Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
10331914 | Information Processing Letters | 2015 | 7 Pages |
Abstract
Cardinality estimation algorithms receive a stream of elements that may appear in arbitrary order, with possible repetitions, and return the number of distinct elements. Such algorithms usually seek to minimize the required storage at the price of inaccuracy in their output. This paper shows how to generalize every cardinality estimation algorithm that relies on extreme order statistics (min/max sketches) to a weighted version, where each item is associated with a weight and the goal is to estimate the total sum of weights. The proposed unified scheme uses the unweighted estimator as a black-box, and manipulates the input using properties of the beta distribution.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Computational Theory and Mathematics
Authors
Reuven Cohen, Liran Katzir, Aviv Yehezkel,