Finding frequent items in sliding windows over data streams using EBF

被引：0
|
作者：
Wang, ShuYun [1 ]
Xu, HeXiang [1 ]
Hu, YunFa [1 ]
机构：
[1] Fudan Univ, Dept Comp & Informat Technol, Shanghai, Peoples R China
来源：
SNPD 2007: EIGHTH ACIS INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, ARTIFICIAL INTELLIGENCE, NETWORKING, AND PARALLEL/DISTRIBUTED COMPUTING, VOL 3, PROCEEDINGS | 2007年
关键词：
D O I：
10.1109/SNPD.2007.451
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper introduces the algorithm FIS-EBF for estimating the frequent items in sliding windows over data streams. FIS-EBF is Based the data structure named EBF(extensible Bloom Filter). Experiments show that FIS-EBF can work with high precision and recall, it is also showed that FIS-EBF is very efficient in terms of processing time.
引用
收藏
页码：682 / +
页数：2
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