An Aggregated Similarity Based Hierarchical Clustering Technique for Relational DDBS Design

In this work, as part of our continuous effort, an optimized heuristic technique is proposed. The basic aim of this technique to fragment data vertically in Distributed Database System (DDBS). Drive by the queries along with using hierarchical clustering algorithm, the proposed technique seeks to propose an aggregated similarity measure to properly perform the clustering-based vertical data fragmentation. Data replication and allocation are also investigated to produce a comprehensive solution. As a matter of fact, the key concern is to find an effective best-fitting solution for improving DDBS throughput through developing an aggregated similarity based data fragmentation process, drawing a site clustering algorithm, and presenting a greedy-based transmission costs-reducing data allocation algorithm. Moreover, data replication is carefully considered in such a way that cost is to be essentially minimized.