Lower bounds for quantile estimation in random-order and multi-pass streaming

  • Authors:
  • Sudipto Guha;Andrew McGregor

  • Affiliations:
  • University of Pennsylvania;University of California, San Diego

  • Venue:
  • ICALP'07 Proceedings of the 34th international conference on Automata, Languages and Programming
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

We present lower bounds on the space required to estimate the quantiles of a stream of numerical values. Quantile estimation is perhaps the most studied problem in the data stream model and it is relatively well understood in the basic single-pass data stream model in which the values are ordered adversarially. Natural extensions of this basic model include the random-order model in which the values are ordered randomly (e.g. [21,5,13,11,12]) and the multi-pass model in which an algorithm is permitted a limited number of passes over the stream (e.g. [6,7,1,19,2,6,7,1,19,2]). We present lower bounds that complement existing upper bounds [21,11] in both models. One consequence is an exponential separation between the random-order and adversarial-order models: using Ω(polylog n) space, exact selection requires Ω(log n) passes in the adversarial-order model while O(log log n) passes are sufficient in the random-order model.