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]](https://eprints.bbk.ac.uk/style/images/fileicons/text.png) |
Text
07057129.pdf - Published Version of Record Restricted to Repository staff only Download (166kB) |
Abstract
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.
Metadata
| 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 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/21819 |
Statistics
Additional statistics are available via IRStats2.
Archive Staff Only (login required)
