BM+-Tree: a hyperplane-based index method for high-dimensional metric spaces

Authors:
Xiangmin Zhou;Guoren Wang;Xiaofang Zhou;Ge Yu
Affiliations:
College of Information Science and Engineering, Northeastern University, China;College of Information Science and Engineering, Northeastern University, China;School of Information Technology & Electrical Engineering, University of Queensland, Australia;College of Information Science and Engineering, Northeastern University, China
Venue:
DASFAA'05 Proceedings of the 10th international conference on Database Systems for Advanced Applications
Year:
2005

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Abstract

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.