License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ESA.2022.34
URN: urn:nbn:de:0030-drops-169725
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Chakraborty, Sourav ; Vinodchandran¹, N. V. ; Meel, Kuldeep S.

Distinct Elements in Streams: An Algorithm for the (Text) Book

LIPIcs-ESA-2022-34.pdf (0.7 MB)


Given a data stream ? = ⟨ a₁, a₂, …, a_m ⟩ of m elements where each a_i ∈ [n], the Distinct Elements problem is to estimate the number of distinct elements in ?. Distinct Elements has been a subject of theoretical and empirical investigations over the past four decades resulting in space optimal algorithms for it. All the current state-of-the-art algorithms are, however, beyond the reach of an undergraduate textbook owing to their reliance on the usage of notions such as pairwise independence and universal hash functions. We present a simple, intuitive, sampling-based space-efficient algorithm whose description and the proof are accessible to undergraduates with the knowledge of basic probability theory.

BibTeX - Entry

  author =	{Chakraborty, Sourav and Vinodchandran¹, N. V. and Meel, Kuldeep S.},
  title =	{{Distinct Elements in Streams: An Algorithm for the (Text) Book}},
  booktitle =	{30th Annual European Symposium on Algorithms (ESA 2022)},
  pages =	{34:1--34:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-247-1},
  ISSN =	{1868-8969},
  year =	{2022},
  volume =	{244},
  editor =	{Chechik, Shiri and Navarro, Gonzalo and Rotenberg, Eva and Herman, Grzegorz},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{},
  URN =		{urn:nbn:de:0030-drops-169725},
  doi =		{10.4230/LIPIcs.ESA.2022.34},
  annote =	{Keywords: F₀ Estimation, Streaming, Sampling}

Keywords: F₀ Estimation, Streaming, Sampling
Collection: 30th Annual European Symposium on Algorithms (ESA 2022)
Issue Date: 2022
Date of publication: 01.09.2022

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