Top-k most influential locations selection
Proceedings of the 20th ACM international conference on Information and knowledge management
Finding the most accessible locations: reverse path nearest neighbor query in road networks
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Continuous reverse k nearest neighbors queries in Euclidean space and in spatial networks
The VLDB Journal — The International Journal on Very Large Data Bases
Efficiently processing snapshot and continuous reverse k nearest neighbors queries
The VLDB Journal — The International Journal on Very Large Data Bases
Monochromatic and bichromatic reverse nearest neighbor queries on land surfaces
Proceedings of the 21st ACM international conference on Information and knowledge management
Loyalty-based selection: retrieving objects that persistently satisfy criteria
Proceedings of the 21st ACM international conference on Information and knowledge management
Continuous maximal reverse nearest neighbor query on spatial networks
Proceedings of the 20th International Conference on Advances in Geographic Information Systems
A safe zone based approach for monitoring moving skyline queries
Proceedings of the 16th International Conference on Extending Database Technology
A branch and bound method for min-dist location selection queries
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
UV-diagram: a voronoi diagram for uncertain spatial databases
The VLDB Journal — The International Journal on Very Large Data Bases
Reverse-k-Nearest-Neighbor join processing
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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Given a set of objects and a query q, a point p is called the reverse k nearest neighbor (RkNN) of q if q is one of the k closest objects of p. In this paper, we introduce the concept of influence zone which is the area such that every point inside this area is the RkNN of q and every point outside this area is not the RkNN. The influence zone has several applications in location based services, marketing and decision support systems. It can also be used to efficiently process RkNN queries. First, we present efficient algorithm to compute the influence zone. Then, based on the influence zone, we present efficient algorithms to process RkNN queries that significantly outperform existing best known techniques for both the snapshot and continuous RkNN queries. We also present a detailed theoretical analysis to analyse the area of the influence zone and IO costs of our RkNN processing algorithms. Our experiments demonstrate the accuracy of our theoretical analysis.