Tracking Quantiles of Network Data Streams with Dynamic Operations

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Provided by: Columbia University
Topic: Networking
Format: PDF
Quantiles are very useful in characterizing the data distribution of an evolving dataset in the process of data mining or network monitoring. The method of Stochastic Approximation (SA) tracks quantiles online by incrementally deriving and updating local approximations of the underly distribution function at the quantiles of interest. In this paper, the authors propose a generalization of the SA method for quantile estimation that allows not only data insertions, but also dynamic data operations such as deletions and updates.
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