Influence sets based on reverse nearest neighbor queries
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Discovery of Influence Sets in Frequently Updated Databases
Proceedings of the 27th International Conference on Very Large Data Bases
On computing top-t most influential spatial sites
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Progressive computation of the min-dist optimal-location query
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
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
FINCH: evaluating reverse k-Nearest-Neighbor queries on location data
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)
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Top-k most influential locations selection
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
The Min-dist Location Selection Query
ICDE '12 Proceedings of the 2012 IEEE 28th International Conference on Data Engineering
Efficiently processing snapshot and continuous reverse k nearest neighbors queries
The VLDB Journal — The International Journal on Very Large Data Bases
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Given a set of client locations, a set of facility locations where each facility has a service capacity, and the assumptions that: (i) a client seeks service from its nearest facility; (ii) a facility provides service to clients in the order of their proximity, we study the problem of selecting all possible locations such that setting up a new facility with a given capacity at these locations will maximize the number of served clients. This problem has wide applications in practice, such as setting up new distribution centers for online sales business and building additional base stations for mobile subscribers. We formulate the problem as location selection query for utility maximization. After applying three pruning rules to a baseline solution,we obtain an efficient algorithm to answer the query. Extensive experiments confirm the efficiency of our proposed algorithm.