License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.ICDT.2022.17
URN: urn:nbn:de:0030-drops-158915
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2022/15891/
Meng, Jingfan ;
Wang, Huayi ;
Xu, Jun ;
Ogihara, Mitsunori
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.
BibTeX - Entry
@InProceedings{meng_et_al:LIPIcs.ICDT.2022.17,
author = {Meng, Jingfan and Wang, Huayi and Xu, Jun and Ogihara, Mitsunori},
title = {{A Dyadic Simulation Approach to Efficient Range-Summability}},
booktitle = {25th International Conference on Database Theory (ICDT 2022)},
pages = {17:1--17:18},
series = {Leibniz International Proceedings in Informatics (LIPIcs)},
ISBN = {978-3-95977-223-5},
ISSN = {1868-8969},
year = {2022},
volume = {220},
editor = {Olteanu, Dan and Vortmeier, Nils},
publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
address = {Dagstuhl, Germany},
URL = {https://drops.dagstuhl.de/opus/volltexte/2022/15891},
URN = {urn:nbn:de:0030-drops-158915},
doi = {10.4230/LIPIcs.ICDT.2022.17},
annote = {Keywords: fast range-summation, locality-sensitive hashing, rejection sampling}
}
Keywords: |
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fast range-summation, locality-sensitive hashing, rejection sampling |
Collection: |
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25th International Conference on Database Theory (ICDT 2022) |
Issue Date: |
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2022 |
Date of publication: |
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19.03.2022 |
Supplementary Material: |
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Audiovisual (Video of the Presentation): https://doi.org/10.5446/57488 |