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http://dx.doi.org/10.9717/kmms.2019.22.12.1466

Efficient Processing of All-farthest-neighbors Queries in Spatial Network Databases  

Cho, Hyung-Ju (Department of Software, Kyungpook National University)
Publication Information
Journal of Korea Multimedia Society / v.22, no.12, 2019 , pp. 1466-1480 More about this Journal
Abstract
This paper addresses the efficient processing of all-farthest-neighbors (AFN) queries in spatial network databases. Given a set of data points P={p1,p2,…,p|p|} in a spatial network, where the distance between two data points p and s, denoted by dist (p,s), is the length of the shortest path between them, an AFN query is defined as follows: find the farthest neighbor ω(p)∈P of each data point p such that dist(p,ω(p)) ≥ dist(p,s) for all s∈P. In this paper, we propose a shared execution algorithm called FAST (for All-Farthest-neighbors Search in spatial neTworks). Extensive experiments on real-world roadmaps confirm the efficiency and scalability of the FAST algorithm, while demonstrating a speedup of up to two orders of magnitude over a conventional solution.
Keywords
All-farthest-neighbors Query; Maximum Distance; Spatial Network Database;
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1 Y. Chen and J.M. Patel, "Efficient Evaluation of All-nearest-neighbor Queries," Proceeding of International Conference on Data Engineering, pp. 1056-1065, 2007.
2 J. Zhang, N. Mamoulis, D. Papadias, and Y. Tao, "All-nearest-neighbors Queries in Spatial Databases," Proceeding of International Conference on Scientific and Statistical Database Management, pp. 297-306, 2004.
3 T. Abeywickrama, M.A. Cheema, and D. Taniar, "K-nearest Neighbors on Road Networks: A Journey in Experimentation and In-Memory Implementation," Proceeding of International Conference on Very Large Data Bases Endowment, Vol. 9, No. 6, pp. 492-503, 2016.
4 O.S. Kwon and H.J. Cho, “Design and Implementation of Real-time Shortest Path Search System in Directed and Dynamic Roads,” Journal of Korea Multimedia Society, Vol. 20, No. 4, pp. 649-659, 2017.   DOI
5 A. Aggarwal and D. Kravets, “A Linear Time Algorithm for Finding All Farthest Neighbors in a Convex Polygon,” Information Processing Letters, Vol. 31, No. 1, pp. 17-20, 1989.   DOI
6 S.W. Bae, M. Korman, and T. Tokuyama, "All Farthest Neighbors in the Presence of Highways and Obstacles," Proceeding of International Workshop on Algorithms and Computation, pp. 71-82, 2009.
7 R.R. Curtin, J. Echauz, and A.B. Gardner, "Exploiting the Structure of Furthest Neighbor Search for Fast Approximate Results," Information Systems, Vol. 80, pp. 124-135, 2019.   DOI
8 Y. Gao, L. Shou, K. Chen, and G. Chen, "Aggregate Farthest-neighbor Queries over Spatial Data," Proceeding of International Conference on Database Systems for Advanced Applications, pp. 149-163, 2011.
9 S. Wang, M.A. Cheema, X. Lin, Y. Zhang, and D. Liu, "Efficiently Computing Reverse k Furthest Neighbors," Proceeding of International Conference on Data Engineering, pp. 1110-1121, 2016.
10 B. Yao, F. Li, and P. Kumar, "Reverse Furthest Neighbors in Spatial Databases," Proceeding of International Conference on Data Engineering, pp. 664-675, 2009.
11 J.R. Thomsen, M.L. Yiu, and C.S. Jensen, "Effective Caching of Shortest Paths for Location-Based Services," Proceeding of International Conference on Management of Data, pp. 313-324, 2012.
12 H. Wang, K. Zheng, H. Su, J. Wang, S.W. Sadiq, and X. Zhou, "Efficient Aggregate Farthest Neighbour Query Processing on Road Networks," Proceeding of the Australasian Database Conference, pp. 13-25, 2014.
13 T.H. Cormen, C.E. Leiserson, R.L. Rivest, and C. Stein, Introduction to Algorithms, Third Edition, The Massachusetts Institute of Technology Press, London, England, 2009.
14 H. Mahmud, A.M. Amin, M.E. Ali, T. Hashem, and S. Nutanong, "A Group Based Approach for Path Queries in Road Networks," Proceeding of International Symposium on Spatial and Temporal Databases, pp. 367-385, 2013.
15 D. Zhang, C.Y. Chow, Q. Li, X. Zhang, and Y. Xu, “SMashQ: Spatial Mashup Framework for k-NN Queries in Time-dependent Road Networks,” Distributed and Parallel Databases, Vol. 31, No. 2, pp. 259-287, 2013.   DOI
16 H. Bast, S. Funke, and D. Matijevic, "TRANSIT: Ultrafast Shortest-path Queries with Linear-time Preprocessing," Proceeding of the 9th DIMACS Implementation Challenge, pp. 175-192, 2006.
17 X.J. Xu, J.S. Bao, B. Yao, J. Zhou, F. Tang, M. Guo, et al., “Reverse Furthest Neighbors Query in Road Networks,” Journal of Computer Science and Technology, Vol. 32, No. 1, pp. 155-167, 2017.   DOI
18 R. Zhong, G. Li, K.L. Tan, L. Zhou, and Z. Gong, “G-Tree: An Efficient and Scalable Index for Spatial Search on Road Networks,” IEEE Transactions on Knowledge and Data Engineering, Vol. 27, No. 8, pp. 2175-2189, 2015.   DOI
19 H. Lu and M.L. Yiu, “On Computing Farthest Dominated Locations,” IEEE Transactions on Knowledge and Data Engineering, Vol. 23, No. 6, pp. 928-941, 2011.   DOI
20 Q.T. Tran, D. Taniar, and M. Safar, "Reverse k Nearest Neighbor and Reverse Farthest Neighbor Search on Spatial Networks," Transactions on Large-scale Data- and Knowledge- centered Systems, pp. 353-372, 2009.
21 A. Guttman, "R-trees: A Dynamic Index Structure for Spatial Searching," Proceeding of International Conference on Management of Data, pp. 47-57, 1984.
22 O. Daescu, N. Mi, C.S. Shin, and A. Wolff, “Farthest-point Queries with Geometric and Combinatorial Constraints,” Computational Geometry, Vol. 33, No. 3, pp. 174-185, 2006.   DOI
23 N. Katoh and K. Iwano, “Finding k Farthest Pairs and k Closest/Farthest Bichromatic Pairs for Points in the Plane,” International Journal of Computational Geometry and Applications, Vol. 5, No. 01n02, pp. 37-51, 1995.   DOI
24 L. Wu, X. Xiao, D. Deng, G. Cong, A.D. Zhu, and S. Zhou, “Shortest Path and Distance Queries on Road Networks: an Experimental Evaluation,” The Proceeding of the Very Large Data Bases Endowment, Vol. 5, No. 5, pp. 406-417, 2012.
25 Y. Yang, H. Li, J. Wang, Q. Hu, X. Wang, and M. Leng, "A Novel Index Method for k Nearest Object Query over Time-dependent Road Networks," Complexity, Vol. 2019, Article ID 4829164, 2019.
26 Real Datasets for Spatial Databases, https://www.cs.utah.edu/-lifeifei/SpatialDataset.htm (accessed July 20, 2019).