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Abstract

Let $S$ be a set of $n$ points in $\IR^{D}$. It is shown that a range tree can be used to find an $L_{\infty}$-nearest neighbor in $S$ of any query point, in $O((\log n)^{D-1} \log\log n)$ time. This data structure has size $O(n (\log n)^{D-1})$ and an amortized update time of $O((\log n)^{D-1} \log\log n)$. This result is used to solve the $(1+\epsilon)$-approximate $L_{2}$-nearest neighbor problem within the same bounds. In this problem, for any query point $p$, a point $q \in S$ is computed such that the euclidean distance between $p$ and $q$ is at most $(1+\epsilon)$ times the euclidean distance between $p$ and its true nearest neighbor. This is the first dynamic data structure for this problem having close to linear size and polylogarithmic query and update times.