一种学习型Bloom过滤器模型及其三明治优化
A Model for Learned Bloom Filters, and Optimizing by Sandwiching
摘要：
最近的工作建议通过使用预过滤器来增强Bloom过滤器，该预过滤器基于应用机器学习来确定对Bloom过滤器要表示的数据集进行建模的函数。在这里，我们对这种学习的Bloom过滤器进行建模，具有以下结果：（1）我们阐明了什么保证可以与这种结构相关联，什么保证不可以与这种结构相关联;（2）我们展示了如何估计学习函数必须获得多大的大小，以获得更好的性能;（3）我们提供了一种简单的方法，优化学习的Bloom过滤器;（4）基于我们的建模方法，提出了一种学习型Bloomier滤波器的设计和分析方法。
英文摘要：
Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters,, with the following outcomes: (1) we clarify what guarantees can and cannot be associated with such a structure; (2) we show how to estimate what size the learning function must obtain in order to obtain improved performance; (3) we provide a simple method, sandwiching, for optimizing learned Bloom filters; and (4) we propose a design and analysis approach for a learned Bloomier filter, based on our modeling approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
