Comparative Analysis of Nearest Neighbor Query Processing Techniques

Abstract The goal of Nearest Neighbour (NN) search is to find the objects in a dataset A that are closest to a query point q. Existing algorithms presume that the dataset is indexed by an R-tree and searching a query point q in a large volume of a dataset, is a tedious task that effects the quality and usefulness of the NNQ processing algorithms which determined by the time as well as space complexity. The simplest solution to the NNS problem is to compute the distance from the query point to every other point in the database. However, due to these complexities issue, the various research techniques have been proposed. It is a technique which has applications in various fields such as pattern recognition, moving object recognition etc. In this paper, a comprehensive analysis on data structures, processing techniques and variety of algorithms in this field is done along with different way to categorize the NNS techniques is presented. This different category such as weighted, additive, reductional, continuous, reverse, principal axis, which are analyzed and compared in this paper. Complexity of different data structures used in different NNS algorithms is also discussed.

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