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3. Filtering Patterns > Bloom Filtering

Bloom Filtering

Pattern Description

Bloom filtering does the same thing as the previous pattern, but it has a unique evaluation function applied to each record.


Filter such that we keep records that are member of some predefined set of values. It is not a problem if the output is a bit inaccurate, because we plan to do further checking. The predetermined list of values will be called the set of hot values.

For each record, extract a feature of that record. If that feature is a member of a set of values represented by a Bloom filter, keep it; otherwise toss it out (or the reverse).


Bloom filtering is similar to generic filtering in that it is looking at each record and deciding whether to keep or remove it. However, there are two major differences that set it apart from generic filtering. First, we want to filter the record based on some sort of set membership operation against the hot values. For example: keep or throw away this record if the value in the user field is a member of a predetermined list of users. Second, the set membership is going to be evaluated with a Bloom filter, described in the Appendix A. In one sense, Bloom filtering is a join operation in which we don’t care about the data values of the right side of the join.


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