L-rCBF: Learning-Based Key–Value Data Structure for Dynamic Data Processing

Abstract

Learning-based data structures, such as a learned Bloom filter and a learned functional Bloom filter (L-FBF), have recently been proposed to replace traditional structures. However, using these structures for dynamic data processing is difficult because a specific element cannot be deleted from a trained model. A counting Bloom filter with return values (rCBF) is a more efficient key–value structure than a functional Bloom filter (FBF) for repetitive insertions and deletions. In this study, we propose a learned rCBF (L-rCBF) comprising a model, a Bloom filter, and an rCBF and the deletion algorithm for the L-rCBF. To delete a specific element from the L-rCBF, two different operations are performed according to four different cases. In the experiments, the proposed L-rCBF is compared with a single rCBF and an L-FBF in terms of undeletables and search failures, and this comparison is conducted using two different models. In addition, we present a theoretical analysis of the rCBF with experimental results to demonstrate that a structure with an rCBF is more suitable for dynamic data than a structure with an FBF.

Keywords

• key–value data structure
• Bloom filter
• learning-based structure
• dynamic data
• deep learning