• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • 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 LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    Delve

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Type
    Article
    Authors
    Akujuobi, Uchenna Thankgod cc
    Zhang, Xiangliang cc
    KAUST Department
    Computer Science Program
    Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
    Machine Intelligence & kNowledge Engineering Lab
    Date
    2017-11-21
    Permanent link to this record
    http://hdl.handle.net/10754/668646
    
    Metadata
    Show full item record
    Abstract
    Research and experimentation in various scientific fields are based on the observation, analysis and benchmarking on datasets. The advancement of research and development has thus, strengthened the importance of dataset access. However, without enough knowledge of relevant datasets, researchers usually have to go through a process which we term \manual dataset retrieval". With the accelerated rate of scholarly publications, manually finding the relevant dataset for a given research area based on its usage or popularity is increasingly becoming more and more difficult and tedious. In this paper, we present Delve, a web-based dataset retrieval and document analysis system. Unlike traditional academic search engines and dataset repositories, Delve is dataset driven and provides a medium for dataset retrieval based on the suitability or usage in a given field. It also visualizes dataset and document citation relationship, and enables users to analyze a scientific document by uploading its full PDF. In this paper, we first discuss the reasons why the scientific community needs a system like Delve. We then proceed to introduce its internal design and explain how Delve works and how it is beneficial to researchers of all levels.
    Citation
    Akujuobi, U., & Zhang, X. (2017). Delve. ACM SIGKDD Explorations Newsletter, 19(2), 36–46. doi:10.1145/3166054.3166059
    Publisher
    Association for Computing Machinery (ACM)
    Journal
    ACM SIGKDD Explorations Newsletter
    DOI
    10.1145/3166054.3166059
    Additional Links
    https://dl.acm.org/doi/10.1145/3166054.3166059
    ae974a485f413a2113503eed53cd6c53
    10.1145/3166054.3166059
    Scopus Count
    Collections
    Articles; Computer Science Program; Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division

    entitlement

     
    DSpace software copyright © 2002-2023  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    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.