Max-stable random sketches can be computed efficiently on fast streaming positive data sets by using only sequential access to the data. They can be used to answer point and Lp-norm queries for the signal. There is an intriguing connection between the so-called p-stable (or sum-stable) and the max-stable sketches. Rigorous performance guarantees through error-probability estimates are derived and the algorithmic implementation is discussed.Published versio
Consider a set of signals fs: {1,..., N} → [0,...,M] appearing as a stream of tuples (i, fs(i)) in ...
AbstractWe present two new algorithms for the range-efficient F0 estimating problem and improve the ...
Estimating ranks, quantiles, and distributions over streaming data is a central task in data analysi...
Let f: f1; 2; : : : ; Ng! [0;1) be a non{negative signal, de¯ned over a very large domain and suppos...
We consider the problem of sketching the p-th frequency moment of a vector, p>2, with multiplicative...
In insertion-only streaming, one sees a sequence of indices a_1, a_2, ..., a_m in [n]. The stream de...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
Recently, [Li, Nguyen, Woodruff, STOC 2014] showed any 1-pass constant probability streaming algorit...
We initiate the study of data dimensionality reduction, or sketching, for the q -> p norms. Given an...
Work on approximate linear algebra has led to efficient distributed and streaming algorithms for pro...
Abstract. In this article, we show several results obtained by combining the use of stable distribut...
We revisit one of the classic problems in the data stream literature, namely, that of estimating the...
Approximating ranks, quantiles, and distributions over streaming data is a central task in data anal...
Learning parameters from voluminous data can be prohibitive in terms of memory and computational req...
The count-min sketch (CMS) is a randomized data structure that provides estimates of tokens’ freque...
Consider a set of signals fs: {1,..., N} → [0,...,M] appearing as a stream of tuples (i, fs(i)) in ...
AbstractWe present two new algorithms for the range-efficient F0 estimating problem and improve the ...
Estimating ranks, quantiles, and distributions over streaming data is a central task in data analysi...
Let f: f1; 2; : : : ; Ng! [0;1) be a non{negative signal, de¯ned over a very large domain and suppos...
We consider the problem of sketching the p-th frequency moment of a vector, p>2, with multiplicative...
In insertion-only streaming, one sees a sequence of indices a_1, a_2, ..., a_m in [n]. The stream de...
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer...
Recently, [Li, Nguyen, Woodruff, STOC 2014] showed any 1-pass constant probability streaming algorit...
We initiate the study of data dimensionality reduction, or sketching, for the q -> p norms. Given an...
Work on approximate linear algebra has led to efficient distributed and streaming algorithms for pro...
Abstract. In this article, we show several results obtained by combining the use of stable distribut...
We revisit one of the classic problems in the data stream literature, namely, that of estimating the...
Approximating ranks, quantiles, and distributions over streaming data is a central task in data anal...
Learning parameters from voluminous data can be prohibitive in terms of memory and computational req...
The count-min sketch (CMS) is a randomized data structure that provides estimates of tokens’ freque...
Consider a set of signals fs: {1,..., N} → [0,...,M] appearing as a stream of tuples (i, fs(i)) in ...
AbstractWe present two new algorithms for the range-efficient F0 estimating problem and improve the ...
Estimating ranks, quantiles, and distributions over streaming data is a central task in data analysi...