Abstract
Nearest neighbor search is one of the most widely-u sed techniques and its applications including mobil e communication, Geographic information systems, bioinformatics, computer vision and marketing. For example, four friends want to rent an apartment whi ch should be nearer to their working places. Our paper discussed about the problems on finding the m ost appropriate location among a set of available places. The problem is defined as a top-k query whi ch gives output of k points from a set of available places P along with the conveniences. We proposed a lgorithms based on R-trees to answer the query exactly. The efficiency of our proposed algorithms is verified through various experiments and found that it is better than existing algorithms use larg e scale real datasets.
Highlights
Nowadays Optimal locations problems got the great focus on research (Corral et al, 2004; Wong et al, 2009)
Our paper discussed about the problems on finding the most appropriate location among a set of available places
We proposed algorithms based on R-trees to answer the query exactly
Summary
Nowadays Optimal locations problems got the great focus on research (Corral et al, 2004; Wong et al, 2009). Already many algorithms have been proposed for all nearest neighbors problem (Chen and Patel, 2007; Emrich et al, 2010) and aggregate nearest neighbor problem (Li et al, 2005; Mouratidis et al, 2005; Papadias et al, 2004) In these papers, only one facility has been taken into account but ours will work out with multiple facilities with certain preferences. The second algorithm ranking the facilities in a R-tree and finds out the most suitable settings. Both algorithms are experimented carefully with many nontrivial optimizations results. We experimented our algorithm with real datasets and results show better performance than the existing algorithm
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