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dc.contributor.authorJasinski, Andzej
dc.contributor.authorQiao, Yuansong
dc.contributor.authorKeeney, John
dc.contributor.authorFallon, Enda
dc.contributor.authorFlynn, Ronan
dc.identifier.citationJasinski, A., Qiao, Y., Keeney, J., Fallon, E., Flynn, R. (2021). A workflow engine server (WES) for the design of adaptive and scalable workflows. Presented at AIT Poster Presentation Seminar 2019.en_US
dc.description.abstractThe generation of a workflow is an important management system that is widely applied in many different types of business. The traditional workflow approach, applied in specialised areas such as health or environmental control, is designed to manage one particular task flow; this means that it is not flexible or adaptive The traditional workflow approach does not lend itself to automatic updates or upgrades when required; this situation can lead to the replacement of a business’s management system. This research proposes a new architecture for the design of workflows that are both adaptive and scalable. It is cantered on a novel workflow engine server (WES) that can process received jobs or requests and, based on a method chosen from a number of options, generate dynamic workflows and actions to be taken. The architecture redefines traditional workflow behaviour. Where previously a workflow was created for a particular job or task, the workflow will now be created by the job or task. The proposed solution eliminates the problems of adaptability and scalability associated with traditional workflow management. The proposed architecture can be applied to the design of workflows in both large-scale (5G networks) and small-scale (home IoT) environments.en_US
dc.publisherAthlone Institute of Technologyen_US
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.subjectWorkflow engine server (WES)en_US
dc.subjectAdaptive workflowsen_US
dc.subjectScalable workflowsen_US
dc.titleA workflow engine server (WES) for the design of adaptive and scalable workflowsen_US
dc.contributor.affiliationAthlone Institute of Technologyen_US
dc.contributor.sponsorIrish Research Councilen_US
dc.identifier.orcid 0000-0002-6118-7361en_US
dc.identifier.orcid 0000-0002-1543-1589en_US
dc.identifier.orcid 0000-0002-8300-5813en_US
dc.identifier.orcid 0000-0002-6475-005Xen_US
dc.subject.departmentFaculty of Engineering & Informatics AITen_US
dc.relation.projectidEPSPG 2017/336en_US

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Attribution-NonCommercial-NoDerivatives 4.0 International
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 International