A parallel cell-based filtering scheme for indexing high-dimensional vector

Many high-dimensional index structures have been proposed, but they suffer from the so called 'dimensional curse' problem, i.e., the retrieval performance becomes increasingly degraded as the dimensionality is increased. To solve this problem, the cell-based filtering (CBF) scheme has been proposed, but it shows a linear decrease in performance as the dimensionality is increased. In this paper, we propose a parallel CBF scheme for indexing high-dimensional vector data, so as to cope with the linear decrease in retrieval performance. In addition, we devise data insertion, range query and k-NN query processing algorithms which are suitable for a parallel architecture. Finally, we show that our parallel CBF scheme achieves good retrieval performance in proportion to the number of servers in the parallel architecture and it outperforms a parallel version of the VA- File when the dimensionality is over 10.