Title :
Finding Frequent Items in SlidingWindows over Data Streams Using EBF
Author :
Wang, Shuyun ; Xu, Hexiang ; Hu, Yunfa
Author_Institution :
Fudan Univ., Shanghai
fDate :
July 30 2007-Aug. 1 2007
Abstract :
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.
Keywords :
data handling; filtering theory; data streams; extensible bloom filter; sliding windows; Aggregates; Artificial intelligence; Counting circuits; Data structures; Distributed computing; Filters; Frequency estimation; Monitoring; Sampling methods; Software engineering;
Conference_Titel :
Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, 2007. SNPD 2007. Eighth ACIS International Conference on
Conference_Location :
Qingdao
Print_ISBN :
978-0-7695-2909-7
DOI :
10.1109/SNPD.2007.451
