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    KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract)

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    TKDE_ICDE-kde-track.pdf
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    Description:
    Accepted Manuscript
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    Type
    Conference Paper
    Authors
    Qahtan, Abdulhakim
    Wang, Suojin
    Zhang, Xiangliang cc
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Computer Science Program
    Date
    2018-10-25
    Online Publication Date
    2018-10-25
    Print Publication Date
    2018-04
    Permanent link to this record
    http://hdl.handle.net/10754/630308
    
    Metadata
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    Abstract
    Recent developments in sensors, global positioning system devices and smart phones have increased the availability of spatiotemporal data streams. Developing models for mining such streams is challenged by the huge amount of data that cannot be stored in the memory, the high arrival speed and the dynamic changes in the data distribution. Density estimation is an important technique in stream mining for a wide variety of applications. In this paper, we present a method called KDE-Track to estimate the density of spatiotemporal data streams. KDE-Track can efficiently estimate the density function with linear time complexity using interpolation on a kernel model, which is incrementally updated upon the arrival of new samples from the stream.
    Citation
    Qahtan A, Wang S, Zhang X (2018) KDE-Track: An Efficient Dynamic Density Estimator for Data Streams (Extended Abstract). 2018 IEEE 34th International Conference on Data Engineering (ICDE). Available: http://dx.doi.org/10.1109/ICDE.2018.00237.
    Publisher
    Institute of Electrical and Electronics Engineers (IEEE)
    Journal
    2018 IEEE 34th International Conference on Data Engineering (ICDE)
    Conference/Event name
    34th IEEE International Conference on Data Engineering, ICDE 2018
    DOI
    10.1109/ICDE.2018.00237
    Additional Links
    https://ieeexplore.ieee.org/document/8509458
    ae974a485f413a2113503eed53cd6c53
    10.1109/ICDE.2018.00237
    Scopus Count
    Collections
    Conference Papers; Computer Science Program; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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