A scalable algorithm for maximizing range sum in spatial databases
Proceedings of the VLDB Endowment
Continuous maximal reverse nearest neighbor query on spatial networks
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
Spatial query processing in road networks for wireless data broadcast
Wireless Networks
Optimal k-constraint coverage queries on spatial objects
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
A branch and bound method for min-dist location selection queries
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
Reverse top-k group nearest neighbor search
WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
Trajectory based optimal segment computation in road network databases
Proceedings of the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Approximate MaxRS in spatial databases
Proceedings of the VLDB Endowment
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Optimal location (OL) queries are a type of spatial queries particularly useful for the strategic planning of resources. Given a set of existing facilities and a set of clients, an OL query asks for a location to build a new facility that optimizes a certain cost metric (defined based on the distances between the clients and the facilities). Several techniques have been proposed to address OL queries, assuming that all clients and facilities reside in an Lp space. In practice, however, movements between spatial locations are usually confined by the underlying road network, and hence, the actual distance between two locations can differ significantly from their Lp distance. Motivated by the deficiency of the existing techniques, this paper presents the first study on OL queries in road networks. We propose a unified framework that addresses three variants of OL queries that find important applications in practice, and we instantiate the framework with several novel query processing algorithms. We demonstrate the efficiency of our solutions through extensive experiments with real data.