Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/10371
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dc.contributor.authorTemel, S.-
dc.contributor.authorUmmak, E.-
dc.contributor.authorTokgöz, A.-
dc.contributor.authorIşık, F.-
dc.contributor.authorAlbayrak, O.-
dc.contributor.authorÜnal, P.-
dc.contributor.authorÖzbayoğlu, M.-
dc.date.accessioned2023-04-16T10:01:19Z-
dc.date.available2023-04-16T10:01:19Z-
dc.date.issued2022-
dc.identifier.isbn9781665480451-
dc.identifier.urihttps://doi.org/10.1109/BigData55660.2022.10020589-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/10371-
dc.descriptionAnkura;et al.;Hitachi;KPMG Consulting Co., Ltd.;NTT Data Intellilink Corporation;Think in Data Initiative, Association Incen_US
dc.description2022 IEEE International Conference on Big Data, Big Data 2022 -- 17 December 2022 through 20 December 2022 -- 186390en_US
dc.description.abstractIn this article, the decision mechanism of a control system has been created by using big data and by applying an ontology model. This type of control is important in order to minimize, or even eliminate human influence in systematic industrial processes. The development presented in the article in order to make the ontology model compatible with a relational database in the decision mechanism, is a step taken to eliminate the human effect. The method used in the article aims to carry out a decision mechanism under the guidance of big data by using relational databases integrated with the ontology model. In line with this goal, the ontology model associated with relational databases with high prevalence will be able to access the continuous data required for the decision process and enrich the decision mechanism. In this study, when the necessary parameters for the decision process are obtained, dynamic threshold determination is provided by a machine learning model with these parameters. This dynamic threshold varies over various time periods, with the combination of inputs provided to the machine learning model and differences in value. Our test results state that the Decision Tree model predictions' accuracy is 100%. © 2022 IEEE.en_US
dc.description.sponsorshipHorizon 2020 Framework Programme, H2020; Horizon 2020: 870130en_US
dc.description.sponsorshipACKNOWLEDGMENT This study has been partially supported by the COGNITWIN project. The COGNITWIN project has been funded by the European Union's Horizon 2020 research and innovation programme under GA No.870130.en_US
dc.description.sponsorshipThis study has been partially supported by the COGNITWIN project. The COGNITWIN project has been funded by the European Union’s Horizon 2020 research and innovation programme under GA No.870130.en_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relation.ispartofProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022en_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectbig dataen_US
dc.subjectcontrolen_US
dc.subjectdecision tree modelen_US
dc.subjectontologyen_US
dc.subjectBig dataen_US
dc.subjectControl systemsen_US
dc.subjectDecision treesen_US
dc.subjectDynamicsen_US
dc.subjectMachine learningen_US
dc.subjectControl system designsen_US
dc.subjectDecision mechanismen_US
dc.subjectDecision processen_US
dc.subjectDecision-tree modelen_US
dc.subjectDynamic thresholden_US
dc.subjectMachine learning modelsen_US
dc.subjectOntology modelen_US
dc.subjectOntology'sen_US
dc.subjectRelational Databaseen_US
dc.subjectSystems implementationen_US
dc.subjectOntologyen_US
dc.titleControl System Design and Implementation Based on Big Data and Ontologyen_US
dc.typeConference Objecten_US
dc.departmentTOBB ETÜen_US
dc.identifier.startpage6048en_US
dc.identifier.endpage6056en_US
dc.identifier.scopus2-s2.0-85147935011en_US
dc.institutionauthor-
dc.identifier.doi10.1109/BigData55660.2022.10020589-
dc.authorscopusid58101634200-
dc.authorscopusid57350611900-
dc.authorscopusid58101983700-
dc.authorscopusid58101100000-
dc.authorscopusid57226393431-
dc.authorscopusid56396952700-
dc.authorscopusid57947593100-
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.grantfulltextnone-
item.fulltextNo Fulltext-
item.openairetypeConference Object-
item.cerifentitytypePublications-
item.languageiso639-1en-
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
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