Parallel high-dimensional index structure for content-based information retrieval

To solve ‘dimensional curse’ problem, the cell-based filtering scheme has been proposed, but it shows a linear decrease in performance as the dimensionality is increased. In this paper, we propose a parallel high-dimensional index structure for content-based information retrieval 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 cluster-based parallel architecture. Finally, we show that our parallel index structure achieves good retrieval performance in proportion to the number of servers in the cluster-based architecture and it outperforms a parallel version of the VA-File when the dimensionality is over 10.