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Bloom filter

Bloom filter
Bloom proposed the technique for applications where the amount of source data would require an impracticably large hash area in memory if "conventional" error-free hashing techniques were applied. He gave the example of a hyphenation algorithm for a dictionary of 500,000 words, out of which 90% follow simple hyphenation rules, but the remaining 10% require expensive disk accesses to retrieve specific hyphenation patterns. With sufficient core memory, an error-free hash could be used to eliminate all unnecessary disk accesses; on the other hand, with limited core memory, Bloom's technique uses a smaller hash area but still eliminates most unnecessary accesses. For example, a hash area only 15% of the size needed by an ideal error-free hash still eliminates 85% of the disk accesses (Bloom (1970)). More generally, fewer than 10 bits per element are required for a 1% false positive probability, independent of the size or number of elements in the set (Bonomi et al. (2006)). . . . as before.

http://en.wikipedia.org/wiki/Bloom_filter

Related:  abstract data types and data structures

Hash table A small phone book as a hash table Hashing[edit] The idea of hashing is to distribute the entries (key/value pairs) across an array of buckets. Given a key, the algorithm computes an index that suggests where the entry can be found: index = f(key, array_size) Think Bayes Bayesian Statistics Made Simple by Allen B. Downey Download Think Bayes in PDF. Read Think Bayes in HTML. Order Think Bayes from Amazon.com. Why Bloom filters work the way they do Imagine you’re a programmer who is developing a new web browser. There are many malicious sites on the web, and you want your browser to warn users when they attempt to access dangerous sites. For example, suppose the user attempts to access You’d like a way of checking whether domain is known to be a malicious site. What’s a good way of doing this? An obvious naive way is for your browser to maintain a list or set data structure containing all known malicious domains.

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Binary search tree A binary search tree of size 9 and depth 3, with 8 at the root. The leaves are not drawn. Binary search trees keep their keys in sorted order, so that lookup and other operations can use the principle of binary search: when looking for a key in a tree (or a place to insert a new key), they traverse the tree from root to leaf, making comparisons to keys stored in the nodes of the tree and deciding, based on the comparison, to continue searching in the left or right subtrees. On average, this means that each comparison allows the operations to skip about half of the tree, so that each lookup, insertion or deletion takes time proportional to the logarithm of the number of items stored in the tree.

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