Paper by Erik D. Demaine

Erik D. Demaine, John Iacono, and Stefan Langerman, “Proximate Point Searching”, Computational Geometry: Theory and Applications, volume 28, number 1, May 2004, pages 29–40. Special issue of selected papers from the 14th Canadian Conference on Computational Geometry, 2002.

In the 2D point searching problem, the goal is to preprocess n points P = {p1, …, pn} in the plane so that, for an online sequence of query points q1, …, qm, it can quickly determined which (if any) of the elements of P are equal to each query point qi. This problem can be solved in O(log n) time by mapping the problem to one dimension. We present a data structure that is optimized for answering queries quickly when they are geometrically close to the previous successful query. Specifically, our data structure executes queries in time O(log d(qi−1qi)), where d is some distance function between two points, and uses O(n log n) space. Our structure works with a variety of distance functions. In contrast, it is proved that, for some of the most intuitive distance functions d, it is impossible to obtain an O(log d(qi−1qi)) runtime, or any bound that is o(log n).

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Related papers:
PointSearching_CCCG2002 (Proximate Point Searching)

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Last updated March 9, 2018 by Erik Demaine.