Search results
Results From The WOW.Com Content Network
The values are usually used to index a fixed-size table called a hash table. Use of a hash function to index a hash table is called hashing or scatter-storage addressing. Hash functions and their associated hash tables are used in data storage and retrieval applications to access data in a small and nearly constant time per retrieval.
In a well-dimensioned hash table, the average time complexity for each lookup is independent of the number of elements stored in the table. Many hash table designs also allow arbitrary insertions and deletions of key–value pairs, at amortized constant average cost per operation. [3] [4] [5] Hashing is an example of a space-time tradeoff.
To create a hashing function for a hash table, often a function is used that has a large domain. To create an index from the output of the function, a modulo can be taken to reduce the size of the domain to match the size of the array; however, it is often faster on many processors to restrict the size of the hash table to powers of two sizes ...
Zobrist hashing (also referred to as Zobrist keys or Zobrist signatures [1]) is a hash function construction used in computer programs that play abstract board games, such as chess and Go, to implement transposition tables, a special kind of hash table that is indexed by a board position and used to avoid analyzing the same position more than once.
An important optimization, to reduce the effect of coalescing, is to restrict the address space of the hash function to only a subset of the table. For example, if the table has size M with buckets numbered from 0 to M − 1 , we can restrict the address space so that the hash function only assigns addresses to the first N locations in the table.
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.
It is fast and efficient during initialization. Many programming environments based on PHP 5, Python, and ASP.NET use variants of this hash. The hash is easy to flood, exposing the servers. BuzHash was created by Robert Uzgalis in 1992. It is designed around a substitution table and can tolerate extremely skewed distributions on the input.
The scalability issue does not occur in this data structure. Once the designed capacity is exceeded, the keys could be reinserted in a new hash table of double size. The space efficient variant by Putze, Sanders & Singler (2007) could also be used to implement counting filters by supporting insertions and deletions.