  In this paper we consider the problem of estimating quantiles when data are
received sequentially (data stream). For real life data streams, the
distribution of the data typically varies with time making estimation of
quantiles challenging. We present a method that simultaneously maintain
estimates of multiple quantiles of the data stream distribution. The method is
based on making incremental updates of the quantile estimates every time a new
sample from the data stream is received. The method is memory and
computationally efficient since it only stores one value for each quantile
estimate and only performs one operation per quantile estimate when a new
sample is received from the data stream. The estimates are realistic in the
sense that the monotone property of quantiles is satisfied in every iteration.
Experiments show that the method efficiently tracks multiple quantiles and
outperforms state of the art methods.
