In this paper, heuristic query-driven clustering-based vertical fragmentation technique is efficiently developed. The intrinsic idea is to meet the idealistic case of DDBS design which comes to happen as each query attune “closely match” its relevant fragment. The proposed technique is mainly sought to breed clusters of queries in the first place. Consequently, these clusters would be tacitly used to generate intended disjoint fragments. Moreover, the allocation process has been considered so that replicated and non-replicated scenarios of data are applied. This technique basically meant to be efficaciously applicable at the initial stage of DDBS design without the need for data statistics or empirical results, in either dynamic or static DDBS environment. Many existing design-related techniques are being incorporated to make a promising work, particularly as communication costs being the foremost design objective. Throughout this work, the experimental results and internal evaluation are extensively illustrated to demonstrate the effectiveness and validity of proposed technique.

No abstract

Multi-Objective Problems (MOPs) are common real-life problems that can be found in different fields, such as bioinformatics and scheduling. Pareto Optimization (PO) is a popular method for solving MOPs, which optimizes all objectives simultaneously. It provides an effective way to evaluate the quality of multi-objective solutions. Swarm Intelligence (SI) methods are population-based methods that generate multiple solutions to the problem, providing SI methods suitable for MOP solutions. SI methods have certain drawbacks when applied to MOPs, such as swarm leader selection and obtaining evenly distributed solutions over solution space. Whale Optimization Algorithm (WOA) is a recent SI method. In this paper, we propose combining WOA with Tabu Search (TS) for MOPs (MOWOATS). MOWOATS uses TS to store non-dominated solutions in elite lists to guide swarm members, which overcomes the swarm leader selection problem. MOWOATS employs crossover in both intensification and diversification phases to improve diversity of the population. MOWOATS proposes a new diversification step to eliminate the need for local search methods. MOWOATS has been tested over different benchmark multi-objective test functions, such as CEC2009, ZDT, and DTLZ. Results present the efficiency of MOWOATS in finding solutions near Pareto front and evenly distributed over solution space.

No abstract

With the substantial ever-upgrading advancement in data and information management field, Distributed Database System (DDBS) is still proven to be the most growingly-demanded tool to handle the accompanied constantly-piled volumes of data. However, the efficiency and adequacy of DDBS is profoundly correlated with the reliability and precision of the process in which DDBS is set to be designed. As for DDBS design, thus, several strategies have been developed, in literature, to be used in purpose of promoting DDBS performance. Off these strategies, data fragmentation, data allocation and replication, and sites clustering are the most immensely-used efficacious techniques that otherwise DDBS design and rendering would be prohibitively expensive. On one hand, an accurate well-architected data fragmentation and allocation is bound to incredibly increase data locality and promote the overall DDBS throughputs. On the other hand, finding a practical sites clustering process is set to contribute remarkably in reducing the overall Transmission Costs (TC). Consequently, consolidating all these strategies into one single work is going to undoubtedly satisfy a massive growth in DDBS influence. In this paper, therefore, an optimized heuristic horizontal fragmentation and allocation approach is meticulously developed. All the drawn-above strategies are elegantly combined into a single effective approach so as to an influential solution for DDBS productivity promotion is set to be markedly fulfilled. Most importantly, an internal and external evaluations are extensively illustrated. Obviously, findings of conducted experiments have maximally been recorded to be in favor of DDBS performance betterment.