Universal Perfect Samplers for Incremental Streams
Seth Pettie, Dingyu Wang

TL;DR
This paper introduces universal and simple exact sampling algorithms for a broad class of functions called Laplace exponents, enabling efficient incremental sampling in streaming data scenarios.
Contribution
It develops the first universal $G$-sampler for Laplace exponents that operates with logarithmic memory and can produce exact samples on demand.
Findings
A $G$-sampler with 2-word memory for any $G$ in the class
A universal $ ext{G}$-sampler with $O( ext{log} n)$ memory
Efficient sampling of multiple indices with controlled overhead
Abstract
If , the -moment of a vector is and the -sampling problem is to select an index according to its contribution to the -moment, i.e., such that . Approximate -samplers may introduce multiplicative and/or additive errors to this probability, and some have a non-trivial probability of failure. In this paper we focus on the exact -sampling problem, where is selected from the class of Laplace exponents of non-negative, one-dimensional L\'evy processes, which includes several well studied classes such as th moments , , logarithms , Cohen and Geri's soft concave sublinear functions, which are used to approximate concave sublinear functions, including…
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Taxonomy
TopicsAdvanced Database Systems and Queries · Data Management and Algorithms · Data Stream Mining Techniques
