We address the problem of replicating a Voronoi diagram @math of a planar point set @math by making proximity queries, which are of three possible (in decreasing order of information content): 1. the exact location of the nearest site(s) in @math ; 2. the distance to and label(s) of the nearest site(s) in @math ; 3. a unique label for every nearest site in @math . We provide algorithms showing how queries of Type 1 and Type 2 allow an exact cloning of @math with @math queries and @math processing time. We also prove that queries of Type 3 can never exactly clone @math , but we show that with @math queries we can construct an @math -approximate cloning of @math . In addition to showing the limits of nearest-neighbor database security, our methods also provide one of the first natural algorithmic applications of retroactive data structures.
By a well-known lifting method (e.g., see @cite_40 ), a 2-dimensional Voronoi diagram can be defined by a projection to the plane of the skeleton of a 3-dimensional convex polyhedron, which is defined by the upper envelope of planes tangent at point sites mapped vertically to a certain paraboloid. This property of Voronoi diagrams implies that the method of Dobkin @cite_12 for discovering a convex polytope via ray-shooting finger'' probes can be used to discover a Voronoi diagram in the plane using nearest-neighbor queries (for instance, the queries we call exact queries''). Translated into this context, their method results in a scheme that would use @math queries to clone a Voronoi diagram, with a time overhead that is @math .
@cite_40 @cite_12
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We address the problem of replicating a Voronoi diagram @math of a planar point set @math by making proximity queries, which are of three possible (in decreasing order of information content): 1. the exact location of the nearest site(s) in @math ; 2. the distance to and label(s) of the nearest site(s) in @math ; 3. a unique label for every nearest site in @math . We provide algorithms showing how queries of Type 1 and Type 2 allow an exact cloning of @math with @math queries and @math processing time. We also prove that queries of Type 3 can never exactly clone @math , but we show that with @math queries we can construct an @math -approximate cloning of @math . In addition to showing the limits of nearest-neighbor database security, our methods also provide one of the first natural algorithmic applications of retroactive data structures. The problem of construction of planar Voronoi diagrams arises in many areas, one of the most important of which is in nearest neighbor problems. This includes clustering [ 141, contour maps [6] and (Euclidean) minimum spanning trees [23]. Shamos [22] gives several more applications. An JZ(N log N) time worst case lower bound can be shown for this problem by reducing it to sorting [2 11. The challenge is to construct an O(N log N) time algorithm. Shamos [213 and Shamos anti Hoey [23] describe an O(N log N) time divide-and-conquer algorithm for construction of the planar Euclidean Voronoi diagram. Lee and Wong [ 161 describe an O(N log N) time algorithm for the L1 and L, metrics in the plane, and Drysdale pnd Lee [8] present an O(N@g N)l *) t’ rme algorithm for the Voronoi diagram of N line segments (which they have since improved to O(N(log N)*) time). Shamos [2 11, Lee and Preparata [ 151, and Lipton and Tarjan [ 171 have produced fast algorithms for searching a Voronoi diagram (or any other straight-line planar graph). In this paper we describe an O(N log N) time algorithm for constructing a planar Euclidean Voronoi diagram which extends straightforwardly to higher dimensions. The fundamental result is that a K-dimensional Euclidean Voronoi diagram of N points can be constructed by transforming the points to K + I-space, The Voronoi diagram is a famous structure of computational geometry. We show that there is a straightforward equivalent in graph theory which can be efficiently computed. In particular, we give two algorithms for the computation of graph Voronoi diagrams, prove a lower bound on the problem, and identify cases where the algorithms presented are optimal. The space requirement of a graph Voronoi diagram is modest, since it needs no more space than does the graph itself. The investigation of graph Voronoi diagrams is motivated by many applications and problems on networks that can be easily solved with their help. This includes the computation of nearest facilities, all nearest neighbors and closest pairs, some kind of collision free moving, and anticenters and closest points. © 2000 John Wiley & Sons, Inc. The Voronoi diagram is an important technique for answering nearest-neighbor queries for spatial databases. In this paper, we study how the Voronoi diagram can be used on uncertain data, which are inherent in scientific and business applications. In particular, we propose the Uncertain-Voronoi Diagram (or UV-diagram in short). Conceptually, the data space is divided into distinct “UV-partitions”, where each UV-partition P is associated with a set S of objects; any point q located in P has the set S as its nearest neighbor with non-zero probabilities. The UV-diagram facilitates queries that inquire objects for having non-zero chances of being the nearest neighbor of a given query point. It also allows analysis of nearest neighbor information, e.g., finding out how many objects are the nearest neighbors in a given area. However, a UV-diagram requires exponential construction and storage costs. To tackle these problems, we devise an alternative representation for UV-partitions, and develop an adaptive index for the UV-diagram. This index can be constructed in polynomial time. We examine how it can be extended to support other related queries. We also perform extensive experiments to validate the effectiveness of our approach.