PPT-Filters (Bloom, Quotient, & Cuckoo)

CSCI 333 Bloom Filters Are there any problems with Bloom filters What operations do they supportnot support How do you grow a Bloom filter What if your filter itself exceeds RAM how bad is locality. Practically Better Than Bloom. Bin Fan . (CMU/Google). David Andersen (CMU). Michael . Kaminsky. (Intel Labs). Michael . Mitzenmacher. (Harvard). 1. What is Bloom Filter? A Compact Data Structure . An Introduction and Really Most Of . It. CMSC 491. Hadoop. -Based Distributed Computing. Spring 2015. Adam Shook. Agenda. Discuss . what a set data structure. . is using math terms. Discuss . the concept of a. Bioinformatics. HW2. Papers. 1. . BLAT-. -The BLAST-Like Alignment Tool. 2. Classification . of DNA sequences using Bloom Filters. Course Intructor. Dr. Jianhua Ruan. Presenters. Husnu Narman. Nihat Altiparmak. )     /)!0
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 6)1 !  &7(%   "   819    "     for Lazy Transactional Memory. Mark Jeffrey . and J. Gregory . Steffan. ECE, University of Toronto. November 10, 2011. Parallel Programming is Hard. Mark Jeffrey, Improving Bloom Filter Configuration for Lazy TM. Probability and Computing. Randomized algorithms and probabilistic analysis P109~P111. Michael . Mitzenmacher. Eli . Upfal. Introduction. Approximate set membership problem .. Trade-off between the space and the false positive probability .. approximate membership. dynamic data structures. Shachar. Lovett. IAS. Ely . Porat. Bar-. Ilan. University. Synergies in lower bounds, June 2011. Information theoretic lower bounds. Information theory. Faster Triangle . Listing and Set Intersection. David Eppstein. 1. , . Michael T. Goodrich. 1. ,. Michael Mitzenmacher. 2. , and Manuel Torres. 1. 1. University of California, Irvine. 2. Harvard University. : Using . Multi-Level . Parallelism and . 2-3 Cuckoo Filters for Faster Set Intersection Queries and Sparse Boolean . Matrix Multiplication. David . Eppstein. and . Michael T. Goodrich. University of California, Irvine. Outline. Randomized methods. : today. SGD with the hash trick (recap). Bloom filters. Later:. count-min sketches. l. ocality sensitive hashing. THE Hash Trick: A Review. Hash Trick - Insights. Save memory: don’t store hash keys. Spring 2018. Stanford University . Computer Science Department. Lecturer: Chris Gregg. CS 106B. Lecture 26: Esoteric Data Structures: Skip Lists and Bloom Filters. Today's Topics. Logistics. Final Exam Review materials posted by 5pm today: . Andrei . Broder. and Michael . Mitzenmacher. Presenter: . Chen Qian. Slides . credit: . Hongkun. Yang. Outline. Bloom Filter Overview. Standard Bloom Filters. Counting Bloom Filters. Historical Applications. Listing and Set Intersection. David Eppstein. 1. , . Michael T. Goodrich. 1. ,. Michael Mitzenmacher. 2. , and Manuel Torres. 1. 1. University of California, Irvine. 2. Harvard University. Triangle Listing Problem. Bloom Filters. Lookup questions: Does item . “. x. ”. exist in a set or multiset?. Data set may be very big or expensive to access. Filter lookup questions with negative results before accessing data..