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For data in which the maximum key size is significantly smaller than the number of data items, counting sort may be parallelized by splitting the input into subarrays of approximately equal size, processing each subarray in parallel to generate a separate count array for each subarray, and then merging the count arrays.
A counting Bloom filter is a probabilistic data structure that is used to test whether the number of occurrences of a given element in a sequence exceeds a given threshold. As a generalized form of the Bloom filter, false positive matches are possible, but false negatives are not – in other words, a query returns either "possibly bigger or equal than the threshold" or "definitely smaller ...
As a collection algorithm, reference counting tracks, for each object, a count of the number of references to it held by other objects. If an object's reference count reaches zero, the object has become inaccessible, and can be destroyed. When an object is destroyed, any objects referenced by that object also have their reference counts decreased.
In a counting filter, the array positions (buckets) are extended from being a single bit to being a multibit counter. In fact, regular Bloom filters can be considered as counting filters with a bucket size of one bit. Counting filters were introduced by Fan et al. (2000).
For example, in base 2, the counter can estimate the count to be 1, 2, 4, 8, 16, 32, and all of the powers of two. The memory requirement is simply to hold the exponent. As an example, to increment from 4 to 8, a pseudo-random number would be generated such that the probability the counter is increased is 0.25. Otherwise, the counter remains at 4.
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 .
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The inputs are shown along the bottom of the figure, and the stored element and counter are shown as the symbols and their heights along the black curve. The Boyer–Moore majority vote algorithm is an algorithm for finding the majority of a sequence of elements using linear time and a constant number of words of memory.