BRADFORD SCHOLARS

    • Sign in
    View Item 
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    •   Bradford Scholars
    • Engineering and Informatics
    • Engineering and Informatics Publications
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of Bradford ScholarsCommunitiesAuthorsTitlesSubjectsPublication DateThis CollectionAuthorsTitlesSubjectsPublication Date

    My Account

    Sign in

    HELP

    Bradford Scholars FAQsCopyright Fact SheetPolicies Fact SheetDeposit Terms and ConditionsDigital Preservation Policy

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Increasing big data front end processing efficiency via locally sensitive Bloom filter for elderly healthcare

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Publication date
    2015
    Author
    Cheng, Yongqiang
    Jiang, Ping
    Peng, Yonghong
    Keyword
    Data classification; Locality sensitive bloom filter; Big data; Instance-based learning
    Peer-Reviewed
    Yes
    
    Metadata
    Show full item record
    Abstract
    In support of the increasing number of elderly population, wearable sensors and portable mobile devices capable of monitoring, recording, reporting and alerting are envisaged to enable them an independent lifestyle without relying on intrusive care programmes. However, the big data readings generated from the sensors are characterized as multidimensional, dynamic and non-linear with weak correlation with observable human behaviors and health conditions which challenges the information transmission, storing and processing. This paper proposes to use Locality Sensitive Bloom Filter to increase the Instance Based Learning efficiency for the front end sensor data pre-processing so that only relevant and meaningful information will be sent out for further processing aiming to relieve the burden of the above big data challenges. The approach is proven to optimize and enhance a popular instance-based learning method benefits from its faster speed, less space requirements and is adequate for the application.
    URI
    http://hdl.handle.net/10454/9133
    Version
    No full-text available in the repository
    Citation
    Cheng Y, Jiang P and Peng Y (2015) Increasing big data front end processing efficiency via locally sensitive Bloom filter for elderly healthcare. In: IEEE Symposium Series on Computational Intelligence in Big Data 2014, CIBD 2014. 9-12 Dec 2014, Orlando, FL, USA: 7011524.
    Link to publisher’s version
    http://dx.doi.org/10.1109/CIBD.2014.7011524
    Type
    Conference Paper
    Collections
    Engineering and Informatics Publications

    entitlement

     
    DSpace software (copyright © 2002 - 2023)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated 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.

    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.