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Learned FBF: Learning-Based Functional Bloom Filter for Key-Value Storage
- Title
- Learned FBF: Learning-Based Functional Bloom Filter for Key-Value Storage
- Authors
- Byun, Hayoung; Lim, Hyesook
- Ewha Authors
- 임혜숙
- SCOPUS Author ID
- 임혜숙
- Issue Date
- 2021
- Journal Title
- IEEE TRANSACTIONS ON COMPUTERS
- ISSN
- 0018-9340
1557-9956
- Citation
- IEEE TRANSACTIONS ON COMPUTERS vol. 71, no. 8, pp. 1928 - 1938
- Keywords
- Data structures; Data models; Programming; Memory management; Indexes; Task analysis; Neural networks; Key-value storage; functional Bloom filter; deep learning; search failure
- Publisher
- IEEE COMPUTER SOC
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- As a challenging attempt to replace a traditional data structure with a learned model, this paper proposes a learned functional Bloom filter (L-FBF) for a key-value storage. The learned model in the proposed L-FBF learns the characteristics and the distribution of given data and classifies each input. It is shown through theoretical analysis that the L-FBF provides a lower search failure rate than a single FBF in the same memory size, while providing the same semantic guarantees. For model training, character-level neural networks are used with pretrained embeddings. In experiments, four types of different character-level neural networks are trained: a single gated recurrent unit (GRU), two GRUs, a single long short-term memory (LSTM), and a single one-dimensional convolutional neural network (1D-CNN). Experimental results prove the validity of theoretical results, and show that the L-FBF reduces the search failures by 82.8% to 83.9% when compared with a single FBF under the same amount of memory used.
- DOI
- 10.1109/TC.2021.3112079
- Appears in Collections:
- 공과대학 > 전자전기공학전공 > Journal papers
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