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
Approximation algorithms for NP-hard problems
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
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
Efficient method for maximizing bichromatic reverse nearest neighbor
Proceedings of the VLDB Endowment
Boosting spatial pruning: on optimal pruning of MBRs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
FAST: fast architecture sensitive tree search on modern CPUs and GPUs
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
SSTD'05 Proceedings of the 9th international conference on Advances in Spatial and Temporal Databases
Finding top k most influential spatial facilities over uncertain objects
Proceedings of the 21st ACM international conference on Information and knowledge management
Solving the k-influence region problem with the GPU
Information Sciences: an International Journal
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Given a set S of servers and a set C of clients, an optimal-location query returns a location where a new server can attract the greatest number of clients. Optimal-location queries are important in a lot of real-life applications, such as mobile service planning or resource distribution in an area. Previous studies assume that a client always visits its nearest server, which is too strict to be true in reality. In this paper, we relax this assumption and propose a new model to tackle this problem. We further generalize the problem to finding top-k optimal locations. The main challenge is that, even the fastest approach in existing studies needs to take hours to answer an optimal-location query on a typical real world dataset, which significantly limits the applications of the query. Using our relaxed model, we design an efficient grid-based approximation algorithm called FILM (Fast Influential Location Miner) to the queries, which is orders of magnitude faster than the best-known previous work and the number of clients attracted by a new server in the result location often exceeds 98% of the optimal. The algorithm is extended to finding k influential locations. Extensive experiments are conducted to show the efficiency and effectiveness of FILM on both real and synthetic datasets.