Skip to Main content Skip to Navigation
Conference papers

An optimal cardinality estimation algorithm based on order statistics and its full analysis

Jérémie Lumbroso 1
1 APR - Algorithmes, Programmes et Résolution
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : Building on the ideas of Flajolet and Martin (1985), Alon et al. (1987), Bar-Yossef et al. (2002), Giroire (2005), we develop a new algorithm for cardinality estimation, based on order statistics which, according to Chassaing and Gerin (2006), is optimal among similar algorithms. This algorithm has a remarkably simple analysis that allows us to take its $\textit{fine-tuning}$ and the $\textit{characterization of its properties}$ further than has been done until now. We prove that, asymptotically, it is $\textit{strictly unbiased}$ (contrarily to Probabilistic Counting, Loglog, Hyperloglog), we verify that its relative precision is about $1/\sqrt{m-2}$ when $m$ words of storage are used, and we fully characterize the limit law of the estimates it provides, in terms of gamma distribution―-this is the first such algorithm for which the limit law has been established. We also develop a Poisson analysis for the pre-asymptotic regime. In this way, we are able to devise a complete algorithm, covering all cardinalities ranges from $0$ to very large.
Complete list of metadata

Cited literature [12 references]  Display  Hide  Download
Contributor : Coordination Episciences Iam <>
Submitted on : Thursday, August 20, 2015 - 4:32:49 PM
Last modification on : Friday, January 8, 2021 - 5:32:06 PM
Long-term archiving on: : Wednesday, April 26, 2017 - 9:57:44 AM


Publisher files allowed on an open archive


  • HAL Id : hal-01185578, version 1


Jérémie Lumbroso. An optimal cardinality estimation algorithm based on order statistics and its full analysis. 21st International Meeting on Probabilistic, Combinatorial, and Asymptotic Methods in the Analysis of Algorithms (AofA'10), Jun 2010, Vienna, Austria. pp.489-504. ⟨hal-01185578⟩



Record views


Files downloads