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  • 1.
    Chrysanthis, P.K.
    et al.
    University of Pittsburgh.
    Sloman, M.
    Imperial College London.
    Zaslavsky, Arkady
    Report on MDM 2003: The 4th international conference on mobile data management2003In: SIGMOD record, ISSN 0163-5808, E-ISSN 1943-5835, Vol. 32, no 3, p. 103-106Article in journal (Other academic)
  • 2.
    Gaber, Mohamed Medhat
    et al.
    Centre for Distributed Systems and Software Engineering, Monash University.
    Zaslavsky, Arkady
    Krishnaswamy, Shonali
    Centre for Distributed Systems and Software Engineering, Monash University.
    Mining data streams: a review2005In: SIGMOD record, ISSN 0163-5808, E-ISSN 1943-5835, Vol. 34, no 2, p. 18-26Article in journal (Refereed)
    Abstract [en]

    The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously and in a very high fluctuating data rates. Examples include sensor networks, web logs, and computer network traffic. The storage, querying and mining of such data sets are highly computationally challenging tasks. Mining data streams is concerned with extracting knowledge structures represented in models and patterns in non stopping streams of information. The research in data stream mining has gained a high attraction due to the importance of its applications and the increasing generation of streaming information. Applications of data stream analysis can vary from critical scientific and astronomical applications to important business and financial ones. Algorithms, systems and frameworks that address streaming challenges have been developed over the past three years. In this review paper, we present the state-of-the-art in this growing vital field.

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