On K-means clustering-based approach for DDBSs design

Abstract

In Distributed Database Systems (DDBS), communication costs and response time have long been open-ended challenges. Nevertheless, when DDBS is carefully designed, the desired reduction in communication costs will be achieved. Data fragmentation (data clustering) and data allocation are on popularity as the prime strategies in constant use to design DDBS. Based on these strategies, on the other hand, several design techniques have been presented in the literature to improve DDBS performance using either empirical results or data statistics, making most of them imperfect or invalid particularly, at least, at the initial stage of DDBSs design. In this paper, thus, a heuristic k-means approach for vertical fragmentation and allocation is introduced. This approach is primarily focused on DDBS design at the initial stage. Many techniques are being joined in a step to make a promising work. A brief yet effective experimental study, on both artificially-created and real datasets, has been conducted to demonstrate the optimality of the proposed approach, comparing with its counterparts, as the obtained results has been shown encouraging.