Frédéric Giroire
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Order statistics and estimating cardinalities of massive data sets
dmtcs:3353 -
Discrete Mathematics & Theoretical Computer Science,
January 1, 2005,
DMTCS Proceedings vol. AD, International Conference on Analysis of Algorithms
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https://doi.org/10.46298/dmtcs.3353
Order statistics and estimating cardinalities of massive data setsArticle
Authors: Frédéric Giroire 1
0000-0002-3727-051X
Frédéric Giroire
1 Algorithms
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.
Shuzhuang Zhang;Hao Luo;Zhigang Wu;Yi Wang, Proceedings of the ACM Turing 50th Celebration Conference - China, Composed sketch framework for quantiles and cardinality queries over big data streams, 2017, Shanghai China, 10.1145/3063955.3063995.
Yousra Chabchoub;Georges Hebrail, IEEE ... International Conference on Data Mining workshops, Sliding HyperLogLog: Estimating Cardinality in a Data Stream over a Sliding Window, pp. 1297-1303, 2010, Sydney, NSW, 10.1109/icdmw.2010.18.