Distinct Elements in Streams: An Algorithm for the (Text) Book
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Abstract (arXiv)
Given a data stream A = langle a1, a2, ldots, am rangle of m elements where each ai ∈ [n], the Distinct Elements problem is to estimate the number of distinct elements in A.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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). First posted on arXiv 24 Jan 2023.
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- 2025Estimating Coverage in Streams via a Modified CVM Methodcited by 0
- 2025Estimating Coverage in Streams via a Modified CVM Method6 Apr 2025