Filter ranking in high-dimensional space
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摘要
High-dimensional index structures are a means to accelerate database query processing in high-dimensional data, like multimedia feature vectors. A particular interest in many application scenarios is to rank data items with respect to a certain distance function and, thus, identifying the nearest neighbor(s) of a query item.In this paper, we propose a novel ranking algorithm that (1) operates on arbitrary high-dimensional filter indexes, like the VA-file, the VA+-file, the LPC-file, or the AV-method. Our ranking algorithm (2) exhibits a nearly balanced I/O load to retrieve subsequent items. Finally, it (3) strictly obeys a predefined main memory threshold and even (4) terminates successfully when memory restrictions are very tight.
论文关键词:High-dimensional indexing,Approximation index,Nearest neighbor search,Storage and access
论文评审过程:Received 14 February 2005, Revised 14 February 2005, Accepted 24 March 2005, Available online 22 April 2005.
论文官网地址:https://doi.org/10.1016/j.datak.2005.03.005