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We present a number of new results on one of the most extensively studied topics in computational geometry, orthogonal range searching. All our results are in the standard word RAM model: We present two data structures for 2-d orthogonal range emptiness. The first achieves O(n lg lg n) space and O(lg lg n) query time, assuming that the n given points are in(More)
<i>Range selection</i> is the problem of preprocessing an input array <i>A</i> of <i>n</i> unique integers, such that given a query (<i>i, j, k</i>), one can report the <i>k</i>'th smallest integer in the subarray <i>A</i>[<i>i</i>], <i>A</i>[<i>i</i> + 1],..., <i>A</i>[<i>j</i>]. In this paper we consider static data structures in the word-RAM for range(More)
We consider NCA labeling schemes: given a rooted tree T , label the nodes of T with binary strings such that, given the labels of any two nodes, one can determine, by looking only at the labels, the label of their nearest common ancestor. For trees with n nodes we present upper and lower bounds establishing that labels of size (2 ± ǫ) log n, ǫ < 1 are both(More)
In this paper, we study the cell probe complexity of evaluating an n-degree polynomial P over a finite field F of size at least n<sup>1+&#x03A9;(1)</sup>. More specifically, we show that any static data structure for evaluating P(x), where x &#x2208; F, must use &#x03A9;(lg |F|/ lg(Sw/n lg |F|)) cell probes to answer a query, where S denotes the space of(More)
Given a set of points with uncertain locations, we consider the problem of computing the probability of each point lying on the skyline, that is, the probability that it is not dominated by any other input point. If each point's uncertainty is described as a probability distribution over a discrete set of locations, we improve the best known exact solution.(More)
Orthogonal range reporting is the problem of storing a set of <i>n</i> points in <i>d</i>-dimensional space, such that the <i>k</i> points in an axis-orthogonal query box can be reported efficiently. While the 2-d version of the problem was completely characterized in the pointer machine model more than two decades ago, this is not the case in higher(More)
In this paper, we consider two fundamental problems in the pointer machine model of computation, namely orthogonal range reporting and rectangle stabbing. Orthogonal range reporting is the problem of storing a set of <i>n</i> points in d-dimensional space in a data structure, such that the t points in an axis-aligned query rectangle can be reported(More)
Range reporting on categorical (or colored) data is a well-studied generalization of the classical range reporting problem in which each of the N input points has an associated color (category). A query then asks to report the set of colors of the points in a given rectangular query range, which may be far smaller than the set of all points in the query(More)
Let D = {d1, d2, ..., dD} be a collection of D string documents of n characters in total. The two-pattern matching problems ask to index D for answering the following queries efficiently. – report/count the unique documents containing P1 and P2. – report/count the unique documents containing P1, but not P2. Here P1 and P2 represent input patterns of length(More)