• Login
    View Item 
    •   Home
    • Research Publications
    • Engineering & Technology
    • Department of Electronics, Computing & Maths
    • View Item
    •   Home
    • Research Publications
    • Engineering & Technology
    • Department of Electronics, Computing & Maths
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UDORACommunitiesTitleAuthorsIssue DateSubmit DateSubjectsThis CollectionTitleAuthorsIssue DateSubmit DateSubjects

    My Account

    LoginRegister

    About and further information

    AboutOpen Access WebpagesOpen Access PolicyTake Down Policy University Privacy NoticeUniversity NewsTools for ResearchersLibraryUDo

    Statistics

    Display statistics

    Big data analytics in healthcare: A cloud based framework for generating insights

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Authors
    Anjum, Ashiq cc
    Aizad, Sanna
    Arshad, Bilal
    Subhani, Moeez cc
    Davies-Tagg, Dominic
    Abdullah, Tariq
    Antonopoulos, Nikolaos
    Affiliation
    University of Derby
    Issue Date
    2017
    
    Metadata
    Show full item record
    Abstract
    With exabytes of data being generated from genome sequencing, a whole new science behind genomic big data has emerged. As technology improves, the cost of sequencing a human genome has gone down considerably increasing the number of genomes being sequenced. Huge amounts of genomic data along with a vast variety of clinical data cannot be handled using existing frameworks and techniques. It is to be efficiently stored in a warehouse where a number of things have to be taken into account. Firstly, the genome data is to be integrated effectively and correctly with clinical data. The other data sources along with their formats have to be identified. Required data is then extracted from these other sources (such as clinical datasets) and integrated with the genome. The main challenge here is to be able to handle the integration complexity as a large number of datasets are being integrated with huge amounts of genome. Secondly, since the data is captured at disparate locations individually by clinicians and scientists, it brings the challenge of data consistency. It has to be made sure that the data consistency is not compromised as it is passed along the warehouse. Checks have to be put in place to make sure the data remains consistent from start to finish. Thirdly, to carry this out effectively, the data infrastructure has to be in the correct order. How frequently the data is accessed plays a crucial role here. Data in frequent use will be handled differently than data which is not in frequent use. Lastly, efficient browsing mechanisms have to put in place to allow the data to be quickly retrieved. The data is then iteratively analysed to get meaningful insights. The challenge here is to perform analysis very quickly. Cloud Computing plays an important role as it is used to provide scalability.
    Citation
    Anjum, A. et al (2017) 'Big data analytics in healthcare: A cloud based framework for generating insights', in Antonopoulos, N and Gillam, L. (eds.) '"Cloud Computing: Principles, Systems and Applications' 2nd ed., London : Springer, pp.
    Publisher
    Springer
    URI
    http://hdl.handle.net/10545/621422
    DOI
    10.1007/978-3-319-54645-2
    Additional Links
    http://www.springer.com/gp/book/9783319546445
    Type
    Book chapter
    Language
    en
    Series/Report no.
    1617-7975
    ISBN
    9783319546445
    ae974a485f413a2113503eed53cd6c53
    10.1007/978-3-319-54645-2
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

    entitlement

     
    DSpace software (copyright © 2002 - 2021)  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.