Robust and fast similarity search for moving object trajectories
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
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
Capacity constrained assignment in spatial databases
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Continuous k-Means Monitoring over Moving Objects
IEEE Transactions on Knowledge and Data Engineering
Efficient search for the top-k probable nearest neighbors in uncertain databases
Proceedings of the VLDB Endowment
The V*-Diagram: a query-dependent approach to moving KNN queries
Proceedings of the VLDB Endowment
Spatial Range Querying for Gaussian-Based Imprecise Query Objects
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Instance optimal query processing in spatial networks
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient processing of probabilistic reverse nearest neighbor queries over uncertain data
The VLDB Journal — The International Journal on Very Large Data Bases
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
Efficient processing of top-k spatial preference queries
Proceedings of the VLDB Endowment
Optimal location queries in road network databases
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
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Given a set of spatial objects, our task is to assign all the objects to the minimum number of service sites and to find the regions for building these service sites. Each service site has a coverage region (i.e., an area of service) and a capacity (i.e., a maximum number of objects it can serve, called k-constraint). The service sites can provide service for objects located within the coverage regions. Aiming at this problem, we propose a novel kind of spatial queries, called Optimal κ-Constraint Coverage (OCC) queries. An OCC query returns some feasible regions such that setting up the minimum number of service sites within these regions will guarantee that all the spatial objects can be served. Furthermore, an optimal coverage scheme to assign the objects to these service sites is retrieved by this query as well. Due to the capacity constraints, objects located within the coverage region of a service site may not be assigned to one service site. Therefore, the cost of searching an optimal coverage over all possible coverage schemes becomes prohibitive. To answer OCC queries efficiently, we devise a general query framework, which provides two solutions to cope with OCC query processing. The naive solution only returns a local optimum without insuring the minimum number of service sites. To improve it, the other solution called Optimal Coverage Algorithm (Opt-C) is proposed to retrieve an optimal coverage scheme. During the procedure, we present refinement methods for reducing intermediate results of OCC queries to improve the efficiency. The performance of the proposed methods is demonstrated by the extensive experiments with both synthetic and real datasets.