A Dyadic Simulation Approach to Efficient Range-Summability
Abstract
Efficient range-summability (ERS) of a long list of random variables is a fundamental algorithmic problem that has applications to three important database applications, namely, data stream processing, space-efficient histogram maintenance (SEHM), and approximate nearest neighbor searches (ANNS). In this work, we propose a novel dyadic simulation framework and develop three novel ERS solutions, namely Gaussian-dyadic simulation tree (DST), Cauchy-DST and Random Walk-DST, using it. We also propose novel rejection sampling techniques to make these solutions computationally efficient. Furthermore, we develop a novel k-wise independence theory that allows our ERS solutions to have both high computational efficiencies and strong provable independence guarantees.
Keywords
Cite
@article{arxiv.2109.06366,
title = {A Dyadic Simulation Approach to Efficient Range-Summability},
author = {Jingfan Meng and Huayi Wang and Jun Xu and Mitsunori Ogihara},
journal= {arXiv preprint arXiv:2109.06366},
year = {2023}
}