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Fragmentation in Distributed Database Design Based on Ant Colony Optimization Technique

机译:基于蚁群优化技术的分布式数据库设计中的碎片化

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摘要

Distributed database design solutions depend heavily on the exploitation of input data sources by using clustering techniques in data mining. A new approach of biomimetic computation systems such as ant colony optimization (ACO) for this solution is of interest to informatics experts. Using ACO techniques for this solution has the advantages such as faster algorithms thanks to the randomness of ant colony behavior. The use of random numbers based on heuristic information to pickup (drop) points will facilitate the flexible search on a large data space, so that it provides us with a better answer. In this article, the authors present ACO algorithms application solutions to clustering techniques for the problem of vertical fragmentation of distributed data.
机译:分布式数据库设计解决方案在很大程度上依赖于在数据挖掘中使用群集技术来利用输入数据源。仿生计算系统的一种新方法,例如针对此解决方案的蚁群优化(ACO),是信息学专家感兴趣的。由于蚁群行为的随机性,将ACO技术用于此解决方案具有诸如更快算法的优势。使用基于启发式信息的随机数来拾取(丢失)点将有助于在大型数据空间上进行灵活的搜索,从而为我们提供了更好的答案。在本文中,作者介绍了ACO算法对聚类技术的应用解决方案,以解决分布式数据的垂直碎片问题。

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