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Boosting distinct random sampling for basic counting on the union of distributed streams

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Publication:497679
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DOI10.1016/j.tcs.2015.08.014zbMath1329.68279OpenAlexW1644787642MaRDI QIDQ497679

Bojian Xu

Publication date: 25 September 2015

Published in: Theoretical Computer Science (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.tcs.2015.08.014

zbMATH Keywords

data streamdirect samplingbasic countingcoordinated adaptive samplingdistinct samplingdistributed streams


Mathematics Subject Classification ID

Distributed algorithms (68W15)




Cites Work

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  • Universal classes of hash functions
  • The space complexity of approximating the frequency moments
  • Sketching asynchronous data streams over sliding windows
  • Data streams. Models and algorithms.
  • Handbook of Finite Fields
  • Algorithms for distributed functional monitoring
  • Sublinear Time Algorithms
  • Time-decaying Sketches for Robust Aggregation of Sensor Data
  • A Deterministic Algorithm for Summarizing Asynchronous Streams over a Sliding Window
  • Faster methods for random sampling
  • Maintaining Stream Statistics over Sliding Windows
  • Communication Complexity
  • Range‐Efficient Counting of Distinct Elements in a Massive Data Stream
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