Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier

Zhenwei Dai, Anshumali Shrivastava

Keywords: Ada-BF, Bloom filter, machine learning, memory efficient

TL;DR: Propose an efficient algorithm to improve the Bloom filter by incorporating the machine learning model in a clever way

Abstract: Recent work suggests improving the performance of Bloom filter by incorporating a machine learning model as a binary classifier. However, such learned Bloom filter does not take full advantage of the predicted probability scores. We proposed new algorithms that generalize the learned Bloom filter by using the complete spectrum of the scores regions. We proved our algorithms have lower False Positive Rate (FPR) and memory usage compared with the existing approaches to learned Bloom filter. We also demonstrated the improved performance of our algorithms on real-world datasets.