在移动云计算基础设施中计算近似查询的成本效益资源
Arun Kumar Sangaiah1,2, Amir Javadpour3,4, Pedro Pinto4
1International Graduate School of Artificial Intelligence, National Yunlin University of Science and Technology, Douliou 64002, Taiwan.
本研究引入了一个智能系统,用于在点对点数据库中进行近似设置值查询,使用没有采样的粒子优化. 与传统方法相比,这种新方法显著提高了查询效率,并减少了执行时间.
科学领域:
- 计算机科学 计算机科学
- 数据库系统 数据库系统
- 人工智能的人工智能
背景情况:
- 同行数据库 (P2P) 由于高成本和时间,在查询和答复方面面临挑战.
- 接近查询通常用于解决这些限制.
- 现有的方法,如洪水和采样算法,在准确性和效率方面存在缺点.
研究的目的:
- 开发一个智能系统,用于P2P数据库中的近似设置值查询.
- 通过粒子优化来增强系统智能.
- 为了避免采样方法及其固有的不准确性.
主要方法:
- 探索粒子优化,以提高P2P数据库查询智能.
- 开发一种新的方法,绕过抽样的需要.
- 对现有方法进行评估,包括洪水和并行扩散算法.
主要成果:
- 拟议的方法在发出查询和对同行进行审查的数量方面取得了显著的改进.
- 与洪水方法相比,实现了明显更快的执行时间.
- 在查询传输中展示了卓越的成本效益.
结论:
- 基于粒子优化的系统有效地处理P2P数据库中的近似设值查询.
- 避免采样可以提高查询处理的准确性和效率.
- 开发的方法为P2P查询答复提供了更有效和更具成本效益的解决方案.
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