We present new algorithms for computing approximate quantiles of large datasets in a single pass. The approximation guarantees are explicit, and apply without regard to the value distribution or the arrival distributions of the dataset. The main memory requirements are smaller than those reported earlier by an order of magnitude. We also discuss methods that couple the approximation algorithms with random sampling to further reduce memory requirements. With sampling, the approximation guarantees are explicit but probabilistic, i.e., they apply with respect to a (user controlled) confidence parameter. We present the algorithms, their theoretical analysis and simulation results. 1 Introduction This article studies the problem of computing...
Abstract Order statistics, i.e., quantiles, are frequently used in databases both at the database se...
A quantile summary is a data structure that approximates to epsilon-relative error the order statist...
We empirically evaluate lightweight moment estimators for the single-pass quantile approximation pro...
In a recent paper [MRL98], we had described a general framework for single pass approximate quantile...
The '-quantile of an ordered sequence of data values is the element with rank ' \Theta n, ...
Quantiles are very important statistics information used to describe the distribution of datasets. G...
In data warehousing applications, numerous OLAP queries involve the processing of holistic operation...
We present a fast algorithm for computing approx-imate quantiles in high speed data streams with det...
Part 10: Learning - IntelligenceInternational audienceThe goal of our research is to estimate the qu...
We present lower bounds on the space required to estimate the quantiles of a stream of numerical val...
The traditional estimator ˆξp,n for the p-quantile ξp of a random variable X, given n observations f...
A fundamental problem in data management and analysis is to generate descriptions of the distributio...
A fundamental problem in data management and analysis is to gen-erate descriptions of the distributi...
In this paper we propose new method for simultaneous generating multiple quantiles corresponding to ...
In this paper, we present two new stochastic approximation algorithms for the problem of quantile es...
Abstract Order statistics, i.e., quantiles, are frequently used in databases both at the database se...
A quantile summary is a data structure that approximates to epsilon-relative error the order statist...
We empirically evaluate lightweight moment estimators for the single-pass quantile approximation pro...
In a recent paper [MRL98], we had described a general framework for single pass approximate quantile...
The '-quantile of an ordered sequence of data values is the element with rank ' \Theta n, ...
Quantiles are very important statistics information used to describe the distribution of datasets. G...
In data warehousing applications, numerous OLAP queries involve the processing of holistic operation...
We present a fast algorithm for computing approx-imate quantiles in high speed data streams with det...
Part 10: Learning - IntelligenceInternational audienceThe goal of our research is to estimate the qu...
We present lower bounds on the space required to estimate the quantiles of a stream of numerical val...
The traditional estimator ˆξp,n for the p-quantile ξp of a random variable X, given n observations f...
A fundamental problem in data management and analysis is to generate descriptions of the distributio...
A fundamental problem in data management and analysis is to gen-erate descriptions of the distributi...
In this paper we propose new method for simultaneous generating multiple quantiles corresponding to ...
In this paper, we present two new stochastic approximation algorithms for the problem of quantile es...
Abstract Order statistics, i.e., quantiles, are frequently used in databases both at the database se...
A quantile summary is a data structure that approximates to epsilon-relative error the order statist...
We empirically evaluate lightweight moment estimators for the single-pass quantile approximation pro...