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A concurrent hash table or concurrent hash map is an implementation of hash tables allowing concurrent access by multiple threads using a hash function. [1] [2] Concurrent hash tables represent a key concurrent data structure for use in concurrent computing which allow multiple threads to more efficiently cooperate for a computation among ...
For unordered access as defined in the java.util.Map interface, the java.util.concurrent.ConcurrentHashMap implements java.util.concurrent.ConcurrentMap. [2] The mechanism is a hash access to a hash table with lists of entries, each entry holding a key, a value, the hash, and a next reference.
Java programming language includes the HashSet, HashMap, LinkedHashSet, and LinkedHashMap generic collections. [54] Python's built-in dict implements a hash table in the form of a type. [55] Ruby's built-in Hash uses the open addressing model from Ruby 2.4 onwards. [56] Rust programming language includes HashMap, HashSet as part of the Rust ...
Each entry in the table contains the frequency or count of the occurrences of values within a particular group or interval, and in this way, the table summarizes the distribution of values in the sample. This is an example of a univariate (=single variable) frequency table. The frequency of each response to a survey question is depicted.
In computing, the count–min sketch (CM sketch) is a probabilistic data structure that serves as a frequency table of events in a stream of data. It uses hash functions to map events to frequencies, but unlike a hash table uses only sub-linear space , at the expense of overcounting some events due to collisions .
The List Update or the List Access problem is a simple model used in the study of competitive analysis of online algorithms.Given a set of items in a list where the cost of accessing an item is proportional to its distance from the head of the list, e.g. a linked List, and a request sequence of accesses, the problem is to come up with a strategy of reordering the list so that the total cost of ...
Using bucket sort, this can be done in O(b + n), where n is the number of nodes in the DHT. When there are multiple operations addressing the same key within one batch, the batch is condensed before being sent out. For example, multiple lookups of the same key can be reduced to one or multiple increments can be reduced to a single add operation.
which shows which documents contain which terms and how many times they appear. Note that, unlike representing a document as just a token-count list, the document-term matrix includes all terms in the corpus (i.e. the corpus vocabulary), which is why there are zero-counts for terms in the corpus which do not also occur in a specific document.