The Community for Technology Leaders
RSS Icon
Issue No.12 - December (2008 vol.20)
pp: 1641-1654
Leong Hou U , University of Hong Kong, Hong Kong
Man Lung Yiu , Aalborg University, Aalborg
Given two datasets $A$ and $B$, their exclusive closest pairs (ECP) join is a one-to-one assignment of objects from the two datasets, such that (i) the closest pair $(a,b)$ in $A \times B$ is in the result and (ii) the remaining pairs are determined by removing objects $a,b$ from $A,B$ respectively, and recursively searching for the next closest pair. A real application of exclusive closest pairs is the computation of (car, parking slot) assignments. This paper introduces the problem and proposes several solutions that solve it in main-memory, exploiting space partitioning. In addition, we define a dynamic version of the problem, where the objective is to continuously monitor the ECP join solution, in an environment where the joined datasets change positions and content. Finally, we study an extended form of the query, where objects in one of the two joined sets (e.g., parking slots) have a capacity constraint, allowing them to match with multiple objects from the other set (e.g., cars). We show how our techniques can be extended for this variant and compare them with a previous solution to this problem. Experimental results on a system prototype demonstrate the efficiency and applicability of the proposed algorithms.
Query processing, Spatial databases
Leong Hou U, Man Lung Yiu, "Computation and Monitoring of Exclusive Closest Pairs", IEEE Transactions on Knowledge & Data Engineering, vol.20, no. 12, pp. 1641-1654, December 2008, doi:10.1109/TKDE.2008.85
[1] M.R. Anderberg, Cluster Analysis for Applications. Academic Press, 1973.
[2] C. Böhm and F. Krebs, “The $k\hbox{-}{\rm Nearest}$ Neighbour Join: Turbo Charging the KDD Process,” Knowledge and Information Systems, vol. 6, no. 6, pp. 728-749, 2004.
[3] A. Corral, Y. Manolopoulos, Y. Theodoridis, and M. Vassilakopoulos, “Closest Pair Queries in Spatial Databases,” Proc. ACM SIGMOD, 2000.
[4] U. Derigs, “A Shortest Augmenting Path Method for Solving Minima Perfect Matching Problems,” Networks, vol. 11, no. 4, pp.379-390, 1981.
[5] D. Eppstein, “Fast Hierarchical Clustering and Other Applications of Dynamic Closest Pairs,” ACM J. Experimental Algorithms, vol. 5, no. 1, 2000.
[6] D. Gale and L.S. Shapley, “College Admissions and the Stability of Marriage,” Am. Math. Monthly, vol. 69, pp. 9-14, 1962.
[7] A. Guttman, “R-Trees: A Dynamic Index Structure for Spatial Searching,” Proc. ACM SIGMOD, 1984.
[8] G.R. Hjaltason and H. Samet, “Incremental Distance Join Algorithms for Spatial Databases,” Proc. ACM SIGMOD, 1998.
[9] G.R. Hjaltason and H. Samet, “Distance Browsing in Spatial Databases,” ACM Trans. Database Systems, vol. 24, no. 2, pp. 265-318, 1999.
[10] G.S. Iwerks, H. Samet, and K.P. Smith, “Maintenance of k-nn and Spatial Join Queries on Continuously Moving Points,” ACM Trans. Database Systems, vol. 31, no. 2, pp. 485-536, 2006.
[11] N. Koudas and K.C. Sevcik, “High Dimensional Similarity Joins: Algorithms and Performance Evaluation,” Proc. IEEE Int'l Conf. Data Eng. (ICDE), 1998.
[12] H.W. Kuhn, “The Hungarian Method for the Assignment Problem,” Naval Research Logistics, 2005.
[13] M.-L. Lee, W. Hsu, C.S. Jensen, B. Cui, and K.L. Teo, “Supporting Frequent Updates in R-Trees: A Bottom-Up Approach,” Proc. Int'l Conf. Very Large Data Bases (VLDB), 2003.
[14] M.F. Mokbel, X. Xiong, and W.G. Aref, “SINA: Scalable Incremental Processing of Continuous Queries in Spatio-Temporal Databases,” Proc. ACM SIGMOD, 2004.
[15] M.F. Mokbel, X. Xiong, M.A. Hammad, and W.G. Aref, “Continuous Query Processing of Spatio-Temporal Data Streams in Place,” Proc. Int'l Workshop Spatio-Temporal Database Management (STDBM), 2004.
[16] K. Mouratidis, D. Papadias, and M. Hadjieleftheriou, “Conceptual Partitioning: An Efficient Method for Continuous Nearest Neighbor Monitoring,” Proc. ACM SIGMOD, 2005.
[17] E.D. Nering and A.W. Tucker, Linear Programs & Related Problems: A Volume in the Computer Science and Scientific Computing Series. Academic Press, 1992.
[18] I. Stanoi, M. Riedewald, D. Agrawal, and A.E. Abbadi, “Discovery of Influence Sets in Frequently Updated Databases,” Proc. Int'l Conf. Very Large Data Bases (VLDB), 2001.
[19] L.H. U, N. Mamoulis, and M.L. Yiu, “Continuous Monitoring of Exclusive Closest Pairs,” Proc. Int'l Symp. Spatial and Temporal Databases (SSTD), 2007.
[20] R.C.-W. Wong, Y. Tao, A.W.-C. Fu, and X. Xiao, “On Efficient Spatial Matching,” Proc. Int'l Conf. Very Large Data Bases (VLDB), 2007.
[21] C. Xia, H. Lu, B.C. Ooi, and J. Hu, “Gorder: An Efficient Method for KNN Join Processing,” Proc. Int'l Conf. Very Large Data Bases (VLDB), 2004.
[22] X. Xiong and W.G. Aref, “R-Trees with Update Memos,” Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2006.
[23] X. Xiong, M.F. Mokbel, and W.G. Aref, “SEA-CNN: Scalable Processing of Continuous k-Nearest Neighbor Queries in Spatio-Temporal Databases,” Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2005.
[24] C. Yang and K.-I. Lin, “An Index Structure for Improving Nearest Closest Pairs and Related Join Queries in Spatial Databases,” Proc. Int'l Database Eng. and Applications Symp. (IDEAS), 2002.
[25] X. Yu, K.Q. Pu, and N. Koudas, “Monitoring k-Nearest Neighbor Queries over Moving Objects,” Proc. IEEE Int'l Conf. Data Eng. (ICDE), 2005.
[26] J. Zhang, N. Mamoulis, D. Papadias, and Y. Tao, “All-Nearest-Neighbors Queries in Spatial Databases,” Proc. Int'l Conf. Scientific and Statistical Database Management (SSDBM), 2004.
25 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool