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Integration of Knime ML and ThingWorx IoT platform to establish a standard for end-to-end data science projects

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dc.contributor.authorBirinci, Makbule Nalkiran
dc.contributor.authorAltuntas, Serkan
dc.date.accessioned2026-06-27T15:23:50Z
dc.date.issued2025
dc.description.abstractThe Internet ofThings (IoT) and Machine Learning (ML) are among the core technologies ofIndustry 4.0. They are widely used in data science projects. IoT collectssensor data, and ML creates value from this data, improving operationalefficiency. This study integrates Thingworx IoT and Knime ML software.The aim is toprovide a user-friendly environment with more methods, flexibility, and lowcoding. An architecture for end-to-end data science projects has been designedand implemented at a leading automotive company. The architecture has beentested in conveyor failure prediction and can also serve as a guide for otherindustries.en
dc.description.sponsorshipTUBITAK
dc.description.sponsorshipTOFAS Turkish Automotive Industry
dc.description.urihttps://doi.org/10.1080/17517575.2025.2579983
dc.identifier.doi10.1080/17517575.2025.2579983
dc.identifier.eissn1751-7583
dc.identifier.issn1751-7575
dc.identifier.issue12
dc.identifier.urihttps://hdl.handle.net/20.500.14981/70484
dc.identifier.volume19
dc.identifier.wos001606490300001
dc.language.isoeng
dc.publisherTAYLOR & FRANCIS LTD
dc.relation.ispartofENTERPRISE INFORMATION SYSTEMS
dc.subjectKnime
dc.subjectThingWorx
dc.subjectIoT
dc.subjectdata science
dc.subjectmachine learning
dc.subjectComputer Science
dc.titleIntegration of Knime ML and ThingWorx IoT platform to establish a standard for end-to-end data science projects
dc.typeArticle
dspace.entity.typePublication
local.import.sourceWOS

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