Filter ranking in high-dimensional space

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.