@inproceedings{da63b4431e2c4ee487cd6dd5e4880dd3,
title = "Stream quantiles via maximal entropy histograms",
abstract = "We address the problem of estimating the running quantile of a data stream when the memory for storing observations is limited.We (i) highlight the limitations of approaches previously described in the literature which make them unsuitable for non-stationary streams, (ii) describe a novel principle for the utilization of the available storage space, and (iii) introduce two novel algorithms which exploit the proposed principle. Experiments on three large realworld data sets demonstrate that the proposed methods vastly outperform the existing alternatives.",
author = "Oggie Arandelovic and Ducson Pham and Svetha Venkatesh",
year = "2014",
language = "English",
isbn = "9783319126395",
volume = "8835",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer-Verlag",
pages = "327--334",
booktitle = "Neural Information Processing - 21st International Conference, ICONIP 2014, Proceedings",
address = "Germany",
note = "21st International Conference on Neural Information Processing, ICONIP 2014 ; Conference date: 03-11-2014 Through 06-11-2014",
}