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Application and Research on Weighted Bloom Filter and Bloom Filter in Web Cache

机译:Web缓存中加权绘制过滤器和绽放过滤器的应用与研究

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A bloom filter is a simple space-efficient randomized data structure for representing a set in order to support membership queries. Bloom filters and their generalizations, weighted bloom filters and compressed bloom filters have been suggested as a means for sharing Web cache information. In this paper, a summary about the current research and application on bloom filter is first given, and then a comparison of theory and practice between the bloom filter and weighted bloom filter is given. In theory, it was proved that weighted bloom filter has lower false prediction than bloom filter. But the simulation results showed that bloom filter is better than weighted. the reason is that weighted bloom filter needs the necessary conditions, which cannot be satisfied in real world.
机译:Bloom过滤器是一个简单的空节空间随机数据结构,用于表示集合以支持成员资格查询。已经建议绽放过滤器及其概括,加权绽放过滤器和压缩绽放过滤器作为共享Web缓存信息的手段。在本文中,首先给出关于盛开滤波器的当前研究和应用的摘要,然后给出了盛开过滤器和加权盛开滤波器之间的理论和实践的比较。理论上,证明了加权盛开滤波器的假预测比盛开滤波器更低。但仿真结果表明,盛开过滤器优于加权。原因是加权绽放过滤器需要必要的条件,这在现实世界中不能满足。

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