Optimizing Learned Bloom Filters: How Much Should Be Learned?

نویسندگان

چکیده

The learned Bloom filter (LBF) combines a machine learning model (learner) with traditional to improve the false positive rate (FPR) that can be achieved for given memory budget. LBF has recently been generalized by making use of full spectrum learner’s prediction score. However, in all those designs, is fixed. In this letter, first time, design LBFs proposed and evaluated considering as one variables process. detail, budget, several are constructed using different models lowest FPR selected. We demonstrate our approach achieve much better performance than existing designs providing reductions up 90% some settings.

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ژورنال

عنوان ژورنال: IEEE Embedded Systems Letters

سال: 2022

ISSN: ['1943-0671', '1943-0663']

DOI: https://doi.org/10.1109/les.2022.3156019