• DocumentCode
    3230616
  • Title

    Finding Frequent Items in SlidingWindows over Data Streams Using EBF

  • Author

    Wang, Shuyun ; Xu, Hexiang ; Hu, Yunfa

  • Author_Institution
    Fudan Univ., Shanghai
  • Volume
    3
  • fYear
    2007
  • fDate
    July 30 2007-Aug. 1 2007
  • Firstpage
    682
  • Lastpage
    687
  • 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;
  • fLanguage
    English
  • Publisher
    ieee
  • 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
  • Type

    conf

  • DOI
    10.1109/SNPD.2007.451
  • Filename
    4287937
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3230616