Approximate medians and other quantiles in one pass and with limited memory
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
Incremental quantile estimation for massive tracking
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Space-efficient online computation of quantile summaries
SIGMOD '01 Proceedings of the 2001 ACM SIGMOD international conference on Management of data
Quantile and histogram estimation
Proceedings of the 33nd conference on Winter simulation
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Managing telecommunication networks involves collecting and analyzing large amounts of statistical data. The standard approach to estimating quantiles involves capturing all the relevant data (what may require significant storage/processing capacities), and performing an off-line analysis (what may delay management actions). It is often essential to estimate quantiles as the data are collected, and to take management actions promptly. Towards this goal, we present a minimalist approach to sequentially estimating constant/changing over time quantiles. We follow prior work and devise a fixed-point algorithm, which does not estimate the unknown probability density function at the quantile. Instead, our algorithm uses the log-odds transformation of the observed fractions, and the exponentially smoothed estimates of the standard deviation to update the quantile estimate. For large data streams, this algorithm can significantly reduce the amount of collected data and the complexity of data analysis.