Compressing (Multidimensional) Learned Bloom Filters
This work addresses memory efficiency for learned Bloom filters, which is an incremental improvement in data structure optimization.
The paper tackles the high memory consumption of learned Bloom filters by introducing a lossless input compression technique, achieving significant memory improvements while preserving comparable model accuracy.
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.