor queries in wireless broadcasting environments. Technical report, Dept. of Computer Science, Hong Kong Univ. of Science and Technology, July. 2002.Google Scholar