bloom filters

We have hosted the application bloom filters in order to run this application in our online workstations with Wine or directly.

Quick description about bloom filters:

A Bloom filter is a concise/compressed representation of a set, where the main requirement is to make membership queries; i.e., whether an item is a member of a set. A Bloom filter will always correctly report the presence of an element in the set when the element is indeed present. A Bloom filter can use much less storage than the original set, but it allows for some 'false positives': it may sometimes report that an element is in the set whereas it is not. When you construct, you need to know how many elements you have (the desired capacity), and what is the desired false positive rate you are willing to tolerate. A common false-positive rate is 1%. The lower the false-positive rate, the more memory you are going to require. Similarly, the higher the capacity, the more memory you will use. You may construct the Bloom filter capable of receiving 1 million elements with a false-positive rate of 1%.

Features:

• You should call NewWithEstimates conservatively
• Our implementation accepts keys for setting and testing as []byte
• Sometimes, the actual false positive rate may differ (slightly) from the theoretical false positive rate
• A Bloom filter has two parameters: m, the number of bits used in storage, and k, the number of hashing functions on elements of the set
• When you construct, you need to know how many elements you have (the desired capacity), and what is the desired false positive rate you are willing to tolerate
• A Bloom filter can use much less storage than the original set

Programming Language: Go.
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