Bloom Filters, Cuckoo Hashing, Cuckoo Filters, Adaptive Cuckoo Filters, and Learned Bloom Filters

Presented on November 26, 2018 at 12:15 p.m. in the Marcus Nanotechnology Building, Room 1116.Michael D. Mitzenmacher is the Thomas J. Watson, Sr. Professor of Computer Science at Harvard University. Professor Mitzenmacher's research focuses on developing randomized algorithms and analyzing random processes, especially for large, distributed computer networks such as the Web.Runtime: 57:52 minutesI will go over some of my past and present work on hashing-based data structures. After presenting some background on Bloom filters and cuckoo hashing, we will describe cuckoo filters, an efficient data structure for approximate set membership that improves on the well-known Bloom filter. We then discuss recent work on how to make cuckoo filters adaptive in response to false positives, which can be important for many practical problems. Finally, I will present some very recent work on how to possibly improve Bloom filters and related data structures using machine learning techniques