Abstract
k Nearest Neighbor (kNN) search is one of the most important operations in spatial and spatio-temporal databases. Although it has received considerable attention in the database literature, there is little prior work on kNN retrieval for moving object trajectories. Motivated by this observation, this paper studies the problem of efficiently processing kNN (k ⩾ 1) search on R-tree-like structures storing historical information about moving object trajectories. Two algorithms are developed based on best-first traversal paradigm, called BFPkNN and BFTkNN, which handle the kNN retrieval with respect to the static query point and the moving query trajectory, respectively. Both algorithms minimize the number of node access, that is, they perform a single access only to those qualifying nodes that may contain the final result. Aiming at saving main-memory consumption and reducing CPU cost further, several effective pruning heuristics are also presented. Extensive experiments with synthetic and real datasets confirm that the proposed algorithms in this paper outperform their competitors significantly in both efficiency and scalability.
Similar content being viewed by others
References
Chen L, Ozsu M T, Oria V. Robust and fast similarity search for moving object trajectories. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Baltimore, Maryland, 2005, pp.491–502.
Lin B, Su J. Shapes based trajectory queries for moving objects. In Proc. the 13th Workshop on Advances in Geographic Information Systems, Bremen, Germany, 2005, pp.21–30.
Yanagisawa Y, Akahani J, Satoh T. Shape-based similarity query for trajectory of mobile objects. In Proc. the 4th Int. Conf. Mobile Data Management, Melbourne, Australia, 2003, pp.63–77.
Vlachos M, Kollios G, Gunopulos D. Discovering similar multidimensional trajectories. In Proc. the 18th Int. Conf. Data Engineering, San Jose, USA, 2002, pp.673–684.
Benetis R, Jensen C S, Karciauskas G, Saltenis S. Nearest neighbor and reverse nearest neighbor queries for moving objects. In Proc. Int. Database Engineering and Applications Symp., Edmonton, Canada, 2002, pp.44–53.
Cheung K L, Fu A W-C. Enhanced nearest neighbour search on the R-tree. SIGMOD Rec., 1998, 27(3): 16–21.
Frentzos E, Gratsias K, Pelekis N, Theodoridis Y. Nearest neighbor search on moving object trajectories. In Proc. the 9th Int. Symp. Advances in Spatial and Temporal Databases, Angra dos Reis, Brazil, 2005, pp.328–345.
Hjaltason G R, Samet H. Distance browsing in spatial databases. ACM Trans. Database Systems, 1999, 24(2): 265–318.
Iwerks G S, Samet H, Smith K. Continuous k-nearest neighbor queries for continuously moving points with updates. In Proc. the 29th Int. Conf. Very Large Data Bases, Berlin, Germany, 2003, pp.512–523.
Mouratidis K, Hadjieleftheriou M, Papadias D. Conceptual partitioning: An efficient method for continuous nearest neighbor monitoring. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Baltimore, USA, 2005, pp.634–645.
Mouratidis K, Papadias D, Bakiras S, Tao Y. A threshold-based algorithm for continuous monitoring of k nearest neighbors. IEEE Trans. Knowledge and Data Engineering, 2005, 17(11): 1451–1464.
Roussopoulos N, Kelley S, Vincent F. Nearest neighbor queries. In Proc. the ACM SIGMOD Int. Conf. Management of Data, San Jose, USA, 1995, pp.71–79.
Song Z, Roussopoulos N. K-nearest neighbor search for moving query point. In Proc. the 7th Int. Symp. Advances in Spatial and Temporal Databases, Redondo Beach, USA, 2001, pp.79–96.
Tao Y, Papadias D, Shen Q. Continuous nearest neighbor search. In Proc. the 28th Int. Conf. Very Large Data Bases, Hong Kong, China, 2002, pp.287–298.
Tao Y, Papadias D. Time parameterized queries in spatio-temporal databases. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Madison, USA, 2002, pp.334–345.
Xiong X, Mokbel M, Aref W. SEA-CNN: Scalable processing of continuous k-nearest neighbor queries in spatio-temporal databases. In Proc. the 21st Int. Conf. Data Engineering, Tokyo, Japan, 2005, pp.643–654.
Yu X, Pu K, Koudas N. Monitoring k-nearest neighbor queries over moving objects. In Proc. the 21st Int. Conf. Data Engineering, Tokyo, Japan, 2005, pp.631–642.
Pfoser D, Jensen C S, Theodoridis Y. Novel approaches in query processing for moving object trajectories. In Proc. the 26th Int. Conf. Very Large Data Bases, Cairo, Egypt, 2000, pp.395–406.
Manolopoulos Y, Nanopoulos A, Papadopoulos A N, Theodoridis Y. R-trees: Theory and Applications. Springer, 2005.
Theodoridis Y, Vazirgiannis M, Sellis T K. Spatio-temporal indexing for large multimedia applications. In IEEE Int. Conf. Multimedia Computing and Systems, Hiroshima, Japan, pp.441–448.
Chakka V P, Everspaugh A, Patel J M. Indexing large trajectory data sets with SETI. In Proc. the First Biennial Conf. Innovative Data Systems Research, Asilomar, USA, 2003.
Chen Z, Li C, Pei J et al. A survey on recent progress in database research. Journal of Computer Science and Technology, 2003, 18(5): 538–552.
Papadopoulos A, Manolopoulos Y. Parallel processing of nearest neighbor queries in declustered spatial data. In Proc. the 4th Workshop on Advances in Geographic Information Systems, Rockville, Maryland, 1996, pp.35–43.
Guttman A. R-trees: A dynamic index structure for spatial searching. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Boston, USA, 1984, pp.47–57.
Sellis T, Roussopoulos N, Faloutsos C. The R+-tree: A dynamic index for multi-dimensional objects. In Proc. the 13th Int. Conf. Very Large Data Bases, Brighton, England, 1987, pp.507–518.
Beckmann N, Kriegel H-P, Schneider R, Seeger B. The R*-tree: An efficient and robust access method for points and rectangles. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Atlantic City, NJ, 1990, pp.322–331.
Theodoridis Y. The R-tree portal. http://www.rtreeportal.org.
Theodoridis Y, Silva J R O, Nascimento M A. On the generation of spatiotemporal datasets. In Proc. the 6th Int. Symp. Advances in Spatial Databases, Hong Kong, China, 1999, pp.147–164.
Corral A, Manolopoulos Y, Theodoridis Y, Vassilakopoulos M. Closest pair queries in spatial databases. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Dallas, USA, 2000, pp.189–200.
Corral A, Manolopoulos Y, Theodoridis Y, Vassilakopoulos M. Algorithms for processing k-closest-pair queries in spatial databases. Data & Knowledge Engineering, 2004, 49(1): 67–104.
Hjaltason G R, Samet H. Incremental distance join algorithms for spatial databases. In Proc. the ACM SIGMOD Int. Conf. Management of Data, Seattle, Washington, 1998, pp.237–248.
Arumugam S, Jermaine C. Closest-point-of-approach join for moving object histories. In Proc. the 22nd Int. Conf. Data Engineering, Atlanta, Georgia, 2006, p.86.
Author information
Authors and Affiliations
Corresponding author
Additional information
Supported by the National High Technology Development 863 Program of China under Grant No. 2003AA4Z3010-03.
Electronic supplementary material
Rights and permissions
About this article
Cite this article
Gao, YJ., Li, C., Chen, GC. et al. Efficient k-Nearest-Neighbor Search Algorithms for Historical Moving Object Trajectories. J Comput Sci Technol 22, 232–244 (2007). https://doi.org/10.1007/s11390-007-9030-x
Received:
Revised:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11390-007-9030-x