The R*-tree: an efficient and robust access method for points and rectangles
SIGMOD '90 Proceedings of the 1990 ACM SIGMOD international conference on Management of data
Network flows: theory, algorithms, and applications
Network flows: theory, algorithms, and applications
An efficient cost scaling algorithm for the assignment problem
Mathematical Programming: Series A and B
Distance browsing in spatial databases
ACM Transactions on Database Systems (TODS)
Closest pair queries in spatial databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A Framework for Generating Network-Based Moving Objects
Geoinformatica
R-trees: a dynamic index structure for spatial searching
SIGMOD '84 Proceedings of the 1984 ACM SIGMOD international conference on Management of data
The R+-Tree: A Dynamic Index for Multi-Dimensional Objects
VLDB '87 Proceedings of the 13th International Conference on Very Large Data Bases
Database Systems Concepts
Clustering objects on a spatial network
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Incremental assignment problem
Information Sciences: an International Journal
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A fair assignment algorithm for multiple preference queries
Proceedings of the VLDB Endowment
Efficient method for maximizing bichromatic reverse nearest neighbor
Proceedings of the VLDB Endowment
Optimal matching between spatial datasets under capacity constraints
ACM Transactions on Database Systems (TODS)
Continuous spatial assignment of moving users
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient methods for finding influential locations with adaptive grids
Proceedings of the 20th ACM international conference on Information and knowledge management
Matching query processing in high-dimensional space
Proceedings of the 20th ACM international conference on Information and knowledge management
Maximizing bichromatic reverse nearest neighbor for Lp-norm in two- and three-dimensional spaces
The VLDB Journal — The International Journal on Very Large Data Bases
HPCA'09 Proceedings of the Second international conference on High Performance Computing and Applications
Location selection for utility maximization with capacity constraints
Proceedings of the 21st ACM international conference on Information and knowledge management
GeoCrowd: enabling query answering with spatial crowdsourcing
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
On optimal worst-case matching
Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data
Optimal k-constraint coverage queries on spatial objects
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
Shortlisting top-K assignments
Proceedings of the 25th International Conference on Scientific and Statistical Database Management
On minimizing the resource consumption of cloud applications using process migrations
Journal of Parallel and Distributed Computing
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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 ∈ Q has a capacity q.k, the capacity constrained assignment (CCA) is a matching M ⊆ Q × P such that (i) each point q ∈ Q (p ∈ P) appears at most k times (at most once) in M, (ii) the size of M is maximized (i.e., it comprises min{|P|, ∑q∈Qq.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.