Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.11851/1992
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dc.contributor.authorÖnal, Aras Can-
dc.contributor.authorSezer, Ömer Berat-
dc.contributor.authorÖzbayoğlu, Ahmet Murat-
dc.contributor.authorDoğdu, Erdoğan-
dc.date.accessioned2019-07-10T14:42:44Z
dc.date.available2019-07-10T14:42:44Z
dc.date.issued2017
dc.identifier.citationOnal, A. C., Sezer, O. B., Ozbayoglu, M., & Dogdu, E. (2017, December). Weather data analysis and sensor fault detection using an extended iot framework with semantics, big data, and machine learning. In 2017 IEEE International Conference on Big Data (Big Data) (pp. 2037-2046). IEEE.en_US
dc.identifier.isbn978-1-5386-2715-0
dc.identifier.issn2639-1589
dc.identifier.urihttps://ieeexplore.ieee.org/document/8258150-
dc.identifier.urihttps://hdl.handle.net/20.500.11851/1992-
dc.descriptionIEEE International Conference on Big Data (IEEE Big Data) (2017 : Boston, MA)
dc.description.abstractIn recent years, big data and Internet of Things (IoT) implementations started getting more attention. Researchers focused on developing big data analytics solutions using machine learning models. Machine learning is a rising trend in this field due to its ability to extract hidden features and patterns even in highly complex datasets. In this study, we used our Big Data IoT Framework in a weather data analysis use case. We implemented weather clustering and sensor anomaly detection using a publicly available dataset. We provided the implementation details of each framework layer (acquisition, ETL, data processing, learning and decision) for this particular use case. Our chosen learning model within the library is Scikit-Learn based k-means clustering. The data analysis results indicate that it is possible to extract meaningful information from a relatively complex dataset using our framework.en_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectInternet of thingsen_US
dc.subjectmachine learningen_US
dc.subjectframeworken_US
dc.subjectbig data analyticsen_US
dc.subjectweather data analysisen_US
dc.subjectanomaly detectionen_US
dc.subjectfault detectionen_US
dc.subjectclusteringen_US
dc.titleWeather Data Analysis and Sensor Fault Detection Using An Extended IoT Framework with Semantics, Big Data, and Machine Learningen_US
dc.typeConference Objecten_US
dc.departmentFaculties, Faculty of Engineering, Department of Computer Engineeringen_US
dc.departmentFakülteler, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümütr_TR
dc.identifier.startpage2037
dc.identifier.endpage2046
dc.authorid0000-0001-7998-5735-
dc.identifier.wosWOS:000428073702004en_US
dc.identifier.scopus2-s2.0-85047833066en_US
dc.institutionauthorÖzbayoğlu, Ahmet Murat-
dc.identifier.doi10.1109/BigData.2017.8258150-
dc.authorwosidH-2328-2011-
dc.authorscopusid6505999525-
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-
crisitem.author.dept02.1. Department of Artificial Intelligence Engineering-
Appears in Collections:Bilgisayar Mühendisliği Bölümü / Department of Computer Engineering
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection
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