Abstract
Reverse nearest neighbor queries frequently occur in several practical situations such as marketing-based profiles, decision making, resource management, image processing, and GIS. A spatial indexing method that considers spatial relationships is required to efficiently process the reverse nearest neighbor queries. In this study, a new index structure, R*nn-tree, which optimizes the processing cost of the reverse nearest neighbor queries is designed and implemented. R*nn-tree constructs an index by employing computation algorithms that use the degree of spatial relationships between the object and subspace. R*nn-treeoutperforms the existing methods regarding various aspects in both static and dynamic scenarios.
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