Accurate quantile estimation for skewed data streams | IEEE Conference Publication | IEEE Xplore

Accurate quantile estimation for skewed data streams


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 More

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.
Date of Conference: 08-13 October 2017
Date Added to IEEE Xplore: 15 February 2018
ISBN Information:
Electronic ISSN: 2166-9589
Conference Location: Montreal, QC, Canada

References

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