Titlebook: Advances in Spatial Databases; 5th International Sy Michel Scholl,Agnès Voisard Conference proceedings 1997 Springer-Verlag Berlin Heidelbe

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Self-Help-Group

New linear node splitting algorithm for R-trees,d in most implementations of .-tree, it is more superior in terms of the time required to split a node, the distribution of data after splitting, as well as the area of overlapping. Most important of all, it has a better query performance. The claim is substantiated by an analysis of the algorithm a

小木槌

The S-tree: An efficient index for multidimensional objects,h as cartography, satellite and medical images. The .-tree discussed in this paper is similar in flavor to the standard .-tree, but accepts mild imbalance in the resulting tree in return for significantly reduced area, overlap and perimeter in the resulting minimum bounding rectangles. In fact, the

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0302-9743 in July 1997..The 18 revised full papers presented were carefully selected from a total of 55 submissions. Also included are two keynote contributions. The papers are organized in topical sections on spatial similarities, geo-algorithms, spatial constraint databases, spatial query processing, system

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Spatial data mining: A database approach, KDD algorithms based on the proposed operations. Furthermore, the efficient support of operations on large neighborhood graphs and on large sets of neighborhood paths by the SDBS is discussed. Neighborhood indices are introduced to materialize selected neighborhood graphs in order to speed up the processing of the proposed operations.

Neolithic

nerve-sparing

Interpretable Intuitive Physics Modelregions. We present an algorithm to efficiently perform ellipsoid queries on the class of spatial index structures that manage their directory by rectilinear hyperrectangles, such as R-trees or X-trees. Our experiments show both, effectiveness as well as efficiency of our method using a sample application from molecular biology.

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Zheng Hui,Xiumei Wang,Lirui Deng,Xinbo Gaoygon intersection during the refinement step by more than 50%. Most state-of-the-art methods in spatial join processing can benefit from SID‘s performance gains because the SID approach integrates easily into the established two-phase spatial join process.