The Buddy-Tree: An Efficient and Robust Access Method for Spatial Data Base Systems

The principles of the buddy-tree, the organization of its directory and the most important algorithms are introduced, and a performance comparison of the Buddy-tree with other access methods demonstrating the superiority and robustness of the friend-tree is presented.

Abstract

In this paper, we propose a new multidimensional access method, called the buddy-tree, to support point as well as spatial data in a dynamic environment. The buddy-tree can be seen as a compromise of the R-tree and the grid file, but it is fundamentally different from each of them. Because grid files loose performance for highly correlated data, the buddy-tree is designed to organize such data very efficiently, partitioning only such parts of the data space which contain data and not partitioning empty data space. The directory consists of a very flexible partitioning and reorganization scheme based on a generalization of the buddy-system. As for B-trees, the buddy-tree fulfills the property that insertions and deletions are restricted to exactly one path of the directory. Additional important properties which are not fulfilled in this combination by any other multidimensional tree-based access method are: (i) the directory grows linear in the number of records, (ii) no overflow pages are allowed, (iii) the data space is partitioned into minimum bounding rectangles of the actual data and (iv) the performance is basicly independent of the sequence of insertions. In this paper, we introduce the principles of the buddy-tree, the organization of its directory and the most important algorithms. Using our standardized testbed, we present a performance comparison of the buddy-tree with other access methods demonstrating the superiority and robustness of the buddy-tree.