The Bloom filter, conceived by Burton H. Bloom in 1970, is a

space-efficient probabilistic data structure that is used to test

whether an element is a member of a set. False positives are possible,

but false negatives are not. Elements can be added to the set, but not

removed (though this can be addressed with a counting filter). The

more elements that are added to the set, the larger the probability of

false positives.

For example, one might use a Bloom filter to do spell-checking in a

space-efficient way. A Bloom filter to which a dictionary of correct

words has been added will accept all words in the dictionary and

reject almost all words which are not, which is good enough in some

cases. Depending on the false positive rate, the resulting data

structure can require as little as a byte per dictionary word.

In the last few years Bloom filter become hot topic again and there

were several modifications and improvements. In this talk I will

present my last few improvements in this topic.

Speaker: Ely Porat

Ely Porat received his Doctorate from Bar-Ilan University in 2000.

Following that, he fulfilled his military service and, in parallel,

worked as a faculty member at Bar-Ilan University. Having spent the

spring 2007 semester as a Visiting Scientist in Google, he is now back

at Bar-Ilan University.

The main body of Ely Porat’s work concerns matching problems: string

matching, pattern matching, subset matching. He also worked on the

nearest pair problem in high-dimensional spaces as well as sketching

and edit distance.

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