论文标题
压缩（多维）学习的绽放过滤器
Compressing (Multidimensional) Learned Bloom Filters
论文作者
论文摘要
Bloom过滤器是广泛使用的数据结构，可紧凑地表示元素集。查询Bloom滤波器会揭示是否未包含在基础集中的元素，还是包含在一定的错误率中。该会员资格测试可以建模为二进制分类问题，并通过深度学习模型解决，从而导致所谓的Bloom过滤器。我们已经确定，只有在考虑大量数据时，学到的Bloom过滤器的好处才是显而易见的，即使那样，也有可能进一步减少其记忆消耗。因此，我们引入了一种无损输入压缩技术，该技术可以改善学习模型的记忆消耗，同时保留可比的模型精度。我们评估了我们的方法，并显示出对学到的Bloom过滤器的重大记忆消耗。
Bloom filters are widely used data structures that compactly represent sets of elements. Querying a Bloom filter reveals if an element is not included in the underlying set or is included with a certain error rate. This membership testing can be modeled as a binary classification problem and solved through deep learning models, leading to what is called learned Bloom filters. We have identified that the benefits of learned Bloom filters are apparent only when considering a vast amount of data, and even then, there is a possibility to further reduce their memory consumption. For that reason, we introduce a lossless input compression technique that improves the memory consumption of the learned model while preserving a comparable model accuracy. We evaluate our approach and show significant memory consumption improvements over learned Bloom filters.
