• Login
    View Item 
    •   Home
    • Research
    • Book Chapters
    • View Item
    •   Home
    • Research
    • Book Chapters
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguidePlumX LibguideSubmit an Item

    Statistics

    Display statistics

    Efficient Estimation of Dynamic Density Functions with Applications in Data Streams

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    DensityEstimation-final.pdf
    Size:
    2.699Mb
    Format:
    PDF
    Description:
    Accepted Manuscript
    Download
    Type
    Book Chapter
    Authors
    Qahtan, Abdulhakim
    Wang, Suojin
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Date
    2018-07-29
    Online Publication Date
    2018-07-29
    Print Publication Date
    2019
    Permanent link to this record
    http://hdl.handle.net/10754/628248
    
    Metadata
    Show full item record
    Abstract
    Recently, many applications such as network monitoring, traffic management and environmental studies generate huge amount of data that cannot fit in the computer memory. Data of such applications arrive continuously in the form of streams. The main challenges for mining data streams are the high speed and the large volume of the arriving data. A typical solution to tackle the problems of mining data streams is to learn a model that fits in the computer memory. However, the underlying distributions of the streaming data change over time in unpredicted scenarios. In this sense, the learned models should be updated continuously and rely more on the most recent data in the streams. \n \nIn this chapter, we present an online density estimator that builds a model called KDE-Track for characterizing the dynamic density of the data streams. KDE-Track summarizes the distribution of a data stream by estimating the Probability Density Function (PDF) of the stream at a set of resampling points. KDE-Track is shown to be more accurate (as reflected by smaller error values) and more computationally efficient (as reflected by shorter running time) when compared with existing density estimation techniques. We demonstrate the usefulness of KDE-Track in visualizing the dynamic density of data streams and change detection.
    Citation
    Qahtan A, Wang S, Zhang X (2018) Efficient Estimation of Dynamic Density Functions with Applications in Data Streams. Learning from Data Streams in Evolving Environments: 247–278. Available: http://dx.doi.org/10.1007/978-3-319-89803-2_11.
    Publisher
    Springer Nature
    Journal
    Learning from Data Streams in Evolving Environments
    DOI
    10.1007/978-3-319-89803-2_11
    Additional Links
    https://link.springer.com/chapter/10.1007%2F978-3-319-89803-2_11
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-89803-2_11
    Scopus Count
    Collections
    Computer Science Program; Book Chapters; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2021  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.