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A Model for Learned Bloom Filters and Optimizing by Sandwiching
Michael Mitzenmacher

Thu Dec 06 07:45 AM -- 09:45 AM (PST) @ Room 517 AB #169

Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters, with the following outcomes: (1) we clarify what guarantees can and cannot be associated with such a structure; (2) we show how to estimate what size the learning function must obtain in order to obtain improved performance; (3) we provide a simple method, sandwiching, for optimizing learned Bloom filters; and (4) we propose a design and analysis approach for a learned Bloomier filter, based on our modeling approach.

Author Information

Michael Mitzenmacher (Harvard University)

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