Business Intelligence Fog IoT node development model for Big Data processing of air quality in scientific partnerships

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dc.contributor.authorBehrens, Grit
dc.contributor.authorKaratzas, Kostas
dc.contributor.authorOrlowski, Cezary
dc.date.accessioned2023-12-26T18:36:01Z
dc.date.available2023-12-26T18:36:01Z
dc.date.issued2024-01-03
dc.identifier.doihttps://doi.org/10.24251/HICSS.2024.034
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other27ac18da-1bc4-41f9-9044-ed673a3383de
dc.identifier.urihttps://hdl.handle.net/10125/106409
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectBusiness Intelligence and Big Data for Innovative, Collaborative and Sustainable Development of Organizations in Digital Era
dc.subjectair quality data
dc.subjectassociation rules
dc.subjectbig data
dc.subjectbusiness intelligence
dc.subjectdevelopment of scientific partnerships
dc.subjectfog nodes
dc.subjectiot
dc.titleBusiness Intelligence Fog IoT node development model for Big Data processing of air quality in scientific partnerships
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractThe aim of the current paper is to present a dynamic model of managing nodes of the Fog layer in Edge-Fog-Cloud systems of the Internet of Things in scientific partnerships. The article is a response to the problem of managing Fog nodes in IoT systems, where classic management mechanisms, based on schedules, become insufficient. It also addresses the problem of delays in data processing in the Cloud layer, which is significant and affects decision-making processes. Therefore, the authors propose a Business Intelligence model based on association rules that predict the number of agents of Fog layer nodes. This prediction allows for the design of nodes in which the requirements of the scientific partner (number of processor cores, RAM size, classifier type, number of processed records, classifier working time) are the basis for selecting the appropriate number of Fog node agents. The model was validated by using association rules to select the appropriate number of Fog node agents for the domain of air quality data processed in three scientific partnerships using different types of data classifiers.
dcterms.extent10 pages
prism.startingpage288

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Business Intelligence and Big Data for Innovative, Collaborative and Sustainable Development of Organizations in Digital Era