Indexing Fast Moving Objects for kNN Queries Based on Nearest Landmarks
- 115 Downloads
With the rapid advancements in positioning technologies such as the Global Positioning System (GPS) and wireless communications, the tracking of continuously moving objects has become more convenient. However, this development poses new challenges to database technology since maintaining up-to-date information regarding the location of moving objects incurs an enormous amount of updates. Existing indexes can no longer keep up with the high update rate while providing speedy retrieval at the same time. This study aims to improve k nearest neighbor (kNN) query performance while reducing update costs. Our approach is based on an important observation that queries usually occur around certain places or spatial landmarks of interest, called reference points. We propose the Reference-Point-based tree (RP-tree), which is a two-layer index structure that indexes moving objects according to reference points. Experimental results show that the RP-tree achieves significant improvement over the TPR-tree.
Keywordsmoving object index nearest neighbor query
Unable to display preview. Download preview PDF.
- 1.“Digital chart of the world server,” in http://www.maproom.psu.edu/dcw/.
- 2.C. C. Aggarwal and D. Agrawal. “On nearest neighbor indexing of nonlinear trajectories,” in Proc. of ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, San Diego, California, USA, pp. 252–259, June 2003.Google Scholar
- 3.N. Beckmann, H. Kriegel, R. Schneider, and B. Seeger. “The r*-tree: an efficient and robust access method for points and rectangles,” in Proc. of ACM SIGMOD Int. Conf. on Manag. of Data, Atlantic City, New Jersey, USA, pp. 322–331, May 1990.Google Scholar
- 4.R. Benetis, C.S. Jensen, G. Karciauskas, and S. Saltenis. “Nearest neighbor and reverse nearest neighbor queries for moving objects,” in Proc. of Int. Database Engineering and Applications Symposium, Edmonton, Canada, pp. 44–53, July 2002.Google Scholar
- 7.A. Guttman. “R-trees: a dynamic index structure for spatial searching,” in Proc. of ACM SIGMOD Int. Conf. on Management of Data, Boston, Massachusetts, pp. 47–57, June 1984.Google Scholar
- 8.G. Kollios, D. Gunopulos, and V.J. Tsotras. “Nearest neighbor queries in a mobile environment,” in Proc. of Int. Workshop on Spatio-Temporal Database Management, Hong Kong, China, pp. 119–134, September 1999.Google Scholar
- 9.M. Kornacker and D. Banks. “High-concurrency locking in r-trees,” in Proc. of Int. Conf. on Very Large Data Bases, Zurich, Switzerland, pp. 134–145, September 1995.Google Scholar
- 10.D. Kwon, S. Lee, and S. Lee. “Indexing the current positions of moving objects using the lazy update,” in Proc. of Int. Conf. on Mobile Data Management, Singapore, pp. 113–120, January 2002.Google Scholar
- 11.B.C. Ooi, K.L. Tan, and C. Yu. “Fast update and efficient retrieval: an oxymoron on moving object indexes,” in Proc. of Int. Web GIS Workshop, Keynote, Singapore, December 2002.Google Scholar
- 12.D. Pfoser, C.S. Jensen, and Y. Theodoridis. “Novel approaches in query processing for moving objects,” in Proc. of Int. Conf. on Very Large Data Bases, Cairo, Egypt, pp. 395–406, September 2000.Google Scholar
- 13.S. Saltenis, C.S.Jensen, S.T. Leutenegger, and M.A. Lopez. “Indexing the positions of continuously moving objects,” in Proc. of ACM SIGMOD Int. Conf. on Mangement of Data, Dallas, Texas, USA, pp. 331–342, May 2000.Google Scholar
- 14.T. Sellis, N. Roussopoulos, and C. Faloutsos. “The r+-tree: a dynamic index for multi-dimensional objects,” in Proc. of Int. Conf. on Very Large Data Bases, Brighton, England, pp. 507–518, September 1987.Google Scholar
- 15.Y. Tao and D. Papadias. “Mv3r-tree: a spatio-temporal access method for timestamp and interval queries,” in Proc. of Int. Conf. on Very Large Data Bases, Roma, Italy, pp. 431–440, September 2001.Google Scholar
- 16.Y. Tao, D. Papadias, and J. Sun. “The tpr*-tree: an optimized spatio-temporal access method for predictive queries,” in Proc. of Int. Conf. on Very Large Data Bases, Berlin, Germany, pp. 790–801, September 2003.Google Scholar
- 19.Y. Theodoridis and T. Sellis. “Model for the prediction of r-tree performance,” in Proc. of ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Montreal, Quebec, Canada, pp. 161–171, June 1996.Google Scholar
- 20.D.A. White and R. Jain. “Similarity indexing with the ss-tree,” in Proc. of Int. Conf. on Data Engineering, New Orleans, Louisiana, pp. 516–523, February 1996.Google Scholar
- 21.Y. Xia and S. Prabhakar. “Q+rtree: efficient indexing for moving object data bases,” in Proc. of Int. Conf. on Database Systems for Advanced Applications, Kyoto, Japan, pp. 175–182, March 2003.Google Scholar