A unified scheme for generalizing cardinality estimators to sum aggregation

Reuven Cohen; Liran Katzir; Aviv Yehezkel

doi:10.1016/j.ipl.2014.10.009

A unified scheme for generalizing cardinality estimators to sum aggregation

Reuven Cohen, Liran Katzir, Aviv Yehezkel

Research output: Contribution to journal › Article › peer-review

5 Scopus citations

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.

Original language	English
Pages (from-to)	336-342
Number of pages	7
Journal	Information Processing Letters
Volume	115
Issue number	2
DOIs	https://doi.org/10.1016/j.ipl.2014.10.009
State	Published - Feb 2015
Externally published	Yes

Bibliographical note

Funding Information:
The research leading to these results has received funding from the European Community's Seventh Framework Programme (FP7/2007–2013) under grant agreement No. 610802 .

Publisher Copyright:
© 2014 Elsevier B.V. All rights reserved.

Keywords

Algorithms
Big data processing
Statistical

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10.1016/j.ipl.2014.10.009

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A unified scheme for generalizing cardinality estimators to sum aggregation

Abstract

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Keywords

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