Abstract:
Quantile estimation is a fundamental method to generate descriptions of the distribution of data for data management and analysis. Although the investigation and design o...Show MoreMetadata
Abstract:
Quantile estimation is a fundamental method to generate descriptions of the distribution of data for data management and analysis. Although the investigation and design of efficient quantile estimation algorithm has attracted much study, the problem of accurately finding quantiles in the case of skewed data streams, which are prevalent in many data sources like IP traffic streams in the 4G mobile network and text data, is still not well addressed. In this paper we specifically address the problem of estimating the quantiles of massive skewed data streams by designing and implementing an incremental quantile estimation with nonlinear-interpolation algorithm. The comprehensive experimental evaluation results demonstrate that the estimated quantiles of the proposed algorithm are highly accurate than existing methods in the literature on both synthetic and real-world datasets, especially on important extreme quantiles.
Published in: 2017 IEEE 28th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC)
Date of Conference: 08-13 October 2017
Date Added to IEEE Xplore: 15 February 2018
ISBN Information:
Electronic ISSN: 2166-9589