Incremental quantile estimation for massive tracking
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast, small-space algorithms for approximate histogram maintenance
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Approximate counts and quantiles over sliding windows
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incremental tracking of multiple quantiles for network monitoring in cellular networks
Proceedings of the 1st ACM workshop on Mobile internet through cellular networks
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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, we 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.