Capacity Constrained Assignment in Spatial Databases
Given a point set P of customers (e.g., WiFi receivers) and a point set Q of service providers (e.g., wireless access points), where each q 2 Q has a capacity q.k, the capacity constrained assignment (CCA) is a matching M Q × P such that (i) each point q 2 Q (p 2 P) appears at most k times (at most...
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sg-smu-ink.sis_research-14112016-04-29T09:34:38Z Capacity Constrained Assignment in Spatial Databases LEONG, Hou U YIU, Man Lung MOURATIDIS, Kyriakos MAMOULIS, Nikos Given a point set P of customers (e.g., WiFi receivers) and a point set Q of service providers (e.g., wireless access points), where each q 2 Q has a capacity q.k, the capacity constrained assignment (CCA) is a matching M Q × P such that (i) each point q 2 Q (p 2 P) appears at most k times (at most nce) in M, (ii) the size of M is maximized (i.e., it comprises min{|P|,P q2Q q.k} pairs), and (iii) the total assignment cost (i.e., the sum of Euclidean distances within all pairs) is minimized. Thus, the CCA problem is to identify the assignment with the optimal overall quality; intuitively, the quality of q’s service to p in a given (q, p) pair is anti-proportional to their distance. Although max-flow algorithms are applicable to this problem, they require the complete distance-based bipartite graph between Q and P. For large spatial datasets, this graph is expensive to compute and it may be too large to fit in main memory. Motivated by this fact, we propose efficient algorithms for optimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem. Additionally, we develop approximate (i.e., suboptimal) CCA solutions that provide a trade-off between result accuracy and computation cost, abiding by theoretical quality guarantees. A thorough experimental evaluation demonstrates the efficiency and practicality of the proposed techniques. 2008-06-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/412 info:doi/10.1145/1376616.1376621 https://ink.library.smu.edu.sg/context/sis_research/article/1411/viewcontent/SIGMOD08_CCA.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Algorithms efficiency spatial databases Databases and Information Systems Numerical Analysis and Scientific Computing |
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Algorithms efficiency spatial databases Databases and Information Systems Numerical Analysis and Scientific Computing LEONG, Hou U YIU, Man Lung MOURATIDIS, Kyriakos MAMOULIS, Nikos Capacity Constrained Assignment in Spatial Databases |
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Given a point set P of customers (e.g., WiFi receivers) and a point set Q of service providers (e.g., wireless access points), where each q 2 Q has a capacity q.k, the capacity constrained assignment (CCA) is a matching M Q × P such that (i) each point q 2 Q (p 2 P) appears at most k times (at most nce) in M, (ii) the size of M is maximized (i.e., it comprises min{|P|,P q2Q q.k} pairs), and (iii) the total assignment cost (i.e., the sum of Euclidean distances within all pairs) is minimized. Thus, the CCA problem is to identify the assignment with the optimal overall quality; intuitively, the quality of q’s service to p in a given (q, p) pair is anti-proportional to their distance. Although max-flow algorithms are applicable to this problem, they require the complete distance-based bipartite graph between Q and P. For large spatial datasets, this graph is expensive to compute and it may be too large to fit in main memory. Motivated by this fact, we propose efficient algorithms for optimal assignment that employ novel edge-pruning strategies, based on the spatial properties of the problem. Additionally, we develop approximate (i.e., suboptimal) CCA solutions that provide a trade-off between result accuracy and computation cost, abiding by theoretical quality guarantees. A thorough experimental evaluation demonstrates the efficiency and practicality of the proposed techniques. |
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text |
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LEONG, Hou U YIU, Man Lung MOURATIDIS, Kyriakos MAMOULIS, Nikos |
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LEONG, Hou U YIU, Man Lung MOURATIDIS, Kyriakos MAMOULIS, Nikos |
author_sort |
LEONG, Hou U |
title |
Capacity Constrained Assignment in Spatial Databases |
title_short |
Capacity Constrained Assignment in Spatial Databases |
title_full |
Capacity Constrained Assignment in Spatial Databases |
title_fullStr |
Capacity Constrained Assignment in Spatial Databases |
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Capacity Constrained Assignment in Spatial Databases |
title_sort |
capacity constrained assignment in spatial databases |
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Institutional Knowledge at Singapore Management University |
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2008 |
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https://ink.library.smu.edu.sg/sis_research/412 https://ink.library.smu.edu.sg/context/sis_research/article/1411/viewcontent/SIGMOD08_CCA.pdf |
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