In this paper, we propose a novel high-dimensional index method, the BM+-tree, to support efficient processing of similarity search queries in high-dimensional spaces. The main idea of the proposed index is to improve data partitioning efficiency in a high-dimensional space by using a rotary binary hyperplane, which further partitions a subspace and can also take advantage of the twin node concept used in the M+-tree. Compared with the key dimension concept in the M+-tree, the binary hyperplane is more effective in data filtering. High space utilization is achieved by dynamically performing data reallocation between twin nodes. In addition, a post processing step is used after index building to ensure effective filtration. Experimental results using two types of real data sets illustrate a significantly improved filtering efficiency. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Zhou, X., Wang, G., Zhou, X., & Yu, G. (2005). BM+-tree: A hyperplane-based index method for high-dimensional metric spaces. In Lecture Notes in Computer Science (Vol. 3453, pp. 398–409). Springer Verlag. https://doi.org/10.1007/11408079_36
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