New upper bounds for neighbor searching
Information and Control
Algorithms for two bottleneck optimization problems
Journal of Algorithms
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Computing Euclidean bottleneck matchings in higher dimensions
Information Processing Letters
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
Capacity constrained assignment in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A n5/2 algorithm for maximum matchings in bipartite
SWAT '71 Proceedings of the 12th Annual Symposium on Switching and Automata Theory (swat 1971)
Assignment Problems
Introduction to Algorithms, Third Edition
Introduction to Algorithms, Third Edition
Efficient method for maximizing bichromatic reverse nearest neighbor
Proceedings of the VLDB Endowment
Continuous spatial assignment of moving users
The VLDB Journal — The International Journal on Very Large Data Bases
Maximum flows by incremental breadth-first search
ESA'11 Proceedings of the 19th European conference on Algorithms
On optimal worst-case matching
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
On optimal worst-case matching
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
| Hi-index | 0.00 |
Bichromatic reverse nearest neighbor (BRNN) queries have been studied extensively in the literature of spatial databases. Given a set P of service-providers and a set O of customers, a BRNN query is to find which customers in O are "interested" in a given service-provider in P. Recently, it has been found that this kind of queries lacks the consideration of the capacities of service-providers and the demands of customers. In order to address this issue, some spatial matching problems have been proposed, which, however, cannot be used for some real-life applications like emergency facility allocation where the maximum matching cost (or distance) should be minimized. In this paper, we propose a new problem called Spatial Matching for Minimizing Maximum matching distance (SPM-MM). Then, we design two algorithms for SPM-MM, Threshold-Adapt and Swap-Chain. Threshold-Adapt is simple and easy to understand but not scalable to large datasets due to its relatively high time/space complexity. Swap-Chain, which follows a fundamentally different idea from Threshold-Adapt, runs faster than Threshold-Adapt by orders of magnitude and uses significantly less memory. We conducted extensive empirical studies which verified the efficiency and scalability of Swap-Chain.