Lower Bounds for Quantile Estimation in Random-Order and Multi-Pass Streaming
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. [6,7,1,19,2,6,7,19,2]). 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(loglog n) passes are sufficient in the random-order model.