A parallel model for multimedia database on cluster system environment

The authors propose a parallel model for content-based retrieval from a multimedia database using a heterogeneous cluster system. The proposed parallel unified model was used to represent multimedia data. All types of multimedia data in the unified model are represented by k-dimensional signals. Each dimension of k-d data is separated into small blocks and then formed into a hierarchical multidimensional tree structure, called a k-tree. Previous experimental results show the huge reduction of retrieval time on a cluster of homogeneous workstations. In this paper, the authors extend their parallel model to a heterogeneous cluster system environment. They demonstrate the experimental results of using a parallel retrieval algorithm for the k-tree unified model on a cluster of heterogeneous systems connected via a network. They use system characteristics to balance the loads of the processors. The experiments of the model with load balancing show a significant reduction in retrieval time while maintaining the quality of perceptual results.