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A new vertical fragmentation algorithm based on ant collective behavior in distributed database systems

机译:分布式数据库系统中一种基于蚁群行为的垂直分割新算法

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摘要

Considering the existing massive volumes of data processed nowadays and the distributed nature of many organizations, there is no doubt how vital the need is for distributed database systems. In such systems, the response time to a transaction or a query is highly affected by the distribution design of the database system, particularly its methods for fragmentation, replication, and allocation data. According to the relevant literature, from the two approaches to fragmentation, namely horizontal and vertical fragmentation, the latter requires the use of heuristic methods due to it being NP-Hard. Currently, there are a number of different methods of providing vertical fragmentation, which normally introduce a relatively high computational complexity or do not yield optimal results, particularly for large-scale problems. In this paper, because of their distributed and scalable nature, we apply swarm intelligence algorithms to present an algorithm for finding a solution to vertical fragmentation problem, which is optimal in most cases. In our proposed algorithm, the relations are tried to be fragmented in such a way so as not only to make transaction processing at each site as much localized as possible, but also to reduce the costs of operations. Moreover, we report on the experimental results of comparing our algorithm with several other similar algorithms to show that ours outperforms the other algorithms and is able to generate a better solution in terms of the optimality of results and computational complexity.
机译:考虑到当今已处理的海量数据以及许多组织的分布式性质,毫无疑问对分布式数据库系统的需求至关重要。在这样的系统中,对事务或查询的响应时间在很大程度上受数据库系统的分布设计的影响,特别是其用于分段,复制和分配数据的方法。根据相关文献,从碎片化的两种方法,即水平碎片化和垂直碎片化,由于后者是NP-Hard,后者需要使用启发式方法。当前,存在多种不同的提供垂直分段的方法,这些方法通常会引入较高的计算复杂度,或者不会产生最佳结果,尤其是对于大规模问题。在本文中,由于它们的分布式和可伸缩性,我们应用群体智能算法来提出一种算法,该算法可以找到垂直碎片问题的解决方案,这在大多数情况下是最佳的。在我们提出的算法中,尝试以这种方式将关系分解,以使不仅使每个站点上的事务处理尽可能本地化,而且降低了操作成本。此外,我们报告了将我们的算法与其他几种相似算法进行比较的实验结果,以表明我们的算法优于其他算法,并且能够在结果的最优性和计算复杂性方面产生更好的解决方案。

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