Revisiting Frequency Moment Estimation in Random Order Streams

Authors Vladimir Braverman, Emanuele Viola, David P. Woodruff, Lin F. Yang

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Author Details

Vladimir Braverman
  • Johns Hopkins University
Emanuele Viola
  • Northeastern University
David P. Woodruff
  • Carnegie Mellon University
Lin F. Yang
  • Princeton University

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Vladimir Braverman, Emanuele Viola, David P. Woodruff, and Lin F. Yang. Revisiting Frequency Moment Estimation in Random Order Streams. In 45th International Colloquium on Automata, Languages, and Programming (ICALP 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 107, pp. 25:1-25:14, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018)


We revisit one of the classic problems in the data stream literature, namely, that of estimating the frequency moments F_p for 0 < p < 2 of an underlying n-dimensional vector presented as a sequence of additive updates in a stream. It is well-known that using p-stable distributions one can approximate any of these moments up to a multiplicative (1+epsilon)-factor using O(epsilon^{-2} log n) bits of space, and this space bound is optimal up to a constant factor in the turnstile streaming model. We show that surprisingly, if one instead considers the popular random-order model of insertion-only streams, in which the updates to the underlying vector arrive in a random order, then one can beat this space bound and achieve O~(epsilon^{-2} + log n) bits of space, where the O~ hides poly(log(1/epsilon) + log log n) factors. If epsilon^{-2} ~~ log n, this represents a roughly quadratic improvement in the space achievable in turnstile streams. Our algorithm is in fact deterministic, and we show our space bound is optimal up to poly(log(1/epsilon) + log log n) factors for deterministic algorithms in the random order model. We also obtain a similar improvement in space for p = 2 whenever F_2 >~ log n * F_1.

Subject Classification

ACM Subject Classification
  • Theory of computation → Sketching and sampling
  • Data Stream
  • Frequency Moments
  • Random Order
  • Space Complexity
  • Insertion Only Stream


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