MIT6_851S10_lec03

MIT6_851S10_lec03 - 6.851 Advanced Data Structures Spring...

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6.851: Advanced Data Structures Spring 2010 Lecture 3 February 9, 2010 Dr. Andr´ e Schulz 1 Overview In the last lecture we continued to study binary search trees from the geometry perspective of arboreally satisfied sets. We gave lower bounds for the runtime of a BST two of them were Wilbur’s lower bounds [Wil89], and the last was “signed greedy.” We also looked at a type of BST called a Tango tree and showed that it was O (lg lg n ) competitive with an optimal dynamic tree. In this lecture we consider the problem of orthogonal range queries, focusing mostly on R 2 but also indicating how to extend the results to higher dimensions. An orthogonal range query is the following problem: given a set S of points in R d , and an axis-aligned box B , what are all elements of S that lie in B ? The goal is to implement a data structure with reasonably efficient preprocessing on the set S that can answer all such queries efficiently. A natural application is to database queries (e.g. how many people lie in a given age range and income range?). Since the output of a query could be very large, we will measure the efficiency of our data structures in terms of both n , the number of points in S , and k , the size of the output of a given query. We start by introducing range trees, which in the two-dimensional case use O ( n lg n ) space and pre- processing time and have a query time of O (lg 2 n + k ). (In d dimensions, they use O ( n lg d 1 n ) storage and pre-processing and have a query time of O (lg d n + k ).) We then describe kd-trees for the 2-dimensional case, which are more storage-efficient ( O ( n ) space), but have a bad worst-case query time O ( n ). We also introduce the idea of fractional cascading, a trick that reduces the query time for range trees from O (lg d n + k ) to O (lg d 1 n + k ). Finally, we indicate how to deal with the case of points with duplicated coordinates (the rest of the exposition assumes that all points have distinct coordinates). A good reference for the material in this lecture is the book Computational Geometry: Algorithms and Applications by deBerg et al. [dBea08]. We begin by describing the problem of orthogonal range queries in detail. We will then look at various data structures for answering orthogonal range queries efficiently. 2 Orthogonal Range Queries The first problem we will approach is orthogonal range searching . Suppose we are given a set of points. For the moment we will assume that these points are in R 2 , but in general we can allow our space to be any dimension. Now draw a rectangle with sides parallel to the coordinate axes (since this is orthogonal range searching, we require a rectangle, although in principle we could allow other shapes). How can we report all of the points that are inside of the rectangle?
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