Developing parallel cell-based filtering scheme under shared-nothing cluster-based architecture

A large number of high-dimensional index structures 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 develop a parallel CBF scheme under an SN(Shared Nothing) cluster-based parallel architecture, 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 the SN parallel architecture. Finally, we show that our parallel CBF scheme achieves good retrieval performance in proportion to the number of servers in the SN architecture and it outperforms a parallel version of the VA-File when the dimensionality is over 10.