Extending range queries and nearest neighbors

摘要

Given an initial rectangular range or k nearest neighbor (k-nn) query (using the L_∞ metric), we consider the problems of incrementally extending the query by increasing the size of the range, or by increasing k , and reporting the new points incorporated by each extension. Although both problems may be solved trivially by repeatedly applying a traditional range query or L_∞ k-nn algorithm, such solutions do not minimize the overall time to process all extensions. We present algorithms that obtain efficient overall query times by performing novel searches of multiple range trees and extending k-nn trees, a new data structure introduced here. In two dimensions, when queries eventually incorporate Θ (N) points or require E =Ω (N) extensions, the overall retrieval time of our algorithms is O(E + N), which is optimal. Our extending L_∞ k-nn algorithm immediately provides a new solution to the traditional L_∞ k-nn problem, improving upon previous results. Our search techniques and data structures generalize to algorithms for extending fixed polytope range queries and extending k-nn using polytope distance functions. In two dimensions, under the same conditions as above, these algorithms also have optimal overall extension times.