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A Sketch Framework for Approximate Data Stream Processing in Sliding Windows

delete2022-01-01
delete4
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OA
AI
Y
Yinda Zhang
Z
Zhoujing Hu
L
Long He
K
Ke Wang
X
Xilai Liu
T
Tong Yang *
Y
Yi Wang
崔斌 封面图
崔斌 (Bin Cui)
DOI：10.1109/TKDE.2022.3151140delete
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摘要

摘要

En 中文
数据流处理已成为近年来的热点问题。有三个基本的流处理任务: 成员查询、频率查询和top-k查询。虽然大多数现有解决方案在固定窗口中解决这些查询，但本文侧重于更具挑战性的任务: 在滑动窗口中回答这些查询。虽然大多数现有解决方案通过使用不同的算法来解决不同类型的查询，但本文侧重于通用框架。在本文中，我们提出了一个通用框架，即滑动草图，该框架可以应用于上述三种查询的许多现有解决方案，并使它们能够支持滑动窗口中的查询。我们将我们的框架应用于上述三种查询的五个最先进的草图。理论分析和广泛的实验结果表明，使用我们的框架后，不支持滑动窗口的现有草图的准确性变得比相应的现有技术高得多。我们在Github上发布了所有源代码。
Keyword:
Probabilistic logic
Hash functions
Data models
Computer science
Big Data
Art
Approximation algorithms
Data stream
sliding window
sketch
approximate query

期刊

IEEE Transactions on Knowledge and Data Engineering 封面图
IEEE Transactions on Knowledge and Data Engineering
IF:
10.4
论文数:
6.8K
被引数:
3.2W

机构

P
Peng Cheng Laboratory
学者数:
1.7K
论文数: 1.8K
被引数: 2.0K
P
peking university
学者数:
11.9W
论文数: 8.7W
被引数: 146
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