BLOOMFILTER(2)BLOOMFILTER(2)NAME
Bloomfilter - Bloom filters
SYNOPSIS
include "sets.m";
include "bloomfilter.m";
bloomfilter := load Bloomfilter Bloomfilter->PATH;
init: fn();
filter: fn(d: array of byte, logm, k: int): Sets->Set;
DESCRIPTION
A Bloom filter is a method of representing a set to support probabilis‐
tic membership queries. It uses independent hash functions of members
of the set to set elements of a bit-vector. Init should be called
first to initialise the module. Filter returns a Set s representing
the Bloom filter for the single-member set {d}. K independent hash
functions are used, each of range [0, 2^logm), to return a Bloom filter
2^logm bits wide. It is an error if logm is less than 3 or greater than
30.
Bloom filters can be combined by set union. The set represented by
Bloom filter a is not a subset of another b if there are any members in
a that are not in b. Together, logm, k, and n (the number of members
in the set) determine the false positve rate (the probability that a
membership test will not eliminate a member that is not in fact in the
set). The probability of a false positive is approximately
(1-e^(-kn/(2^logm))^k. For a given false positive rate, f, a useful
formula to determine appropriate parameters is: k=ceil(-log₂(f)), and
logm=ceil(log₂(nk)).
EXAMPLES
Create a 128 bit-wide bloom filter f representing all the elements in
the string array elems, with k=6.
A, B, None: import Sets;
for(i:=0; i<len elems; i++)
f = f.X(A|B, filter(array of byte elems[i], 7, 6));
Test whether the string s is a member of f. If there were 12 elements
in elems, the probability of a false positive would be approximately
0.0063.
if(filter(array of byte s, 7, 6).X(A&~B, f).eq(None))
sys->print("'%s' might be a member of f\n", s);
SOURCE
/appl/lib/bloomfilter.b
SEE ALSOsets(2)BLOOMFILTER(2)
