Adaptive sampling

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Adaptive sampling [a1] is a probabilistic algorithm invented by M. Wegman (unpublished) around 1980. It provides an unbiased estimator of the number of distinct elements (the "cardinality" ) of a file (a sequence of data items) of potentially large size that contains unpredictable replications. The algorithm is useful in data-base query optimization and in information retrieval. By standard hashing techniques [a3], [a6] the problem reduces to the following.

A sequence of real numbers is given. The sequence has been formed by drawing independently and randomly an unknown number of real numbers from , after which the elements are replicated and permuted in some unknown fashion. The problem is to estimate the cardinality in a computationally efficient manner.

Three algorithms can perform this task.

1) Straight scan computes incrementally the sets , where replications are eliminated on the fly. (This can be achieved by keeping the successive in sorted order.) The cardinality is then determined exactly by but the auxiliary memory needed is , which may be as large as , resulting in a complexity that is prohibitive in many applications.

2) Static sampling is based on a fixed sampling ratio , where (e.g., ). One computes sequentially the samples . The cardinality estimate returned is . The estimator is unbiased and the memory used is on average.

3) Adaptive sampling is based on a design parameter (e.g., ) and it maintains a dynamically changing sampling rate and a sequence of samples . Initially, and . The rule is like that of static sampling, but with divided by each time the cardinality of would exceed and with modified accordingly in order to contain only . The estimator (where the final value of is used) is proved to be unbiased and the memory used is at most .

The accuracy of any such unbiased estimator of is measured by the standard deviation of divided by . For adaptive sampling, the accuracy is almost constant as a function of and asymptotically close to

a result established in [a1] by generating functions and Mellin transform techniques. An alternative algorithm, called probabilistic counting [a2], provides an estimator of cardinalities that is unbiased only asymptotically but has a better accuracy, of about .

Typically, the adaptive sampling algorithm can be applied to gather statistics on word usage in a large text. Well-designed hashing transformations are then known to fulfill practically the uniformity assumption [a4]. A general perspective on probabilistic algorithms may be found in [a5].


[a1] P. Flajolet, "On adaptive sampling" Computing , 34 (1990) pp. 391–400
[a2] P. Flajolet, G.N. Martin, "Probabilistic counting algorithms for data base applications" J. Comp. System Sci. , 31 : 2 (1985) pp. 182–209
[a3] D.E. Knuth, "The art of computer programming" , 3. Sorting and Searching , Addison-Wesley (1973)
[a4] V.Y. Lum, P.S.T. Yuen, M. Dodd, "Key to address transformations: a fundamental study based on large existing format files" Commun. ACM , 14 (1971) pp. 228–239
[a5] R. Motwani, P. Raghavan, "Randomized algorithms" , Cambridge Univ. Press (1995)
[a6] R. Sedgewick, "Algorithms" , Addison-Wesley (1988) (Edition: Second)
How to Cite This Entry:
Adaptive sampling. Encyclopedia of Mathematics. URL:
This article was adapted from an original article by Ph. Flajolet (originator), which appeared in Encyclopedia of Mathematics - ISBN 1402006098. See original article