# Order statistics and estimating cardinalities of massive data sets

1 ALGORITHMS - Algorithms
Inria Paris-Rocquencourt
Abstract : We introduce a new class of algorithms to estimate the cardinality of very large multisets using constant memory and doing only one pass on the data. It is based on order statistics rather that on bit patterns in binary representations of numbers. We analyse three families of estimators. They attain a standard error of $\frac{1}{\sqrt{M}}$ using $M$ units of storage, which places them in the same class as the best known algorithms so far. They have a very simple internal loop, which gives them an advantage in term of processing speed. The algorithms are validated on internet traffic traces.
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Type de document :
Communication dans un congrès
Conrado Martínez. 2005 International Conference on Analysis of Algorithms, 2005, Barcelona, Spain. Discrete Mathematics and Theoretical Computer Science, DMTCS Proceedings vol. AD, International Conference on Analysis of Algorithms, pp.157-166, 2005, DMTCS Proceedings
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Littérature citée [12 références]

https://hal.inria.fr/hal-01184025
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Soumis le : mercredi 12 août 2015 - 15:51:21
Dernière modification le : vendredi 25 mai 2018 - 12:02:05
Document(s) archivé(s) le : vendredi 13 novembre 2015 - 11:39:58

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Frédéric Giroire. Order statistics and estimating cardinalities of massive data sets. Conrado Martínez. 2005 International Conference on Analysis of Algorithms, 2005, Barcelona, Spain. Discrete Mathematics and Theoretical Computer Science, DMTCS Proceedings vol. AD, International Conference on Analysis of Algorithms, pp.157-166, 2005, DMTCS Proceedings. 〈hal-01184025〉

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