BIROn - Birkbeck Institutional Research Online

    Towards a social graph approach for modeling risks in big data and Internet of Things (IoT)

    Johny, O. and Sotiriadis, Stelios and Asimakopoulou, E. and Bessis, N. (2014) Towards a social graph approach for modeling risks in big data and Internet of Things (IoT). In: UNSPECIFIED (ed.) 2014 International Conference on Intelligent Networking and Collaborative Systems. IEEE, pp. 439-444. ISBN 9781479963874.

    [img] Text
    07057129.pdf - Published Version of Record
    Restricted to Repository staff only

    Download (166kB)


    The discovery and integration of big data and Internet of Things (IoTs) highlight new challenges in the area of risks. This work focuses on the analysis of literature review approaches by presenting a study that includes works for resource discovery and data integration, social search engines, ranking techniques, and social graphs in order to provide a cross comparison and a preliminary evaluation study. The approaches are analyzed in order to define theoretical key requirements that could enable the utilization of social graphs towards the discovery and modeling of interconnected entities in big data and IoT scenarios.


    Item Type: Book Section
    Keyword(s) / Subject(s): Big Data, Internet of Things, data integration, graph theory, search engines, Big Data discovery, Big Data integration, Internet of Things, IoT scenarios, interconnected entities, ranking techniques, risk modeling, social graph approach, social search engines, Big data, Collaboration, Data models, Databases, Filtering, Search engines, Social network services, Big data, Data discovery, Data integration, IoT, Risk analysis, Social graphs
    School: Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences
    Research Centres and Institutes: Birkbeck Knowledge Lab
    Depositing User: Stelios Sotiriadis
    Date Deposited: 01 Oct 2018 10:10
    Last Modified: 09 Aug 2023 12:43


    Activity Overview
    6 month trend
    6 month trend

    Additional statistics are available via IRStats2.

    Archive Staff Only (login required)

    Edit/View Item Edit/View Item