The document summarizes a presentation on Bloom filters. The 3 sentence summary is: Bloom filters are compact data structures that allow probabilistic representation of a set to support membership queries with potential for false positives but not false negatives. The presentation explained how Bloom filters work by using multiple hash functions to map elements to bit vectors and set the corresponding bits, demonstrated sample code, and discussed usage and algorithms for setting up and testing membership in a Bloom filter. Questions from the audience were invited at the end.

1. fooTalks Increasing you knowledge through sharing Tell what you know, hear what others know Expect it to happen periodically, every week Expects only a limited audience Expects contributing audience Intrest level rises, can bring experts Need volunteered speakers

2. FooTalks #1 Agenda Introduction Explanation Demo Algorithms Q&A

3. Agenda Bloom Filters

4. Introduction Bloom filters are compact data structures for probabilistic representation of a set in order to support membership queries (i.e. queries that ask: “Is element X in set Y ?”). This compact representation is the payoff for allowing a small rate of false positives in membership queries; that is, queries might incorrectly recognize an element as member of the set.

5. In a simple way.. A datastructure to represent presence of an element in a set With bloom filters you can check if an element is present in the set or not Allows false positives: will say yes even if no Never allow false negatives: will never say no if yes Ie {a,b,c,d} check for 'z' might give yes but check for 'a' will never give a no

6. What else you need? f(”hello”) = 9 f(”fooTalks”) = 23 hash function(s): given an i/p gives a numerical o/p

7. How its represented A bit vector 0 1 2 3 4 5

8. And how it works? f(”hello”) = 5->map this to our bit vector Ie, set index 5 in bit vector 0 1 2 3 4 5

9. If index exceeds Use mod!, why the hell did u learn it for? f('fooTalks') = 9%5=4 0 1 2 3 4 5

10. And what else? Multiple hashes can map to same index f(”hello”) = 5 & f(”foo”)=5 This leads to false positives

11. And you now know why no false negs If not, get out of here...

12. Usage Simple Spell Checker Or i could complicate, used in networks,bioinformatics,blah..blah..blah..

13. Enough with the talking.... Demo Code available at: github.com/jesly.varghese

14. Algorithm: Setting up BF Procedure BloomFilter(set A, hash_functions, integer m) returns filter filter = allocate m bits initialized to 0 foreach a i in A : foreach hash function h j : filter[ h j (a i ) ] = 1 end foreach end foreach return filter

15. Algorithm: Membership Test Procedure MembershipTest (elm, filter, hash_functions) returns yes/no foreach hash function h j : if filter[ h j (elm) ] != 1 return No end foreach return Yes

16. Q&A I hate this part Well you can ask, i can try answering...

17. fooTalks Ends May the force be with you